Flow tracing operation in container cluster

Description

BACKGROUND

The use of containers has changed the way applications are packaged and deployed, with monolithic applications being replaced by microservice-based applications. Here, the application is broken down into multiple, loosely coupled services running in containers, with each service implementing a specific, well-defined part of the application. However, the use of containers also introduces new challenges, in that the fleet of containers need to be managed and all these services and containers need to communicate with each other.

Management of the containers is addressed by container orchestration systems, such as Docker Swarm, Apache Mesos, or Kubernetes, the latter of which has become a de-facto choice for container orchestration. Kubernetes clusters can be run in an on-premises datacenter or in any public cloud (e.g., as a managed service or by bringing-up your own cluster on compute instances). These Kubernetes clusters may include both Linux and Windows hosts, and thus to enable networking between the entities of a cluster it is important to have a single network plugin that can be easily run on any platform and any cloud. It is also highly desirable to be able to configure global security policies across these clusters and provide centralized visibility (e.g., with simple debugging and troubleshooting features).

BRIEF SUMMARY

Some embodiments provide debugging and troubleshooting techniques for a container network interface plugin (e.g., for a Kubernetes cluster). Specifically, some embodiments map networking and/or network policy data to cluster abstractions in order to present this networking and/or network policy data to users (e.g., network administrators, application developers, etc.). This networking and/or network policy data may include flow table entries, ongoing network connections, flow tracing information, etc.

Some embodiments provide this mapping data in the context of a container network interface plugin. In some embodiments, a container cluster (e.g., a Kubernetes cluster) includes numerous nodes (e.g., virtual machines (VMs), physical host servers), each of which executes one or more pods (e.g., lightweight VMs on which parts of an application execute). The container network interface (CNI) plugin of some embodiments includes a set of modules that execute on each node to provide networking and security functionality for the pods. Specifically, some embodiments operate a flow-based managed forwarding element (e.g., Open vSwitch) on each node, to which each of the pods connect. In addition, a CNI agent executes on each node (i) to configure the forwarding element and (ii) to handle troubleshooting requests. In addition, a CNI controller separate from the nodes provides configuration data (e.g., forwarding information, network policy to be enforced) to the agents, which use this configuration data to configure the forwarding elements on their respective nodes. Distributed services (e.g., for aggregating troubleshooting information from multiple nodes) may also execute within the cluster.

In this context, the agent of some embodiments on a particular container host (e.g., a Kubernetes node) is capable of identifying flow entries used by the managed forwarding element of the particular container host related to a particular container cluster concept (e.g., a Kubernetes abstraction, such as a pod or network policy) and mapping elements of these flow entries to different Kubernetes concepts (including those in the request). Flow entries in flow-based forwarding elements include, in some embodiments, (i) a set of match conditions against which data messages are compared and (ii) a set of actions for the forwarding element to perform on data messages that match the match conditions.

When an agent receives a request for flow entries that relate to a particular Kubernetes concept (e.g., to a specific network policy), the agent identifies flow entries realized by the forwarding element executing on its container host that match the request. For example, for specific network policies or network policy rules, flow entries include a specific identifier in one of the match or action fields (e.g., a conjunction identifier, for conjunctive flow entries). Specific pods can be identified by network addresses (or data link addresses) used in flow entries (e.g., as match conditions). For each identified flow entry that matches the request, the agent generates mapping data that maps elements of the flow entry to specific Kubernetes concepts (e.g., pods, network policies, rules, etc.). For instance, matches over table identifiers, network addresses, and other conditions may be indicative of specific network policies and/or network policy rules, pods, nodes, etc. Raw flow entry data may be difficult for a network administrator or application developer to understand, so the generated mapping data is provided along with each flow entry for presentation to the requesting user. In different embodiments, this data is provided to the controller or directly to a user interface (e.g., a command line interface) from which the request was received.

In some embodiments, either the agent or another module operating on each of the container hosts along with the agent (e.g., a connection exporter module), also monitors ongoing connections being processed by the forwarding element. This module retrieves data about these ongoing connections, maps the data to Kubernetes concepts implemented in the cluster, and exports the ongoing connection information along with the mapped Kubernetes concepts. The connection data typically includes at least source and destination network addresses and transport layer ports as well as transport layer protocol, and may also include information about the status of the ongoing connection (e.g., the number of packets sent in either direction, the amount of data sent, any related connections, etc.). In some embodiments, the module maps the source and destination addresses to specific Kubernetes concepts (e.g., pods executing on that container host or other container hosts, distributed services, etc.). In addition, in some embodiments, the module identifies network policies that are relevant to each ongoing connection and exports this information along with the other connection and mapping data.

In some embodiments, the forwarding element uses a separate connection tracker module to store state regarding ongoing connections (because the flow entries used by the forwarding element are otherwise stateless). This connection tracker module stores the connection information (e.g., source and destination network addresses, source and destination transport layer ports, protocol, amounts of packets and data transferred, connection state). In some embodiments, the connection tracker stores this information for both incoming and outgoing directions of a connection. For flow entries (e.g., network policy flow entries) that require connection state information, the forwarding element retrieves this connection state information from the connection tracker module.

The connection exporter module on a host container, in some embodiments, retrieves the ongoing connections from the connection tracker module (e.g., at regular intervals) and maps this data to the Kubernetes concepts as described above. In some embodiments, the connection exporter module exports the connection data to a connection aggregator executing in the Kubernetes cluster (e.g., as a distributed service). This connection data may be exported as, e.g., IPFIX data, with the mapping information stored as metadata (e.g., as IPFIX information elements). In addition, in some embodiments, the connection exporter module also maintains metrics tied to the Kubernetes concepts, such as the number of connections per pod, per pod namespace, or per node, the number of connections to which each different network policy is applied, or the number of connections that meet a specific TCP connection state (e.g., only having the TCP_SYN flag).

The connection aggregator receives connection data (with mapping data for the Kubernetes concepts) from the flow exporter modules on multiple nodes of the cluster. In some embodiments, the connection aggregator also adds additional mapping data for the Kubernetes cluster concepts (e.g., information that is not efficient for the connection exporter module to add). This connection aggregator may also compare and combine data for the same ongoing connections from source and destination nodes. In addition, either the connection aggregator or a separate network visualizer analyzes the aggregated connection data and uses this data to generate a cluster visualization. This cluster visualization may show pods, nodes, services, etc. of the cluster, as well as the connections between these entities.

In some embodiments, the agents on the nodes in a cluster also enable flow tracing and relate flow entries matched in a flow tracing operation to Kubernetes concepts. Flow tracing operations allow one or more forwarding elements in the cluster to simulate the processing of a data message with pre-specified characteristics in order for an administrator or application developer to determine which flow entries act upon the data message (either using a simulation mechanism provided by the forwarding element or by injecting a flow tracing data message having the specified characteristics).

The CNI controller may receive a flow tracing request (e.g., via a user interface) that specifies parameters for the flow tracing operation (e.g., the source and destination addresses and/or pods for the trace operation). In response, the controller notifies one or more agents in the cluster of the request, indicating the specified parameters. In some embodiments, the controller only notifies the agent located at the source node for the flow tracing operation (i.e., the node on which the specified source pod executes) or the agents at the source and destination nodes for the flow tracing operation. In other embodiments, the controller notifies all of the agents in the cluster in case the flow tracing data message is sent by the source node to any of the other nodes in the cluster.

Each agent that receives flow tracing instructions from the controller installs a set of new flow entries on the respective forwarding element in some embodiments. These new flow entries, in some embodiments, are copies of the existing flow entries installed in the forwarding element (e.g., for forwarding and tunneling, implementing network policies, performing network address translation, etc.), with additional match fields and/or actions. In some embodiments, the flow tracing data message includes a flow trace marker over which the new flow entries match. That is, each of these new flow entries matches over the same fields as its corresponding existing flow entry as well as this additional match field (and with a slightly higher priority than its corresponding existing flow entry). Furthermore, in addition to the actions specified by their corresponding existing flow entries, the new flow entries specify to write information to a temporary memory about the flow entry being matched, so that this information can be reported back to the agent after the forwarding element has completed its processing of the flow tracing data message. The new flow entries also include one or more additional flow entries that specify to send this information back to the agent after completion of the processing, even if the flow trace data message is dropped or tunneled to another node.

After installing the flow entries, the agent on the source node injects a flow trace data message into the forwarding element on that node. This data message is injected at the forwarding element with all of the properties that a data message would have if received from the pod specified as the packet source (e.g., a particular source port, source and destination addresses, etc.). The forwarding element then processes the data message through its flow tables, during the course of which the data message matches various flow entries. After the processing is completed, as mentioned, the forwarding element sends a message back to the agent to indicate which flow entries were matched by the data message. In addition, if the flow entries specify to send the data message to another node, the forwarding element performs this action.

Upon receiving the message from the forwarding element, the agent generates mapping data for at least a subset of the flow entries matched by the flow trace data message. This mapping data, similar to that described above in other contexts, maps elements of the flow entries to specific Kubernetes concepts (e.g., pods, network policies, rules, etc.). For instance, matches over table identifiers, network addresses, and other conditions may be indicative of specific network policies and/or network policy rules, pods, nodes, etc.

The agent at the source node, in some embodiments, reports this information (the set of flow entries matched as well as the mapping data) to the controller that sent the initial flow tracing request. In addition, if the flow trace data message is sent to another node (e.g., if the destination address corresponds to a pod or service on another node), then the forwarding element on the other node will also send a message to the agent on that node, which will in turn generate mapping data and report the information to the controller. The controller is responsible for aggregating the flow entry and mapping data from each of the relevant agents and providing a report back via the interface through which the initial request was received. In different embodiments, this report may include all of the flow entries along with mapping data or a condensed set of information based on the flow entries (e.g., source and destination pods, network policies applied to the data message, etc.).

The preceding Summary is intended to serve as a brief introduction to some embodiments of the invention. It is not meant to be an introduction or overview of all inventive subject matter disclosed in this document. The Detailed Description that follows and the Drawings that are referred to in the Detailed Description will further describe the embodiments described in the Summary as well as other embodiments. Accordingly, to understand all the embodiments described by this document, a full review of the Summary, Detailed Description and the Drawings is needed. Moreover, the claimed subject matters are not to be limited by the illustrative details in the Summary, Detailed Description and the Drawing, but rather are to be defined by the appended claims, because the claimed subject matters can be embodied in other specific forms without departing from the spirit of the subject matters.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth in the appended claims. However, for purpose of explanation, several embodiments of the invention are set forth in the following figures.

FIG. 1 conceptually illustrates a Kubernetes cluster with forwarding and networking managed by a container network interface (CNI) plugin of some embodiments.

FIG. 2 conceptually illustrates a process of some embodiments for responding to a request for information about flow entries realized on a node that relate to a particular Kubernetes concept.

FIG. 3 conceptually illustrates an example in which a CNI agent receives and processes a request directly from a command line interface (CLI) tool of the CNI.

FIG. 4 conceptually illustrates an example in which a controller sends a request to CNI agents on multiple nodes in a cluster and aggregates the responses from these nodes.

FIG. 5 illustrates an example network policy of some embodiments for web servers in a cluster.

FIG. 6 illustrates an example of network policy state report of some embodiments retrieved from the CNI controller for the network policy of FIG. 5.

FIG. 7 illustrates a report of some embodiments showing flow entries annotated with Kubernetes cluster mapping data.

FIG. 8 conceptually illustrates a Kubernetes cluster with forwarding and networking managed by a CNI plugin of some embodiments, which has capability to monitor ongoing connections and provide information about these connections to a user.

FIG. 9 illustrates an example of a visualization tool graphical user interface (GUI) showing connections between pods in a cluster.

FIG. 10 conceptually illustrates a process of some embodiments for reporting connection data from a node in a cluster such as that shown in FIG. 8.

FIG. 11 conceptually illustrates an example of the data transfer involved in the connection export process.

FIG. 12 conceptually illustrates an example of raw connection data provided by a connection tracker module to a connection exporter of some embodiments.

FIG. 13 conceptually illustrates a connection and mapping data export message of some embodiments from a connection exporter on a node to a connection aggregator.

FIG. 14 conceptually illustrates a process of some embodiments for performing a flow tracing operation on a Kubernetes node.

FIGS. 15A-15B conceptually illustrate an example of a CNI controller in a cluster of some embodiments receiving a flow tracing operation request, CNI agents and forwarding elements carrying out the flow tracing operation and reporting back results to the controller, and the controller providing a report regarding the flow tracing operation.

FIG. 16 conceptually illustrates a process of some embodiments for performing a flow tracing operation on a Kubernetes node by having the forwarding element simulate the processing of a packet.

FIG. 17 conceptually illustrates a process of some embodiments for generating a report based on flow trace information from one or more agents.

FIG. 18 illustrates an example of a summary report output by a CLI tool of some embodiments.

FIGS. 19A-B illustrate an example of a report aggregating a full set of raw flow entries and mapping data from source and destination nodes for a flow tracing operation.

FIG. 20 illustrates an example of a graphical report that shows the results for a flow tracing packet injected at a forwarding element at a source node and indicates the operations performed by the forwarding elements at both the source node and the destination node.

FIG. 21 conceptually illustrates an electronic system with which some embodiments of the invention are implemented.

DETAILED DESCRIPTION

In the following detailed description of the invention, numerous details, examples, and embodiments of the invention are set forth and described. However, it will be clear and apparent to one skilled in the art that the invention is not limited to the embodiments set forth and that the invention may be practiced without some of the specific details and examples discussed.

Some embodiments provide this mapping data in the context of a container network interface plugin for a container cluster (e.g., a Kubernetes cluster). FIG. 1 conceptually illustrates a Kubernetes cluster 100 with forwarding and networking managed by a container network interface (CNI) plugin of some embodiments. As shown, the cluster 100 includes numerous nodes 105, as well as a deployment 110 on which a CNI controller 115 runs, a Kubernetes API 120, an Octant UI 125, and a CNI command line interface 130 (which may or may not actually be part of the cluster 100, in different embodiments).

In some embodiments, the nodes 105 are virtual machines or physical host servers that host pods 135, as well as various entities that enable the pods 135 to run on the node 105. As shown, these various entities include a kubelet 140, a CNI plugin 145, a kube-proxy 150, iptables 155, a daemonset 160 which includes a CNI agent 165 and one or more Open vSwitch (OVS) daemons 170, and an OVS bridge 175. The pods, in some embodiments, are lightweight virtual machines (VMs) or other data compute nodes (DCNs) that encapsulate one or more containers. Pods may wrap a single container or a number of related containers (e.g., containers for the same application) that share resources. In some embodiments, each pod 135 includes storage resources for the containers as well as a network address (e.g., an IP address) at which the pod can be reached.

The kubelet 140 is a standard Kubernetes agent that runs on each node in a cluster to manage containers running in the pods 135. Similarly, the kube-proxy 150 is a network proxy that runs on each node in a Kubernetes cluster to maintain network rules and manage the iptables 155 or another datapath implementation. In some embodiments, the iptables 155 are configured by the kube-proxy 150, but not actually used for networking as the OVS daemons 170 and OVS bridge 175 are instead used for handling networking between pods 135.

The kubelet 140 on each node 105 executes the CNI plugin 145 on that node, in some embodiments. In some embodiments, the CNI plugin 145 is a gRPC client that issues remote procedure calls to the CNI agent 165 on the node for each container network interface command, which returns the result of those commands (or an error) to CNI plugin 145.

The daemonset 160 is a type of pod that is instantiated on all of the nodes 105 of the cluster, in this case to execute the CNI agent 165 and the OVS daemons 170. The CNI agent 165 on a particular node is responsible for managing the OVS instantiation on the node (i.e., the OVS daemons 170 and the OVS bridge 175), the operation of which is described below. In some embodiments, the CNI agent 165 executes in a separate container of the daemonset 160 than the OVS daemons 170.

Whenever a new pod 135 is to be created on a node 105, the CNI agent 165 receives instructions from the CNI plugin 145 about the creation of the new pod. The CNI agent 165 creates the pod's network interface (e.g., a veth interface or other virtual ethernet device/adapter), allocates a network address (e.g., IP address) for the interface, and connects the interface to the OVS bridge 175 (e.g., via a corresponding network interface on the OVS bridge 175). The CNI agent 165 also generates and installs the necessary networking flow entries for this network address in one of the OVS daemons 170 (i.e., in ovs-vswitchd, the daemon responsible for processing data messages for which flow entries are not cached in the OVS bridge 175). Flow entries in flow-based forwarding elements such as OVS include, in some embodiments, (i) a set of match conditions against which data packets are compared and (ii) a set of actions for the forwarding element to perform on data packets that match the match conditions.

In some embodiments, the agent 165 also receives updates from the Kubernetes API server 120 regarding the creation of new nodes 105 (either by monitoring for updates at the Kubernetes API server 120, or by the server 120 pushing such updates to all of the agents 165), so that it can create a new tunnel to each remote node in the cluster in OVS (e.g., by creating new flow entries for tunneling to the remote node). In addition, the agent 165 receives updates from the CNI controller 115 when network policies are created, removed, or modified (again, either by monitoring for updates at the CNI controller 115, or by the controller 115 pushing such updates to all of the agents 165 that require the updates). The agent 165 generates and installs new flow entries in OVS to implement these network policies for the local pods 135. Furthermore, as described in greater detail below, the CNI agent 165 handles certain types of troubleshooting and/or monitoring requests (e.g., flow entry realization requests, flow tracing requests, etc.).

OVS, as mentioned, includes the OVS bridge 175 as well as OVS daemons 170. These daemons 170, in some embodiments, include ovsdb-server (for handling configuration of the OVS bridge via database tables) and ovs-vswitchd. The latter daemon, as mentioned, processes data messages for which the OVS bridge 175 does not have flow entries. The OVS bridge 175 on a particular node 105 includes a classifier that processes data messages (i) between two pods 135 on its node 105, (ii) between a pod 135 on its node 105 and another pod or service on a different node in the cluster, and (iii) between a pod 135 on its node 105 and an external address. The OVS bridge 175 stores cached flow entries for faster processing of these data messages without involving the OVS daemon ovs-vswitchd when possible.

However, when the OVS bridge 175 receives a data message for which it does not have a cached flow entry (e.g., the first data message of a connection), the bridge 175 passes the data message to the ovs-vswitchd daemon 170. This user space classifier daemon processes the data message through a set of staged flow tables (or subtables of a single flow table) that store flow entries, typically requiring multiple lookups over the course of several stages. The daemon uses these results to generate a cache flow entry for similar data messages (e.g., for the same connection and/or for similar connections) and provides this cache flow entry to the OVS bridge 175 so that the bridge can more efficiently process subsequent data messages for the same or similar connections.

The CNI agent 165 creates the OVS bridge 175 on its node, as well as the virtual ethernet device/adapter (e.g., veth pairs) for each pod 135. In addition, the CNI agent 165 creates an internal port on the OVS bridge 175 as the gateway for the node's pod subnet, as well as a tunnel port for creating overlay tunnels to other nodes (e.g., using VXLAN, Geneve, STT, etc.). In some embodiments, each node 105 in the cluster 100 is assigned a subnet for its pods, and all of the pods 135 are assigned a network address (i.e., by the CNI agent 165) in the subnet of their node 105 (referred to herein as a “pod subnet” or “node pod subnet”). The agent 165 also assigns the gateway port a network address in the node's pod subnet (e.g., the first IP address in a classless inter-domain routing (CIDR) block allocated for the node).

When one pod 135 sends a data message to a pod or service on another node, the OVS bridge 175 on the source node applies any network policies, performs forwarding (e.g., based on the destination network address of the data message), and encapsulates the data message to be sent to the appropriate node. The agent 165 is responsible for generating and installing flow entries in the OVS daemon 170 (ovs-vswitchd) that map destination network addresses (i.e., pod subnets of nodes) to the appropriate encapsulation tunnel addresses (which typically correspond to network addresses of node network interfaces (which are different from and outside of the pod subnets allocated for the nodes).

Outside of the nodes 105, the cluster 100 also includes a CNI controller 115, which in some embodiments is implemented as a Kubernetes deployment 110. The CNI controller 115 receives configuration changes (e.g., to network policy, pod, or namespace resources) from the Kubernetes API 120 (e.g., by monitoring the Kubernetes API 120). The controller 115 computes policy rules that can be translated to OVS flow entries and provides these policy rules to the agents 165 on the nodes 105. In some embodiments, the controller 115 identifies which nodes 105 host pods 135 to which each rule applies, and only disseminate the rules to the agents 165 on the appropriate nodes 105.

The controller 115 also exposes a REST API to enable the CNI command line interface (CLI) 130 to access the controller 115. The CLI 130, in some embodiments, queries information from (i) the controller 115 and (ii) the agents 165. A user (e.g., an administrator, application developer, etc.) can use CLI queries to retrieve basic runtime information from the controller 115 and/or agents 165. In addition, as described further below, the users may also request information about the flow entries realized on the nodes, request data about ongoing connections within the cluster, and/or initiate flow trace operations through the CLI 130.

Lastly, the Octant UI 125 is an existing user interface tool for developers to use to gain insight into how applications run on a Kubernetes cluster. The CNI plugin 180 for the Octant UI 125 enables a user to view the health and basic runtime information for the controller 115 and the agents 165 in the Octant UI 125. In some embodiments, the plugin 180 accesses the Kubernetes API 120 to retrieve this information via custom resource definitions (CRDs) in the API 120 that are created by the controller 115 and each agent 165 to populate their health and runtime information.

The CNI aspects of the cluster (i.e., the controller 115, Octant CNI plugin 180, CLI 130, plugins 145, agents 165, and OVS modules 170 and 175) can be deployed in some embodiments via a single YAML manifest in some embodiments. Just as Kubernetes supports multiple platforms (e.g., Linux and Windows) for the nodes, the CNI plugin of some embodiments can also be adapted to support multiple platforms. Specifically, for Windows nodes, some embodiments directly connect containers to the physical network through an external Hyper-V switch. In this configuration, OVS operates as an extension of the Hyper-V switch that is bound to a host networking system (HNS) network. This allows the OVS bridge to process data messages sent to and from the containers on the pods. To maintain the host connectivity, the original network configuration on the physical NIC is moved to the OVS bridge in some such embodiments. In addition, in some such embodiments, both the agent 165 and the OVS daemons 170 are run as processes rather than as containers.

When using the CNI of some embodiments in the cloud, this CNI may operate in tandem with or as an alternative to a default CNI provided by the cloud provider. For instance, the CNI of some embodiments may operate in a policy-only mode, in which case the default cloud provider CNI is responsible for performing IP address management (IPAM) operations and allocating secondary IP addresses from virtual private cloud (VPC) subnets. The cloud provider CNI is already aware of the underlying VPC network and configuration. In this mode, the CNI of some embodiments does not perform any encapsulation, as all inter-node traffic is served by the VPC network. Instead, the CNI of this invention enforces Kubernetes network policies on the pods in the cluster. In addition, in some embodiments, the CNI implements its own policy specification using CRDs, thereby providing additional security features in the cloud.

In such a managed cloud, the pod network is known to the physical underlay network, and therefore (as mentioned) there is no need to perform encapsulation. In this mode, an additional bridge (referred to as a transport bridge, rather than the integration bridge shown in FIG. 1) of OVS is created on each node to allow unencapsulated access to the underlay network. The integration bridge 175 is connected to the transport bridge via a patch port, with the physical uplink attached to the transport bridge and its network address moved to the internal transport bridge interface. In this case, routing is performed using the routing tables in the host network stack. Service traffic from the gateway port uses a custom routing table, such that a packet is sent back to the integration bridge after DNAT is performed by the kube-proxy 150. In addition to these changes, some embodiments add additional flows to an ARP response table in order to respond to ARP requests.

Some embodiments allow the CNI configuration to be modified in such a way that multiple CNIs can be invoked, in a specified order, to perform various tasks involved in the life cycle of a network interface created for a container. In the cloud case here, the cloud provider CNI is invoked first to allocate an IP and MAC address from the VPC subnet. The cloud provider CNI then passes that information in the network configuration to the CNI of this invention. This CNI is then invoked with this network configuration, to create the required interface(s) for the container and program the necessary flows in OVS.

It should be understood that the cluster shown in FIG. 1 is only one example of a Kubernetes cluster in which the CNI of some embodiments may be deployed. For instance, some embodiments do not include a kube-proxy executing in the nodes and/or use a different datapath implementation rather than iptables. In addition, as shown below, other modules may also execute in the nodes.

In these contexts, the CNI agent of some embodiments on a particular node is capable of identifying flow entries used by the forwarding element of the particular node (i.e., the OVS instance on the node) related to a particular container cluster concept (e.g., a Kubernetes abstraction, such as a pod or network policy) requested by a user. The CNI agent maps elements of these flow entries to different Kubernetes concepts, including those in the request, and returns them to the requestor. In general, OVS provides useful tools that allow a user to view bridge configurations as well as raw flow entries. However, a typical Kubernetes application developer is not familiar with OVS flow entries (or the tools for viewing OVS data), which can make it more difficult for them to understand and troubleshoot the network. However, if the agent can correlate the Kubernetes abstractions (e.g., pods, services, nodes, network policies) to the internal state of the CNI (e.g., via the flow entries realized in the OVS instances), this allows such a user to identify whether the Kubernetes abstractions are properly realized.

FIG. 2 conceptually illustrates a process 200 of some embodiments for responding to a request for information about flow entries realized on a node that relate to a particular Kubernetes concept. The process 200 is performed, in some embodiments, by a CNI agent executing on a node (e.g., the agent 165 executing within a daemonset pod on a node). The process 200 will be described at least in part by reference to FIGS. 3-7. FIGS. 3 and 4 illustrate examples of the data transfer involved in different scenarios of one or more agent(s) responding to requests (either directly from the command line interface or via the controller). FIG. 5 illustrates an example of a network policy definition, while FIG. 6 illustrates a CLI request and response for the state of that network policy, and FIG. 7 illustrates an example of a CLI request and response for data about the flow entries implementing the network policy.

As shown, the process 200 begins by receiving (at 205) a request for information about flow entries associated with a particular Kubernetes concept in a cluster. The request may relate to a particular network policy (i.e., a declared Kubernetes network policy), or a specific entity in the cluster (e.g., a particular pod, node, or service). In addition, some embodiments allow more complex requests, such as a request for all flow entries relating to any network policy that are applied to a specific pod. In different embodiments, this request may be received at the CNI agent directly from the command line interface tool associated with the CNI (or a different interface with which a developer or administrator interacts) or via the centralized CNI controller (e.g., based on a request to the controller from the CLI tool).

FIG. 3 conceptually illustrates an example in which a CNI agent 300 receives and processes a request directly from the CLI tool 305 of the CNI. As shown, the agent 300 executes within a daemonset 310 on a particular node 315 on which several pods 320 also execute. Furthermore, as in FIG. 1, the OVS daemons 325 execute within the daemonset 310 and the OVS bridge 330 executes outside of the pods and provides connectivity for the pods 320. The agent 300 also stores Kubernetes cluster mapping data 335 in some embodiments. In different embodiments, this storage 335 may be a separate storage within the daemonset 310, part of the agent 300, etc. This mapping data, in some embodiments, stores data about the Kubernetes cluster that can be used for responding to flow entry requests. For instance, the mapping data 335 may include network addresses associated with specific pods, which network policies apply to which pods, which flow entries enforce which network policies, and other data used to respond to these requests. In the example shown in this figure, the CLI tool 305 receives input from a user (e.g., an application developer) that requests the flow entries realized on a particular node pertaining to a particular network policy. This request is interpreted by the CLI tool 305, which sends the request to the agent 300 at the specific node 315.

FIG. 4, on the other hand, conceptually illustrates an example in which a controller 400 sends a request to the CNI agents on multiple nodes in a cluster and aggregates the responses from these nodes. This figure illustrates three of the nodes 405-415, and for simplicity only shows the CNI agents 420-430 and the cluster mapping data 435-445 on each of these nodes 405-415. Though not shown, each of these nodes executes multiple pods as well as the OVS daemons and bridge, as in FIG. 1. In this example, the CLI tool 450 receives input from a user (e.g., an application developer) that requests the flow entries realized on any nodes in the cluster pertaining to a particular network policy. In this case, the CLI tool 450 interprets the request and forwards this request to the controller 400. The controller stores the span of the network policy (because the controller stores information about which pods to which the policy applies as well as on which nodes these pods execute). Thus, in this case, the controller 400 identifies that the particular policy applies to pods executing on at least the nodes 405 and 415 (and not on node 410) and sends requests to the agents 420 and 430 on these nodes.

Returning to the FIG. 2, the process 200 uses (at 210) the data stored regarding the locally-installed flow entries to identify flow entries realized on the node that relate to the particular Kubernetes concept specified in the request. That is, the agent identifies flow entries realized by the OVS instance executing on the node that match the request. As mentioned, the request could relate to a specific network policy, a specific pod, etc. For network policies or network policy rules, in some embodiments flow entries include a specific identifier in the one of the match or action fields. For instance, some embodiments use conjunction flow entries for certain types of network policies that simplify matches over multiple fields with many possible options for each field (these sort of flow entries are described in greater detail in U.S. Pat. No. 10,348,619, which is incorporated herein by reference). These flow entries include a conjunction identifier, which the CNI agent can use to correlate with the network policy implemented by the flow entry. Specific pods can be identified by network addresses (e.g., IP addresses) or data link addresses (e.g., MAC addresses) used as match conditions in flow entries.

Next, the process 200 selects (at 215) one of the identified flow entries that is responsive to the request. The process 200 maps (at 220) relevant match fields of the selected flow entry and/or actions specified by the selected flow entry to Kubernetes concepts in the cluster. As with the Kubernetes concepts specified in the request, the elements of the flow entry may be mapped to pods, network policies and/or network policy rules, etc. For instance, matches over table identifiers might be indicative of a specific network policy, matches over specific addresses might be indicative of specific pods, conjunction identifiers specified in actions might be indicative of a specific policy rule, etc.

The process 200 then determines (at 225) whether additional identified flow entries remain. If more flow entries remain, the process 200 returns to 215 to select another flow entry identified as responsive to the request for processing. It should be understood that the process 200 is a conceptual process, and the agent may use other methods to map all of the flow entries to Kubernetes cluster concepts. For instance, the agent of some embodiments processes some or all of the flow entries in parallel.

Once the mapping data is generated for all of the relevant flow entries, the process provides (at 230) the raw flow entry data along with the mapping data to the requestor. The process 200 then ends. As shown in FIG. 3, in the case that the CNI agent 300 receives the request directly from the CLI tool, the CNI agent 300 provides a response with the flow entries and mapping data to the CLI tool 305. The CLI tool 305 in turn provides a report with this data to the requesting user. In the case of FIG. 4, the agents 420 and 430 (as well as any other agents that received the request) provide the responsive flow entries with mapping data to the controller 400, which acts as an aggregation point. The controller 400 combines this information and provides the combined flow entries and mapping data to the CLI tool 450. Again, this CLI tool 450 provides a report with the flow entries and mapping data, in this case from multiple nodes.

As mentioned, FIG. 7 illustrates an example of such a report provided by the CLI tool of some embodiments based on data returned by a CNI agent. Before discussing this report, an example network policy will be discussed. FIG. 5 illustrates an example network policy 500 of some embodiments for web servers in a cluster. Specifically, this network policy 500 is referred to as web-app-policy and is applied to pods that match the label app=web-server. This network policy 500 is an ingress policy that, for the web-server pods, only allows http ingress traffic (TCP port 80) from pods that match the label app=web-client. That is, the web server pods are only allowed to receive http traffic from the web client pods.

The CLI tool of some embodiments enables retrieving other information about the network policies in addition to the flow entries. For a given network policy, the CLI tool can retrieve from the controller the rules for implementing that policy, the groups of addresses to which the policy relates, the pods to which the policy is applied, and the nodes on which the policy is applied. Similarly, for a specific pod, in some embodiments the CLI tool can retrieve a list of all of the network policies applied to the pod.

FIG. 6 illustrates an example of network policy state report 600 of some embodiments retrieved from the CNI controller for the network policy 500. In this case, antctl is the name of the CLI tool, and the command entered into the tool specifically requests the state of the network policy web-app-policy (in the policy namespace web-app). The report 600 provided by the controller indicates the group of pods to which the policy is applied (web-server-sp7z2 and web-server-wpgmq) and the span of the policy (nodes k8s1 and k8s2). In addition, the report 600 specifies the particular rule computed (e.g., by the controller) to enforce this policy, which specifies an allowed source IP address and allowed TCP port.

With information indicating on which nodes the policy is specified, the user can then use the CLI tool to request the flows for realizing the policy from one or more of those nodes, as in FIG. 3 (the user could also, in some embodiments, request the flow information from all nodes on which the policy is realized, as in FIG. 4). FIG. 7 illustrates such a report 700 of some embodiments showing flow entries annotated with Kubernetes cluster mapping data. In this case, the CLI request asks for flows for the network policy web-app-policy (in the policy namespace web-app) that are on the node k8s2. In response, the CNI agent on this node returns information about four flows 705-720 that are used to implement this network policy. These flows 705-720 are conjunctive flow entries which, as mentioned, are discussed in greater detail in U.S. Pat. No. 10,348,619, incorporated by reference above. It should be understood that this is a simple example, and that the realization of a typical network policy in a Kubernetes cluster would include more than one flow entry per conjunctive field.

In this example, the first flow entry 705 reads cookie=0x8501000094e9d817, table=90, priority=200, ip, nw_src=172.100.0.3 actions=conjunction (1,1/3). This raw flow entry data may be difficult for a network administrator or application developer to understand, but the report provides the generated mapping data along with the flow entry 705 (and the other flow entries) for presentation to the requestor. This mapping data indicates the table (or sub-table) of OVS to which the flow entry belongs (ingressRuleTable (NetworkPolicy ingress rules), to which table=90 maps). In addition, the mapping data specifies that this is a network policy conjunction flow that matches network policy rule conditions, and specifies the particular network policy (web-app/web-app-policy) and policy rule (rule 1). All of this information enables the user to properly contextualize the flow entry. Lastly, the mapping data indicates that the match condition ip,nw_src=172.100.0.3 means that the flow entry matches over the source IP address 172.100.0.3, which corresponds to a specific web client. Finally, the mapping data provided in the report indicates that the flow entry 705 is realized on the node.

The mapping data for flow entries 710 and 715 is similar to that for flow entry 705. The data for the second flow entry 710 indicates that the match condition ip,reg1=0x4 means that the flow entry matches on packets specifically sent to the pod web-server-sp7z2, which operates on the node k8s2. The data for the third flow entry 715 indicates that the match condition tcp, tp_dst=80 means that the flow entry matches on packets with a TCP port number of 80 (for http traffic).

Finally, the mapping data for the fourth flow entry 720 indicates that the flow entry belongs to the same table as the other three. This flow entry is different than the previous flow entries in that its match condition is a conjunction identifier conj_id=1, which is met only when the match conditions of entries for all of the conjunction dimensions have been met (i.e., the first three flow entries 705-715 have been matched). In this case, the network policy passes the data traffic because the relevant network policy rule is an allow rule. The mapping information indicates this information, as well as noting the specific network policy and rule. Furthermore, the mapping information indicates the number of packets and total bytes that have been allowed as per this flow entry. In some embodiments, the CNI agent stores data mapping these conjunction identifiers to network policy rules, so as to be able to identify the particular flow entries that implement a given network policy or policy rule.

In general, OVS supports using multiple protocols to export connection information, such as IPFIX, NetFlow, and sFlow. The CNI of some embodiments enables this export for traffic between the pods (which is governed by the CNI rules). The connection data can be exported to any number of different collectors, analyzers, and visualization consoles that support these protocols. However, standard connection exporting functionality has some limitations, in that (i) no context is added to the exported connection data to correlate flows with applications or Kubernetes abstractions, (ii) policy enforcement information is not included in the data, and (iii) the standard export mechanisms are based on packet sampling, which could generate too much flow traffic to the collector while nevertheless missing some connections.

Instead, the CNI of some embodiments provides an efficient way to export ongoing connections correlated to Kubernetes concepts and associated with network policy information, so that the consumers (e.g., a policy analytics engine, visualization solution, or direct user observation) can more easily identify the patterns of the connections within the cluster as well as the network policies and specific network policy rules that impact the different connections. Thus, some embodiments collect connection information from the data plane, append Kubernetes context to the connection information, and export the connection data (with the appended context) using, e.g., IPFIX. The context added to a connection may include source pod, source node, destination pod, destination node, destination service (if the connection is between a pod and a service in the cluster), and ingress and/or egress network policy and policy rules.

To accomplish this, in some embodiments, either the agent or another module operating on each of the container hosts along with the agent (e.g., a connection exporter module) monitors ongoing connections being processed by the forwarding element. This module retrieves data about these ongoing connections, maps the data to Kubernetes concepts implemented in the cluster, and exports the ongoing connection information along with the mapped Kubernetes concepts.

FIG. 8 conceptually illustrates a Kubernetes cluster 800 with forwarding and networking managed by a CNI plugin of some embodiments, and which has capability to monitor ongoing connections and provide information about these connections to a user (e.g., an application developer and/or administrator). As with the cluster shown in FIG. 1, the Kubernetes cluster 800 includes numerous nodes 805, as well as a deployment 810 on which a CNI controller 815 runs, a Kubernetes API 820, and a CNI command line interface 825. For simplicity, the Octant UI is not shown in this figure. Each of the nodes, as in the example of FIG. 1, includes one or more pods 835, as well as various entities such as a kubelet 840, a CNI plugin 845, a kube-proxy 850, iptables 855, a daemonset 860 that includes a CNI agent 865 and one or more OVS daemons 870, and an OVS bridge 875.

To enable connection monitoring within the cluster, in addition to these various components each node 805 executes a connection tracker 880, and within the daemonset 860 a flow exporter module 885 also operates. Furthermore, the cluster 800 includes a set of one or more flow aggregation, monitoring, and visualization servers 830.

The connection tracker 880 is a module used by the OVS bridge 875 (and, in some cases, the OVS daemons 870 as well) to store state regarding ongoing connections. The flow entries used by OVS are otherwise stateless, but in some embodiments can specify actions that request data from the connection tracker 880. The connection tracker 880 receives data about connections from the OVS bridge 875 when they are initiated, and stores this connection information (e.g., source and destination network addresses, source and destination transport layer ports, protocol, amounts of packets, and data transferred, connection state) for ongoing connections. In some embodiments, the connection tracker 880 stores this information for both incoming and outgoing directions of a connection, as well as related connections. This allows, for example, network policies to specify that a particular pod (or group of pods) cannot initiate connections with external addresses, but can only respond to such connections (or vice versa). Further information about the operation of the connection tracker 880 of some embodiments can be found in U.S. Patent Publication 2019/0149516, which is incorporated herein by reference.

The flow exporter 885 may be part of the CNI agent 865, or a separate module (e.g., operating in a separate container in the daemonset 860) in different embodiments. The flow exporter 885 retrieves the ongoing connections from the connection tracker module (e.g., at regular intervals) and maps this data to the Kubernetes concepts. In some embodiments, the flow exporter 885 polls the connection tracker 880 at 5 or 10 second intervals. The mapping operation is similar to that described above for flow entries in some embodiments and uses the cluster data stored by the CNI agent 865. The retrieved connection data includes, in some embodiments, source and destination network addresses and transport layer ports. In addition, the connection data also includes information about the status of the ongoing connection (e.g., the number of packets sent in either direction, the amount of data sent, any related connections, etc.).

The flow exporter 885 uses information from the CNI agent 865 to map connection data to the available Kubernetes cluster data, in some embodiments (as further described below, some Kubernetes cluster data may not be available to the CNI agent on a node, and is added at a centralized aggregator). As described above, the CNI agent 865 stores data that maps, e.g., IP addresses to specific pods in the cluster. This data can also include mapping of pods to specific nodes (so that the flow exporter 885 can export the connection information along with source and destination nodes in addition to source and destination pods).

Furthermore, the flow exporter also identifies network policies that are applied to each connection in some embodiments. As described above, the network policies are implemented using conjunctive match flow entries in some embodiments, and therefore policy rules can be identified by the conjunction identifiers. In some embodiments, the OVS bridge 875 is configured to provide to the connection tracker 880 the conjunction identifiers for flows that are applied to each connection. In other embodiments, other types of unique identifiers (e.g., UUIDs) are embedded in the OVS flow entries that implement network policy rules, and these unique identifiers are provided to the connection tracker 880.

The flow exporter 885 exports this connection information with mapping data to the flow aggregation, monitoring, and visualization components 830. In some embodiments, the connection information is exported in IPFIX format, with the Kubernetes concepts included as metadata (e.g., as IPFIX Information Elements). In some embodiments, rather than exporting the same connection numerous times, the flow exporter 885 tracks the connection lifecycle based on the regular updates from the connection tracker, and only sends updates to the components 830 when a flow is created or deleted (in order to reduce traffic).

The flow aggregation, monitoring, and visualization components 830 may execute within the Kubernetes cluster 800 (e.g., as a distributed service) or outside of the cluster, and may operate as a single component or as multiple separate components. For instance, some embodiments include an aggregator within the cluster that aggregates connection data (e.g., IPFIX data) from multiple nodes, then forwards this aggregated data to one or more collectors and/or visualizers outside of the cluster that are accessed by users (e.g., application developers and/or administrators).

In addition, some embodiments add Kubernetes mapping data if that data is not available at the CNI agent 865. In some cases, it may not be efficient for every agent in a cluster to store information about every Kubernetes concept in the cluster. For example, in some embodiments, the CNI agent 865 only has the pod names for each IP address, but does not store information mapping every pod in the cluster to the node on which that pod executes. Similarly, the CNI agent 865 might only store information for its local pods and not for all of the remote pods, and might not store information about distributed services in the cluster. In some such embodiments, the aggregator 830 adds its own Kubernetes concepts mapping data (e.g., source and destination node names, distributed services data) in addition to the mapping data received from the flow exporters 885.

The additional monitoring and/or visualization components 830 may be part of a larger network management and control system, in some embodiments. For instance, in some embodiments, a flow aggregation component 830 in the cluster 800 provides the aggregated data to a network management and control system visualization (e.g., NSX Intelligence). Such a management and control system can push global policies (e.g., via the CLI tool 825 or another adaptor executing in the cluster) as well as monitor network and policy realization status. These global policies are enforced by the CNI either by conversion to Kubernetes network policies or as separate flow entries, in different embodiments. This allows the network management and control system to manage multiple federated Kubernetes clusters in some embodiments, each running the same CNI plugins. The management and control system collects cluster health and statistics, as well as log bundles of the CNI components from any connected cluster in some embodiments.

The network analysis and visualization tools of such a management and control system can also be extended to consume the connection data exported by the flow exporter 885 and flow aggregation component in the cluster. Such a tool can visualize all of the connections and applied network policies within the cluster. In addition, advanced connection monitoring, policy analytics, and recommendation features of such a tool may be enabled for a Kubernetes cluster.

FIG. 9 illustrates an example of such a visualization tool graphical user interface (GUI) 900 showing connections between pods in a cluster. The visualization tool GUI 900 is part of a larger network management application, and in this case is part of the troubleshooting and monitoring features of the application. As shown, the GUI 900 illustrates the various pods of the cluster in their different groups (in this case, a web app group, a web client group, and a service A group). The visualization tool, in some embodiments, may group the pods based on various different factors (e.g., the deployment to which the pods belong).

The GUI 900 also shows ongoing (or recent) connections between the pods, as determined by the flow exporter and aggregator features within the cluster. Some embodiments, as is the case here, visualize different connections differently (e.g., different types of lines, different colors, etc.), and use arrows to show the initiation direction of the connections. In some embodiments, the GUI 900 may also be customizable to show the nodes on which each pod executes (or group the pods by node), pod IP addresses, or other information about the pods. The GUI 900 may also include options to filter out certain pods (i.e., to only show certain groups, to remove services, etc.) or connections, in some embodiments, as well as to display information about the policies in place.

FIG. 10 conceptually illustrates a process 1000 of some embodiments for reporting connection data from a node in a cluster such as that shown in FIG. 8. The process 1000 is performed, in some embodiments, by a connection exporter operating on a node (e.g., the flow exporter module 885 executing within a daemonset pod on a node), or a CNI agent if the connection export functionality is built into the agent. The process 1000 will be described at least in part by reference to FIGS. 11-13. FIG. 11 illustrates an example of the data transfer involved in the flow export process, FIG. 12 illustrates an example of connection data received from a connection tracker, and FIG. 13 illustrates an example of a connection export message sent from the export module on a node to an aggregator.

As shown, the process 1000 begins by identifying (at 1005) the expiration of a timer for polling a connection tracker module. In some embodiments, the connection exporter module uses a timer in order to ensure that the connection tracker is polled at regular intervals (e.g., every 5 seconds, every 10 seconds, every minute, etc.). It should also be understood that some embodiments may retrieve connection data form the connection tracker for other reasons (e.g., an external request for the data), either as an alternative to or in addition to regular polling.

In response to identifying the expiration of the timer (or based on another event), the process 1000 retrieves (at 1010) ongoing connection data from the connection tracker module on its node. This connection data typically includes at least source and destination network addresses and transport layer ports and may also include information about the status of the ongoing connection (e.g., the number of packets sent in either direction, the amount of data sent, any related connections, etc.).

As mentioned, FIG. 11 conceptually illustrates an example of the data transfer involved in the connection export process. This figure includes two nodes 1105 and 1110, for simplicity only showing the connection trackers 1115 and 1120, flow exporters 1125 and 1130, and CNI agents (with mapping data) 1135 and 1140. Both of the flow exporters 1125 and 1130 (as well as those on additional nodes) provide data to a connection data aggregator 1145. As shown in this figure, the flow exporter 1125 on the first node 1105 retrieves raw connection data 1150 from the connection tracker 1115 on its node 1105, while the flow exporter 1130 on the second node 1110 retrieves raw connection data 1155 from the connection tracker 1120 on its node 1110.

FIG. 12 conceptually illustrates an example of raw connection data 1200 provided by a connection tracker module to a connection exporter of some embodiments. Specifically, the data 1200 includes data for two connections 1205 and 1210. The data for the first connection 1205 indicates that the connection is a TCP connection, first listing the origination connection data (i.e., the data for the direction in which the connection was initiated) and then the reply connection data. For each direction, the connection data 1205 includes source and destination IP addresses and TCP ports (which are reversed between the two directions) as well as the amount of packets and total data processed in that direction by the local OVS bridge. In addition, the connection data 1205 indicates the current state of the connection as established. This state can be used by the flow exporter modules (e.g., to identify new connections or changes in connection state) as well as the OVS bridge on the node. The data for the second connection 1210 provides similar data, except that the state of this connection 1210 is shown as “TIME_WAIT” which indicates that the connection is in the process of being torn down (but additional packets may still need to be processed for the connection).

Returning to FIG. 10, the process 1000 next identifies (at 1015) any new connections. Some embodiments automatically generate cluster mapping data for all of the connections retrieved from the connection tracker and report all of these connections to the aggregator at each time interval, but this can waste processing resources and network bandwidth. Instead, some embodiments only report new connections or deleted (ended) connections. The connection exporter of some embodiments stores data for all of the connections for which mapping data has previously been generated and exported and compares the recently retrieved connection data to this stored data in order to identify any connections that are new.

The process 1000 then selects (at 1020) one of the identified new connections, and maps (at 1025) the source and destination addresses of the selected connection to Kubernetes concepts in the cluster (as available to the flow exporter). In some embodiments, the flow exporter maps the source and destination addresses to pods executing on its node or on other nodes, and possibly also to the nodes on which the identified pods execute.

The process 1000 also identifies (at 1030) network policies that are applied to the selected connection on the node. As described above, the connection tracker stores identifiers for the network policy rules applied to packets belonging to the connection in some embodiments. These may be conjunction identifiers (for conjunctive match flow entries), UUIDs associated with the rule, etc. The CNI agent on the node stores the mappings of these identifiers to the network policy rules, and so can identify the policy rules (and thus the network policies) applied to packets of each connection. As noted above, in some embodiments, the flow exporter only maps a portion of the Kubernetes cluster information to the connections (i.e., the portion for which the CNI agent on the node includes the mapping data). In such embodiments, the aggregator to which the connection and mapping data is reported also adds additional Kubernetes cluster mapping data to the connection information.

In some embodiments, the flow exporter queries the CNI agent for the cluster mapping data. For instance, the flow exporter of some embodiments sends the source and destination IP addresses of each new connection to the CNI agent, which returns the names of the pods associated with those addresses (and, if storing the information, the names of the nodes on which those pods execute). For network policies, the flow exporter sends the identifier(s) associated with the connection, and the CNI agent returns the policy rules (and their associated network policies) that correspond to those identifiers. FIG. 11 illustrates the CNI agents 1135 and 1140 providing mapping data 1160 and 1165 to their respective flow exporters 1125 and 1130. These flow exporters 1125 and 1130 use this data to generate the cluster mapping data for export with the connection information.

Returning to FIG. 10, the process 1000 next determines (at 1035) whether any additional new connections remain and, if so, returns to 1020 to select the next such connection. It should be understood that the process 1000 is a conceptual process, and the flow exporter may use other methods to map data for new connections to Kubernetes cluster concepts. For instance, in some embodiments the flow exporter sends a single query to the CNI agent for all of the data on all of the new connections, rather than processing each connection separately. In addition, in some cases there may not be any new connections since the last retrieval of connection data, in which case the process skips 1020-1030.

Once the mapping data has been generated for any new connections, the process 1000 identifies (at 1040) any expired (i.e., torn down) connections. The connection exporter may identify these expired connections based on the connection state provided by the connection tracker (e.g., in FIG. 12, the second connection data 1210 indicates that this connection is in the process of being torn down). In addition, when the connection exporter stores an ongoing list of all of the active connections, it can identify any connections that are no longer in the data retrieved from the connection tracker.

Finally, the process reports (at 1045) the new and expired connection data along with the Kubernetes cluster mapping data. As described above, in some embodiments the connection exporter exports this data to an aggregation service operating in the same cluster (e.g., as a service). This aggregation service compares and combines data for the same ongoing connections from source and destination nodes and may add additional Kubernetes information to the data (e.g., node information).

FIG. 11 shows that both of the flow exporters 1125 and 1130 transmit IPFIX data with both the connection and mapping data to the connection data aggregator 1145. In some embodiments, the connection data is exported in standard IPFIX format, with the mapping data appended as IPFIX information elements. Other embodiments may use other formats, such as sFlow or NetFlow. FIG. 13 conceptually illustrates a connection and mapping data export message 1300 of some embodiments from a connection exporter on a node to a connection aggregator. The export message 1300, as shown, includes information for each of multiple connections, with the mapping data appended to each set of connection data. In this example, the connection and mapping data is sent as a single message; depending on the amount of data to be transmitted, one or more messages may be sent for each export.

The connection aggregator 1145, as described above, may also (i) supplement the mapping data received from the flow exporters 1125 and 1130 and (ii) provide the connection and mapping data to additional monitoring and/or visualization components in the cluster or outside of the cluster (e.g., components of a network management and control system). These components may visualize all of the connections and applied network policies within the cluster. In addition, advanced connection monitoring, policy analytics, and recommendation features of such a tool may be enabled for a Kubernetes cluster.

FIG. 14 conceptually illustrates a process 1400 of some embodiments for performing a flow tracing operation on a Kubernetes node. The process 1400 is performed, in some embodiments, by a CNI agent executing on a node (e.g., the agent 165 executing within a daemonset pod on a node). The process 1400 will be described at least in part by reference to FIGS. 15A-15B, which illustrate an example of a flow tracing operation within a cluster.

As shown, the process 1400 begins by receiving (at 1405) a flow tracing operation request from a controller specifying characteristics of a flow tracing packet. This request, in some embodiments, defines the parameters of the packet to be used for flow tracing. The flow tracing operation, as mentioned, uses a sample data message (or multiple data messages) to enable a user (e.g., administrator, application developer) to identify the flow entries installed on one or more forwarding elements that are matched by the sample data message. This enables a user to determine whether actual data messages with similar characteristics are being properly processed by the cluster and correctly reaching their destinations. Flow tracing operations in non-Kubernetes contexts are described in greater detail in U.S. Pat. Nos. 9,282,019 and 9,197,529, both of which are incorporated herein by reference.

FIGS. 15A-15B conceptually illustrate an example of a CNI controller 1500 in a cluster of some embodiments receiving a flow tracing operation request, CNI agents and forwarding elements carrying out the flow tracing operation and reporting back results to the controller, and the controller providing a report regarding the flow tracing operation. As shown in FIG. 15A, the cluster includes the controller 1500 and at least two nodes 1505 and 1510. For simplicity, only the forwarding elements (OVS daemon and/or bridges) 1515 and 1520 and the agents 1525 and 1530 are shown, but it should be understood that the nodes 1505 and 1510 may execute all of the modules shown in FIG. 1 and/or FIG. 8. Each of the agents 1525 and 1530 stores mapping data (e.g., as shown in FIG. 3), and includes as one of its functions a flow tracing module. The flow tracing module is responsible for interpreting flow tracing requests from the controller and ensuring that these requests are performed on the node (e.g., for performing the operations of the process 1400).

In FIG. 15A, the controller 1500 receives a flow tracing request that specifies parameters for the flow tracing operation. The controller 1500 may receive this request from the CNI command line interface or directly via a different interface (e.g., a direct user interaction with the controller, the Kubernetes API, etc.). In some embodiments, the flow tracing request specifies at least the source and destination addresses (or source and destination pods/services) for the flow tracing operation. Some embodiments also require that the user specify additional parameters for the flow tracing packet, such as the source and destination ports, a layer 4 protocol, etc. In some embodiments, the users can define the tracing requests using high-level expressions and Kubernetes concepts, which are translated into network addresses and other data required for the flow tracing operation.

Based on receiving this request, the controller 1500 notifies one or more agents in the cluster by sending a message to the agents with the flow tracing request. In some embodiments, these requests indicate the parameters of the flow tracing packet (e.g., the source and destination addresses and/or ports, the protocol, etc.). In addition, in some embodiments the controller generates a set of flow trace characters for use by the agents. These flow trace characters are, e.g., a specific marker that is included in the flow tracing packet so that the flow entries identify the packet as a flow trace packet for the particular trace operation. In this example, the controller 1500 sends the message with the flow tracing request to all of the nodes in the cluster (including at least nodes 1505 and 1510). While typically only one of the agents will initiate the flow trace operation, the flow trace packet could be sent to any other node in the cluster, and thus it is beneficial for all of the agents to receive the flow trace request message. In other embodiments, only the agent for a single node is notified (e.g., if the flow trace request message will be contained to only that node), or only the agents executing on the nodes with the specified source and destination pods.

Returning to FIG. 14, the process 1400 then generates and installs (at 1410) new flow entries relating to the flow tracing operation on the forwarding element (i.e., the forwarding element on the same node). For instance, on nodes with the architecture shown in FIG. 1, the CNI agent of some embodiments installs new flow entries in the data message processing daemon in the same daemonset as the CNI agent (e.g., ovs-vswitchd). In some embodiments, all of the agents in the cluster that receive the flow tracing operation generate flow entries for their respective forwarding elements and install these respective flow entries. FIG. 15A illustrates the agents 1525 and 1530 installing flow entries for the flow tracing operation in their respective forwarding elements 1515 and 1520.

These new flow entries, in some embodiments, are copies of the existing flow entries installed in the forwarding element (e.g., for forwarding and tunneling, implementing network policies, performing network address translation, etc.), with additional match fields and/or actions. In some embodiments, the flow tracing packet includes a flow trace marker over which the new flow entries match (e.g., the flow trace characters). That is, each of these new flow entries matches over the same fields as its corresponding existing flow entry as well as the flow trace characters that will be inserted into the flow trace data packet. In addition, each of these flow entries is given a higher priority than its corresponding existing flow entry, so that the flow trace data message will match the new flow entries rather than their corresponding existing flow entries.

Furthermore, in addition to or instead of the actions specified by their corresponding existing flow entries, the new flow entries also specify further actions related to the flow trace operation. Specifically, the new flow entries specify to write information to a register (temporary storage used by the forwarding element for processing a data message) relating to the flow entry being matched. This information can indicate the table or sub-table to which the flow entry belongs, information about the network policy implemented by the flow entry (e.g., an ingress or egress rule identifier), forwarding and/or tunneling rules implemented by the flow entry, etc. In addition, some embodiments change or add to the actions of certain flow entries to ensure that a flow tracing message is returned to the CNI agent. For flow entries that include actions to drop the packet or output the packet to a port corresponding to a pod, the agent replaces these with actions that instead output the packet back to the agent. For flow entries that include actions to output the packet to a tunnel (to another node), the agent adds actions to these flow entries that also output a copy of the packet back to the agent (in addition to tunneling the packet to the other node). In addition, the output to tunnel action is modified so that the packet output will include the trace flow characters. Lastly, the agent adds a completely new flow entry so that if a packet is received via a tunnel port with the trace flow characters, the forwarding element treats the packet as a flow tracing packet. In some embodiments, a different set of trace flow characters are used for tunneling, and this action specifies for the forwarding element to set the primary trace flow characters for the packet.

The new flow entries, in some embodiments, are installed with a timeout period such that the forwarding element removes the new flow entries after a specified period of time (e.g., 1 minute, 5 minutes, 1 hour, etc.). In other embodiments, the new flow entries installed for the flow tracing operation do not have any timeout, and remain in the flow tables of the forwarding element until removed by a specific action of the CNI agent.

Next, the process 1400 injects (at 1415) a flow trace data packet (i.e., data message) at the local forwarding element on its node. This flow trace packet is injected at the forwarding element with all of the properties that a packet would have if the forwarding element received the packet from the pod specified as the packet source. That is, the packet has the source and destination addresses, protocol, etc., specified by the flow tracing operation request. In addition, the forwarding element treats the flow trace packet as though it was received at the port by which the source pod connects to the forwarding element (e.g., based on the agent setting a register value in the OVS pipeline for the packet). In addition, the flow trace packet is set to have the flow trace characters (e.g., as another set of register values) so that the packet will match the newly-installed flow entries.

The forwarding element then processes the packet through its flow table(s), during the course of which the data message matches various flow entries. In some embodiments, the ovs-vswitchd pipeline (rather than the cache in the ovs bridge) is used, so that the packet matches flow entries in multiple tables or sub-tables. As described above, the packet matches the newly-installed flow entries because these have higher priorities than their counterpart previously-existing flow entries and match over the flow trace characters. The flow entries installed for the flow tracing operation include actions for writing information about the matched flow entries into registers, so that this information can be conveyed to the agent as flow tracing operation results.

Following this processing by the forwarding element, the process 1400 receives (at 1420) a message from the forwarding element that indicates the flow entries matched by the flow trace data packet. As described above, in some embodiments, the newly-installed flow tracing flow entries have certain actions modified as compared to the original flow entries. Rather than dropping the packet or forwarding the packet to a local pod, the forwarding element outputs the packet back to the agent; in addition to forwarding the packet to another node, the forwarding element also outputs a copy of the packet back to the agent. In some embodiments, the agent is configured to read the matched flow entry data out of the registers upon receiving the packet, which allows the agent to reconstruct the entire pipeline used to process the flow trace packet (i.e., to identify which flow entries in each table or sub-table the flow trace packet matched).

FIG. 15B illustrates that the agent 1525 on the node 1505 injects a trace flow data packet to the forwarding element 1515 on that node. The forwarding element 1515 then processes the data packet through its pipeline, storing information in its registers about which flow entries are matched by the data packet. In this case, the forwarding element 1515 also makes a determination to forward the data packet to the forwarding element 1520 at another node 1510. As mentioned above, the flow tracing operation flow entries with such actions are modified to also include an action for pushing the flow trace information back to the agent. As such, in addition to forwarding the flow trace packet, the forwarding element 1515 also outputs information back to the agent 1525 indicating which flow entries were matched by the flow trace data packet. In some embodiments, the forwarding element 1515 outputs the processed packet to the agent 1525, which retrieves information from the registers of the forwarding element 1515 indicating which flow entries were matched by the packet.

The forwarding element 1520 on the node 1510 receives the flow trace data packet and also processes the packet. As shown in FIG. 15A, the agent 1530 on this node also generated and installed modified flow entries for the flow tracing operation on the forwarding element 1520. Using these flow entries, the forwarding element 1520 processes the flow trace data packet having identified the packet as a flow trace packet based on the flow trace characters embedded in the encapsulating tunnel header of the packet as received. After processing the packet, the forwarding element 1520 outputs information to the agent 1530 indicating which flow entries were matched by the flow trace data packet. As with the first forwarding element 1515, in some embodiments, the forwarding element 1520 outputs the processed packet to the agent 1530, which retrieves information from the registers of the forwarding element 1520 indicating which flow entries were matched by the packet.

Returning to FIG. 14, the process 1400 selects (at 1425) one of the identified flow entries matched by the flow trace packet while processed by the local forwarding element (e.g., retrieved by the agent from the register of the forwarding element). The process 1400 maps (at 1430) relevant match fields of the selected flow entry and/or actions specified by the selected flow entry to Kubernetes concepts in the cluster. In some embodiments, the elements of the flow entry may be mapped to pods, network policies and/or network policy rules, etc. For instance, matches over table identifiers might be indicative of a specific network policy or processing stage (e.g., spoofguard, forwarding, tunneling, ingress or egress network policy, etc.), matches over specific addresses might be indicative of specific pods, conjunction identifiers specified in actions might be indicative of a specific policy rule, etc.

The process 1400 then determines (at 1435) whether additional flow entries matched by the flow trace packet remain for analysis. If more flow entries remain, the process 1400 returns to 1425 to select another flow entry matched by the flow trace packet. It should be understood that the process 1400 is a conceptual process, and the agent may use other methods to map all of the flow entries to Kubernetes cluster concepts. For instance, the agent of some embodiments processes some or all of the flow entries in parallel.

Once the mapping data is generated for all of the matched flow entries, the process 1400 reports to the controller (at 1440) the flow entry data (i.e., the raw flow entries matched by the flow trace packet at the local forwarding element) along with the mapping data generated by the agent. In some embodiments, the agent on each node that processes the flow trace packets sends a separate report to the controller. Typically, a flow trace packet is processed by the forwarding elements of either one or two nodes depending on whether the source and destination addresses of the flow trace packet correspond to pods/services on the same or different nodes in the cluster (i.e., the packet does not pass through intermediate nodes).

FIG. 15B illustrates that both of the agents 1525 and 1530 provide reports with the list of matched flow entries (i.e., the raw flow entry data) as well as the mapping data to the controller 1500. In other cases, if the flow trace packet would typically be dropped or forwarded to a pod on the same node 1505 (i.e., a similar packet without the flow trace characters would be dropped or forwarded to a local pod), then only the agent 1525 at the source node sends this information to the controller 1500. As shown, the controller aggregates this flow entry and mapping data from each of the agents and generates a report to be presented to the user requesting the flow tracing operation in some embodiments. This controller report generation is described in further detail below.

As briefly mentioned above, in some embodiments the agent does not actually inject a packet into the forwarding element, and instead instructs the forwarding element to simulate the processing of a packet with the characteristics specified for the flow tracing operation (e.g., using a tracing tool that is part of the forwarding element). In some such embodiments, using this process (rather than injecting a flow tracing packet) only allows the operations of a single node to be traced, while other such embodiments allow for simulation at multiple nodes. FIG. 16 conceptually illustrates a process 1600 of some embodiments for performing a flow tracing operation on a Kubernetes node by having the forwarding element simulate the processing of a packet. The process 1600 is similar to the process 1400 and is performed, in some embodiments, by a CNI agent executing on a node (e.g., the agent 165 executing within a daemonset pod on a node).

As shown, the process 1600 begins by receiving (at 1605) a flow tracing operation request from a controller specifying characteristics of a flow tracing packet. This request, in some embodiments, defines the parameters of the simulated packet to be used for flow tracing. The flow tracing operation, as mentioned, simulates the processing of a sample data message (or multiple data messages) to enable a user (e.g., administrator, application developer) to identify the flow entries installed on one or more forwarding elements that would be matched by an actual data message having the same characteristics. This enables a user to determine whether data messages with these or similar characteristics are being properly processed by the cluster and correctly reaching their destinations.

The process 1600 then instructs (at 1610) the forwarding element (i.e., the forwarding element on the same node) to simulate processing of a flow trace packet with the specified characteristics. This process 1600 differs from the process 1400 in that new flow entries do not need to be generated or installed on the forwarding element. Instead, the agent uses a specific command to provide a packet description to the forwarding element, which uses this forwarding description to compute all of the flow entries that would be matched by a packet having the provided description.

Following this simulated processing by the forwarding element, the process 1600 receives (at 1615) a message from the forwarding element that indicates the flow entries matched by the packet simulation. In some embodiments, this operates differently than described above, because there is no processed packet to return to the agent (and no register data for the agent to retrieve). Instead, the forwarding element sends a message specifying the list of flow entries matched by the simulated packet.

Next, the process 1600 selects (at 1620) one of the flow entries specified in the message from the forwarding element. The process 1600 maps (at 1625) relevant match fields of the selected flow entry and/or actions specified by the selected flow entry to Kubernetes concepts in the cluster. In some embodiments, the elements of the flow entry may be mapped to pods, network policies and/or network policy rules, etc. For instance, matches over table identifiers might be indicative of a specific network policy or processing stage (e.g., spoofguard, forwarding, tunneling, ingress or egress network policy, etc.), matches over specific addresses might be indicative of specific pods, conjunction identifiers specified in actions might be indicative of a specific policy rule, etc.

The process 1600 then determines (at 1630) whether additional flow entries matched by the flow trace packet remain for analysis. If more flow entries remain, the process 1600 returns to 1620 to select another flow entry matched by the flow trace packet. It should be understood that the process 1600 is a conceptual process, and the agent may use other methods to map all of the flow entries to Kubernetes cluster concepts. For instance, the agent of some embodiments processes some or all of the flow entries in parallel.

Once the mapping data is generated for all of the matched flow entries, the process 1600 reports to the controller (at 1635) the flow entry data (i.e., the raw flow entries identified during the packet simulation) along with the mapping data generated by the agent. Because no actual packet is used, in some embodiments this technique only results in a single report to the controller, as opposed to the controller possibly receiving multiple reports from different nodes.

As described above, any agents at nodes where the flow trace data packet is processed send reports to the central CNI controller for the cluster, and the controller generates a report to be provided to the user. In some embodiments, the controller aggregates the flow entry and mapping data from each of the agents and provides a report back via the interface through which the initial request was received. In different embodiments, this report may include all of the flow entries along with mapping data or a condensed set of information based on the flow entries (e.g., source and destination pods, network policies applied to the data message, etc.).

FIG. 17 conceptually illustrates a process 1700 of some embodiments for generating such a report based on flow trace information from one or more agents. In some embodiments, the process 1700 is performed by a controller upon the controller receiving a flow trace operation request. Referring to the architecture described in FIG. 1, in different embodiments the controller receives the flow tracing operation request via the CLI 130, the Kubernetes API 120, or another interface.

As shown, the process 1700 begins by instructing (at 1705) agents in the cluster regarding the flow trace operation request. In some embodiments, the controller sends instructions that specify for a flow tracing operation to be performed and define the parameters of the packet to be used for flow tracing. As described above, in some embodiments the controller sends these instructions to each of the agents in the cluster, or a set of agents in the cluster. In addition, for the agent at the source node, in some embodiments the controller indicates that this agent should inject the flow trace data packet at its local forwarding element.

After the flow trace operation has been performed (i.e., one or more flow trace data packets have been propagated through the forwarding elements of the cluster), the process 1700 receives (at 1710) from the agents at one or more nodes information indicating (i) the flow entries matched by the forwarding elements at those nodes when processing the flow trace data packet and (ii) mapping data for the flow entries. As described above, the mapping data may include the elements of the flow entries mapped to pods, network policies and/or network policy rules, etc. For instance, matches over table identifiers might be indicative of a specific network policy or processing stage (e.g., spoofguard, forwarding, tunneling, ingress or egress network policy, etc.), matches over specific addresses might be indicative of specific pods, conjunction identifiers specified in actions might be indicative of a specific policy rule, etc.

The process 1700 aggregates (at 1715) this information and mapping data from the one or more nodes into a report and provides (at 1720) the report to the user interface (e.g., through which the initial request was received). In some embodiments, this report may be a summary of the operations that each of the forwarding elements performs, while in other embodiments the report includes all of the raw flow entries as well as mapping data for some or all of the elements of the flow entries. Some embodiments automatically generate one or the other of these types of reports, while in other embodiments the user can specify the type of report desired (e.g., by using different CLI commands).

FIG. 18 illustrates an example of a summary report 1800 output by a CLI tool of some embodiments (e.g., output through the CLI by a controller). As indicated above, antctl is the name of the CLI tool of some embodiments; in this example, the command entered into the tool requests a flow tracing operation. This flow tracing request specifies a source pod web-client and a destination pod web-server-sp7z2, as well as that the packet is an http packet.

The summary report 1800 indicates the operations performed by each of the forwarding elements at the source node (k8s1) and the destination node (k8s2). At the first node, the forwarding element receives the packet at an input port (actually, the packet is injected so that the forwarding element treats the packet as though received at that input port). Rather than specifying that input port by a UUID or another identifier, the summary report instead indicates the name of the pod (web-app/web-client). The summary report also provides information on the spoofguard operation (to ensure that the MAC and IP addresses are correct for the port), that the forwarding element performed a connection state check (e.g., using the connection tracking module described above), whether any network policy egress rule was applied (in this case, no specific rule was matched) and that because no specific rule was matched a default rule allowing the packet is applied. In addition, the report specifies that the L3 forwarding operation identifies a destination tunnel using the name of the destination node (k8s2) as well as the tunneling IP address for that node, and finally indicates that the packet is output to the port tun0 (i.e., the tunnel port on the node).

At the second node (k8s2), the summary report 1800 indicates that the forwarding element receives the flow trace packet at its tunnel port (also tun0), and performs a connection state check (e.g., using the connection tracking module described above). The summary report also indicates that L3 forwarding identifies a local destination for the flow trace packet (using both the pod name web-server-sp7z2 as well as the IP address) and that L2 forwarding identifies an output port of the forwarding element for the flow trace packet (again indicating the pod name, along with the MAC address). On this end, a specific ingress network policy rule is matched, so the summary report 1800 indicates the network policy (web-app/web-app-policy) as well as the specific rule (rule1). Finally, the summary report specifies that the forwarding element outputs the flow trace packet to the output port for the destination pod (though the forwarding element does not actually output the flow trace packet to the pod).

FIGS. 19A-B, on the other hand, illustrate an example of a report 1900 aggregating the full set of raw flow entries and mapping data from the same source and destination nodes (k8s1 and k8s2) as in the previous summary report. As shown in FIG. 19A, in this example, the command received at the CLI is similar, but also specifies to see all flows (i.e., all flow entries). Here, each raw flow entry matched by the flow trace packet at the forwarding element in the first node (k8s1) is provided in order (indicated by table numbers 0, 10, 30, 31, etc.). In addition to the raw flow entry information, additional data mapping the flow entry information to Kubernetes cluster concepts, generated by the agent at the first node, is provided. For instance, the mapping data for the first flow entry specifies that this flow entry belongs to the classifier table (source port classification), classifies the source of traffic, and indicates that the ingress port corresponds to the pod web-app/web-client. Similarly, the mapping data for the second flow entry specifies that this flow entry belongs to the spoofguard table and that the packet has the allowed source MAC and IP addresses for the input port (in addition to indicating the input port and addresses). As shown, similar information is provided for additional flow entries relating to connection tracking, destination NAT (for redirecting service traffic to the gw0 port, not required in this case), egress network policy rules (in this case, the default allow rule is applied), L3 forwarding and tunneling, (mapping the destination IP address to a pod subnet and remote node), and L2 forwarding out of the local tunnel port.

FIG. 19B shows a continuation of the report 1900 with the raw flow entries matched by the flow trace packet at the forwarding element in the second node (k8s2), again in order by the table numbers. For simplicity, this example removes some of the early flow entries (e.g., relating to connection tracking). The additional mapping data generated by the agent at the second node is also provided. For the first flow entry shown, this mapping data specifies that the flow entry belongs to the classifier table (source port classification). The figure then skips the intermediate (e.g., connection tracking) flow entries to the L3 forwarding table flow entry, which forwards to a local pod based on the destination IP address, and rewrites the MAC address (the mapping data also specifies the pod name (web-ap/web-server-sp7z2) as well as its IP address. The mapping data for the L2 forwarding calculation table flow entry specifies the output port for the destination pod based on the MAC address, and the ingress rule table flow entry is matched to a specific ingress network policy and rule. Finally, the connection tracking commit operation is indicated, as well as the L2 forwarding output table flow entry.

It should be noted that summary reports (such as that shown in FIG. 18) and more detailed reports (such as that shown in FIG. 19) may be generated by either of the previously-described flow tracing techniques in some embodiments. That is, these reports may be generated based on either (i) injection of a flow tracing packet to a forwarding element and subsequent processing of the flow tracing packet by that forwarding element or another forwarding element or (ii) simulation by a flow tracing tool of the forwarding element(s).

FIG. 20 illustrates an example of such a graphical report 2000. This report shows the results for a flow tracing packet injected at the forwarding element executing on node k8s1, simulating a packet sent from the pod web-app/web-client, and indicates the operations performed by the forwarding elements at both the source node (k8s1) and the destination node (k8s2). At the first node, the forwarding element receives the packet at an input port (actually, the packet is injected so that the forwarding element treats the packet as though received at that input port) corresponding to the pod web-app/web-client. In this diagram, operations are shown to have forwarded the flow tracing packet if the packet is not dropped and is instead sent to the next stage. Thus, the spoofguard flow entry (or entries) validates the flow tracing packet, as does the network policy flow entry/entries (in this case, a default allow rule is applied). The flow tracing packet is forwarded to the node k8s2, and the forwarding element on that node applies an ingress network policy rule. As the Kubernetes mapping information includes information about which rule was applied, some embodiments also include this information in the graphical report 2000. The graphical report also indicates that the flow tracing packet is delivered to its destination pod web-app/web-server, although the forwarding element does not actually output the flow tracing packet to the pod (and instead drops the flow tracing packet).

It should be noted that, while this example graphical report only shows certain features (network policy, spoofguard, L3 forwarding), it should be understood that some embodiments may display additional stages or features that are available in the flow entry and Kubernetes mapping data, such as load balancing (e.g., L2 and/or L3 load balancing), additional tunnel information, SNAT information, flow tracing packet register data after each stage, etc. That is, the reported information from the node may include various stages of packet processing that the flow trace packet passed (i.e., without being dropped), including input, spoofguard, load balancing, network policy (both ingress and egress rules), L2 and/or L3 forwarding, tunneling, and output. In some embodiments, the CNI agent reports each of these stages that the packet passed.

In addition, while the examples (both the graphical report and the previous examples) illustrate cases in which only one or two nodes receive a flow tracing packet, some embodiments allow for the flow tracing packet to be forwarded to/through more than two nodes (as all of the agents in the cluster generate and install the flow entries for the flow trace operation). For instance, broadcast or multicast packets may be injected, which can be forwarded to many nodes in a cluster. Similarly, certain packets may be forwarded from a first node to a second node via a third node (or additional nodes). In these cases, the agents on all of the nodes that process the flow tracing packet generate mapping data for the flow entries that match on the flow tracing packet and provide information to the controller regarding the flow tracing operation and the mapping data.

In addition to flow tracing, it should also be noted that some embodiments enable monitoring of the cluster operations by capturing data messages (i.e., data messages for ongoing connections, rather than specified flow trace data messages) at various stages in the forwarding path. In some such embodiments, the user uses Kubernetes concepts to specify what traffic should be captured (e.g., by using pod names to identify source and destination pods for traffic that should be captured).

FIG. 21 conceptually illustrates an electronic system 2100 with which some embodiments of the invention are implemented. The electronic system 2100 may be a computer (e.g., a desktop computer, personal computer, tablet computer, server computer, mainframe, a blade computer etc.), phone, PDA, or any other sort of electronic device. Such an electronic system includes various types of computer readable media and interfaces for various other types of computer readable media. Electronic system 2100 includes a bus 2105, processing unit(s) 2110, a system memory 2125, a read-only memory 2130, a permanent storage device 2135, input devices 2140, and output devices 2145.

The bus 2105 collectively represents all system, peripheral, and chipset buses that communicatively connect the numerous internal devices of the electronic system 2100. For instance, the bus 2105 communicatively connects the processing unit(s) 2110 with the read-only memory 2130, the system memory 2125, and the permanent storage device 2135.

From these various memory units, the processing unit(s) 2110 retrieve instructions to execute and data to process in order to execute the processes of the invention. The processing unit(s) may be a single processor or a multi-core processor in different embodiments.

The read-only-memory (ROM) 2130 stores static data and instructions that are needed by the processing unit(s) 2110 and other modules of the electronic system. The permanent storage device 2135, on the other hand, is a read-and-write memory device. This device is a non-volatile memory unit that stores instructions and data even when the electronic system 2100 is off. Some embodiments of the invention use a mass-storage device (such as a magnetic or optical disk and its corresponding disk drive) as the permanent storage device 2135.

Other embodiments use a removable storage device (such as a floppy disk, flash drive, etc.) as the permanent storage device. Like the permanent storage device 2135, the system memory 2125 is a read-and-write memory device. However, unlike storage device 2135, the system memory is a volatile read-and-write memory, such a random-access memory. The system memory stores some of the instructions and data that the processor needs at runtime. In some embodiments, the invention's processes are stored in the system memory 2125, the permanent storage device 2135, and/or the read-only memory 2130. From these various memory units, the processing unit(s) 2110 retrieve instructions to execute and data to process in order to execute the processes of some embodiments.

The bus 2105 also connects to the input and output devices 2140 and 2145. The input devices enable the user to communicate information and select commands to the electronic system. The input devices 2140 include alphanumeric keyboards and pointing devices (also called “cursor control devices”). The output devices 2145 display images generated by the electronic system. The output devices include printers and display devices, such as cathode ray tubes (CRT) or liquid crystal displays (LCD). Some embodiments include devices such as a touchscreen that function as both input and output devices.

Finally, as shown in FIG. 21, bus 2105 also couples electronic system 2100 to a network 2165 through a network adapter (not shown). In this manner, the computer can be a part of a network of computers (such as a local area network (“LAN”), a wide area network (“WAN”), or an Intranet, or a network of networks, such as the Internet. Any or all components of electronic system 2100 may be used in conjunction with the invention.

Some embodiments include electronic components, such as microprocessors, storage and memory that store computer program instructions in a machine-readable or computer-readable medium (alternatively referred to as computer-readable storage media, machine-readable media, or machine-readable storage media). Some examples of such computer-readable media include RAM, ROM, read-only compact discs (CD-ROM), recordable compact discs (CD-R), rewritable compact discs (CD-RW), read-only digital versatile discs (e.g., DVD-ROM, dual-layer DVD-ROM), a variety of recordable/rewritable DVDs (e.g., DVD-RAM, DVD-RW, DVD+RW, etc.), flash memory (e.g., SD cards, mini-SD cards, micro-SD cards, etc.), magnetic and/or solid state hard drives, read-only and recordable Blu-Ray® discs, ultra-density optical discs, any other optical or magnetic media, and floppy disks. The computer-readable media may store a computer program that is executable by at least one processing unit and includes sets of instructions for performing various operations. Examples of computer programs or computer code include machine code, such as is produced by a compiler, and files including higher-level code that are executed by a computer, an electronic component, or a microprocessor using an interpreter.

While the above discussion primarily refers to microprocessor or multi-core processors that execute software, some embodiments are performed by one or more integrated circuits, such as application specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs). In some embodiments, such integrated circuits execute instructions that are stored on the circuit itself.

As used in this specification, the terms “computer”, “server”, “processor”, and “memory” all refer to electronic or other technological devices. These terms exclude people or groups of people. For the purposes of the specification, the terms display or displaying means displaying on an electronic device. As used in this specification, the terms “computer readable medium,” “computer readable media,” and “machine readable medium” are entirely restricted to tangible, physical objects that store information in a form that is readable by a computer. These terms exclude any wireless signals, wired download signals, and any other ephemeral signals.

This specification refers throughout to computational and network environments that include virtual machines (VMs). However, virtual machines are merely one example of data compute nodes (DCNs) or data compute end nodes, also referred to as addressable nodes. DCNs may include non-virtualized physical hosts, virtual machines, containers that run on top of a host operating system without the need for a hypervisor or separate operating system, and hypervisor kernel network interface modules.

VMs, in some embodiments, operate with their own guest operating systems on a host using resources of the host virtualized by virtualization software (e.g., a hypervisor, virtual machine monitor, etc.). The tenant (i.e., the owner of the VM) can choose which applications to operate on top of the guest operating system. Some containers, on the other hand, are constructs that run on top of a host operating system without the need for a hypervisor or separate guest operating system. In some embodiments, the host operating system uses name spaces to isolate the containers from each other and therefore provides operating-system level segregation of the different groups of applications that operate within different containers. This segregation is akin to the VM segregation that is offered in hypervisor-virtualized environments that virtualize system hardware, and thus can be viewed as a form of virtualization that isolates different groups of applications that operate in different containers. Such containers are more lightweight than VMs.

Hypervisor kernel network interface modules, in some embodiments, is a non-VM DCN that includes a network stack with a hypervisor kernel network interface and receive/transmit threads. One example of a hypervisor kernel network interface module is the vmknic module that is part of the ESXi™ hypervisor of VMware, Inc.

It should be understood that while the specification refers to VMs, the examples given could be any type of DCNs, including physical hosts, VMs, non-VM containers, and hypervisor kernel network interface modules. In fact, the example networks could include combinations of different types of DCNs in some embodiments.

While the invention has been described with reference to numerous specific details, one of ordinary skill in the art will recognize that the invention can be embodied in other specific forms without departing from the spirit of the invention. In addition, a number of the figures (including FIGS. 2, 10, 14, 16, and 17) conceptually illustrate processes. The specific operations of these processes may not be performed in the exact order shown and described. The specific operations may not be performed in one continuous series of operations, and different specific operations may be performed in different embodiments. Furthermore, the process could be implemented using several sub-processes, or as part of a larger macro process. Thus, one of ordinary skill in the art would understand that the invention is not to be limited by the foregoing illustrative details, but rather is to be defined by the appended claims.

Claims

1. For an agent executing on a Kubernetes node in a cluster, a method comprising: receiving a message from a controller in the cluster specifying parameters for the flow tracing packet prior to instructing the forwarding element;installing a plurality of new flow entries in the forwarding element, each of the plurality of new flow entries being configured to match a flow tracing marker associated with the flow tracing packet, and each of the plurality of new flow entries being characterized by a priority higher than its corresponding existing flow entry;instructing a forwarding element that also executes on the node to process a flow tracing packet;receiving, from the forwarding element, a message indicating a set of flow entries matched by the flow tracing packet as the forwarding element processes the flow tracing packet;for each flow entry of at least a subset of the flow entries matched by the flow tracing packet, generating mapping data that maps elements of the flow entry to Kubernetes concepts implemented in the cluster; andreporting data regarding the set of flow entries along with the generated mapping data.
2. The method of claim 1 wherein the forwarding element uses at least a subset of the new flow entries to process the flow tracing packet.
3. The method of claim 2, wherein the new flow entries comprise copies of existing flow entries with at least one of (i) one or more additional match fields and (ii) one or more additional actions.
4. The method of claim 3, wherein: the additional match fields match on a flow tracing marker that accompanies the flow tracing packet; andthe additional actions specify to store information regarding the matched flow entry in a register so that the information is available to be included in the message sent to the agent indicating the set of flow entries matched by the flow tracing packet.
5. The method of claim 3, wherein the new flow entries further comprise an additional set of flow entries that specify to send the message to the agent after the forwarding element completes processing of the flow tracing packet.
6. The method of claim 2, wherein the new flow entries are installed with a timeout period such that the forwarding element removes the new flow entries after a specified period of time.
7. The method of claim 2, wherein agents execute on a plurality of nodes in the cluster and install sets of new flow entries in forwarding elements executing on the plurality of nodes.
8. The method of claim 1, wherein the cluster comprises a plurality of nodes, wherein agents execute on each node in the cluster.
9. The method of claim 1, wherein each respective flow entry comprises (i) a respective set of match conditions and (ii) a respective set of actions to be performed when a data message matches the respective set of match conditions.
10. The method of claim 9, wherein generating mapping data for a particular flow entry comprises mapping at least one of the match conditions for the particular flow to a particular Kubernetes network policy.
11. The method of claim 9, wherein generating mapping data for a particular flow entry comprises mapping at least one of the match conditions for the particular flow to a particular pod and specifying a name for the particular pod.
12. The method of claim 9, wherein generating mapping data for a particular flow entry comprises mapping at least one of the match conditions for the particular flow to a particular service and specifying a name for the particular service.
13. The method of claim 9, wherein the flow entries are organized into a plurality of tables, wherein generating the mapping data comprises: identifying that a particular flow entry is in a particular table that corresponds to a particular Kubernetes network policy; andspecifying a particular rule of the particular Kubernetes network policy to which the particular flow entry corresponds.
14. The method of claim 1, wherein instructing the forwarding element to process the flow tracing packet comprises instructing the forwarding element to simulate processing of a packet with characteristics of the flow tracing packet.
15. The method of claim 1, wherein: generating the mapping data comprises generating a set of packet processing stages passed by the flow tracing packet; andreporting data regarding the set of flow entries along with the generated mapping data comprises reporting the set of packet processing stages.
16. The method of claim 15, wherein the set of packet processing stages comprises at least one of input processing, spoofguard processing, load balancing, ingress network policy processing, egress network policy processing, layer 3 (L3) forwarding, layer 2 (L2) forwarding, tunneling, and output processing.
17. A method comprising: receiving a request for flow entries associated with a particular Kubernetes concept;installing, prior to receiving the request, a plurality of new flow entries in a forwarding element, each of the plurality of new flow entries being configured to match a flow tracing marker and characterized by a priority higher than its corresponding existing flow entry;identifying flow entries that match the request;for each flow entry that matches the request, generating mapping data that maps elements of the flow entry to additional Kubernetes concepts; andproviding the flow entries with the mapping data in response to the request.
18. The method of claim 17, wherein: the method is performed by an agent executing on a particular node of a cluster;the identified flow entries comprise flow entries used by a forwarding element also executing on the particular node.
19. The method of claim 18, wherein the particular node is one of (i) a virtual machine (VM) and (ii) a physical server computer.
20. The method of claim 17, wherein each respective flow entry comprises (i) a respective set of match conditions and (ii) a respective set of actions to be performed when a data message matches the respective set of match conditions.
21. The method of claim 17, wherein: the particular Kubernetes concept is a particular pod; andidentifying flow entries that match the request comprises identifying flow entries that are applied to data messages sent to and from the particular pod.

Priority Claims (1)

Number	Date	Country	Kind
PCT/CN2020/105533	Jul 2020	WO	international

CLAIM OF BENEFIT TO PRIOR APPLICATION

This application is a continuation application of U.S. patent application Ser. No. 17/006,845, filed Aug. 30, 2020, now published as U.S. Patent Publication 2022/0038368. U.S. patent application Ser. No. 17/006,845 claims the benefit of International Patent Application PCT/CN/2020105533, filed Jul. 29, 2020. U.S. patent application Ser. No. 17/006,845, now published as U.S. Patent Publication 2022/0038368 is incorporated herein by reference.

US Referenced Citations (437)

Number	Name	Date	Kind
5224100	Lee et al.	Jun 1993	A
5245609	Ofek et al.	Sep 1993	A
5265092	Soloway et al.	Nov 1993	A
5504921	Dev et al.	Apr 1996	A
5550816	Hardwick et al.	Aug 1996	A
5729685	Chatwani et al.	Mar 1998	A
5751967	Raab et al.	May 1998	A
5781534	Perlman et al.	Jul 1998	A
5805819	Chin et al.	Sep 1998	A
6104699	Holender et al.	Aug 2000	A
6104700	Haddock et al.	Aug 2000	A
6141738	Munter et al.	Oct 2000	A
6219699	McCloghrie et al.	Apr 2001	B1
6253337	Maloney et al.	Jun 2001	B1
6430160	Smith et al.	Aug 2002	B1
6456624	Eccles et al.	Sep 2002	B1
6512745	Abe et al.	Jan 2003	B1
6539432	Taguchi et al.	Mar 2003	B1
6658002	Ross et al.	Dec 2003	B1
6680934	Cain	Jan 2004	B1
6721334	Ketcham	Apr 2004	B1
6785843	McRae et al.	Aug 2004	B1
6882642	Kejriwal et al.	Apr 2005	B1
6941487	Balakrishnan et al.	Sep 2005	B1
6963585	Pennec et al.	Nov 2005	B1
6999454	Crump	Feb 2006	B1
7013342	Riddle	Mar 2006	B2
7047423	Maloney et al.	May 2006	B1
7062559	Yoshimura et al.	Jun 2006	B2
7079544	Wakayama et al.	Jul 2006	B2
7180856	Breslau et al.	Feb 2007	B1
7197572	Matters et al.	Mar 2007	B2
7200144	Terrell et al.	Apr 2007	B2
7209439	Rawlins et al.	Apr 2007	B2
7243143	Bullard	Jul 2007	B1
7283473	Arndt et al.	Oct 2007	B2
7315985	Gauvin et al.	Jan 2008	B1
7342916	Das et al.	Mar 2008	B2
7360158	Beeman	Apr 2008	B1
7391771	Orava et al.	Jun 2008	B2
7450598	Chen et al.	Nov 2008	B2
7457870	Lownsbrough et al.	Nov 2008	B1
7463579	Lapuh et al.	Dec 2008	B2
7478173	Delco	Jan 2009	B1
7483370	Dayal et al.	Jan 2009	B1
7555002	Arndt et al.	Jun 2009	B2
7577131	Joseph et al.	Aug 2009	B2
7590133	Hatae et al.	Sep 2009	B2
7602723	Mandato et al.	Oct 2009	B2
7606260	Oguchi et al.	Oct 2009	B2
7627692	Pessi	Dec 2009	B2
7633955	Saraiya et al.	Dec 2009	B1
7639625	Kaminsky et al.	Dec 2009	B2
7643488	Khanna et al.	Jan 2010	B2
7649851	Takashige et al.	Jan 2010	B2
7706266	Plamondon	Apr 2010	B2
7710874	Balakrishnan et al.	May 2010	B2
7729245	Breslau et al.	Jun 2010	B1
7760735	Chen et al.	Jul 2010	B1
7764599	Doi et al.	Jul 2010	B2
7792987	Vohra et al.	Sep 2010	B1
7802000	Huang et al.	Sep 2010	B1
7808919	Nadeau et al.	Oct 2010	B2
7808929	Wong et al.	Oct 2010	B2
7818452	Matthews et al.	Oct 2010	B2
7826482	Minei et al.	Nov 2010	B1
7839847	Nadeau et al.	Nov 2010	B2
7885276	Lin	Feb 2011	B1
7936770	Frattura et al.	May 2011	B1
7937438	Miller et al.	May 2011	B1
7937492	Kompella et al.	May 2011	B1
7948986	Ghosh et al.	May 2011	B1
7953865	Miller et al.	May 2011	B1
7991859	Miller et al.	Aug 2011	B1
7995483	Bayar et al.	Aug 2011	B1
8018943	Pleshek et al.	Sep 2011	B1
8024478	Patel	Sep 2011	B2
8027354	Portolani et al.	Sep 2011	B1
8031606	Memon et al.	Oct 2011	B2
8031633	Bueno et al.	Oct 2011	B2
8046456	Miller et al.	Oct 2011	B1
8054832	Shukla et al.	Nov 2011	B1
8055789	Richardson et al.	Nov 2011	B2
8060875	Lambeth	Nov 2011	B1
8131852	Miller et al.	Mar 2012	B1
8149737	Metke et al.	Apr 2012	B2
8155028	Abu-Hamdeh et al.	Apr 2012	B2
8161270	Parker et al.	Apr 2012	B1
8166201	Richardson et al.	Apr 2012	B2
8199750	Schultz et al.	Jun 2012	B1
8223668	Allan et al.	Jul 2012	B2
8224931	Brandwine et al.	Jul 2012	B1
8224971	Miller et al.	Jul 2012	B1
8254273	Kaminsky et al.	Aug 2012	B2
8265062	Tang et al.	Sep 2012	B2
8265075	Pandey	Sep 2012	B2
8281067	Stolowitz	Oct 2012	B2
8290137	Yurchenko et al.	Oct 2012	B2
8306043	Breslau et al.	Nov 2012	B2
8312129	Miller et al.	Nov 2012	B1
8339959	Moisand et al.	Dec 2012	B1
8339994	Gnanasekaran et al.	Dec 2012	B2
8345558	Nicholson et al.	Jan 2013	B2
8351418	Zhao et al.	Jan 2013	B2
8359576	Prasad et al.	Jan 2013	B2
8456984	Ranganathan et al.	Jun 2013	B2
8504718	Wang et al.	Aug 2013	B2
8565108	Marshall et al.	Oct 2013	B1
8571031	Davies et al.	Oct 2013	B2
8611351	Gooch et al.	Dec 2013	B2
8612627	Brandwine	Dec 2013	B1
8625594	Safrai et al.	Jan 2014	B2
8625603	Ramakrishnan et al.	Jan 2014	B1
8625616	Vobbilisetty et al.	Jan 2014	B2
8644188	Brandwine et al.	Feb 2014	B1
8645952	Biswas et al.	Feb 2014	B2
8713467	Goldenberg et al.	Apr 2014	B1
8750288	Nakil et al.	Jun 2014	B2
8762501	Kempf et al.	Jun 2014	B2
8806005	Miri et al.	Aug 2014	B2
8837300	Nedeltchev et al.	Sep 2014	B2
8838743	Lewites et al.	Sep 2014	B2
8929221	Breslau et al.	Jan 2015	B2
8934495	Hilton et al.	Jan 2015	B1
9059926	Akhter et al.	Jun 2015	B2
9197529	Ganichev et al.	Nov 2015	B2
9226220	Banks et al.	Dec 2015	B2
9258195	Pendleton et al.	Feb 2016	B1
9280448	Farrell et al.	Mar 2016	B2
9282019	Ganichev et al.	Mar 2016	B2
9311670	Hoffberg	Apr 2016	B2
9344349	Ganichev et al.	May 2016	B2
9407580	Ganichev et al.	Aug 2016	B2
9602334	Benny	Mar 2017	B2
9658983	Barber et al.	May 2017	B1
9729433	Polland	Aug 2017	B2
9860151	Ganichev et al.	Jan 2018	B2
9898317	Nakil et al.	Feb 2018	B2
10044581	Russell	Aug 2018	B1
10181993	Ganichev et al.	Jan 2019	B2
10200306	Nhu et al.	Feb 2019	B2
10225309	Rosenzweig et al.	Mar 2019	B1
10469342	Lenglet et al.	Nov 2019	B2
10608887	Jain et al.	Mar 2020	B2
10728121	Chitalia et al.	Jul 2020	B1
10778557	Ganichev et al.	Sep 2020	B2
10805239	Nhu et al.	Oct 2020	B2
11075847	Kwan et al.	Jul 2021	B1
11088916	Chandrashekhar et al.	Aug 2021	B1
11128550	Lenglet et al.	Sep 2021	B2
11196628	Shen et al.	Dec 2021	B1
11201808	Ganichev et al.	Dec 2021	B2
11240163	Chen et al.	Feb 2022	B2
11258763	Cummins et al.	Feb 2022	B2
11283699	Chen et al.	Mar 2022	B2
11336533	Bogado et al.	May 2022	B1
11336590	Nhu et al.	May 2022	B2
11444864	K	Sep 2022	B2
11558426	Shen et al.	Jan 2023	B2
11570090	Shen	Jan 2023	B2
11620338	Bullard et al.	Apr 2023	B1
11677645	Cheng et al.	Jun 2023	B2
11687210	Parashar et al.	Jun 2023	B2
11706109	Cheng et al.	Jul 2023	B2
11711278	Gajar et al.	Jul 2023	B2
11736436	Nigam et al.	Aug 2023	B2
20010020266	Kojima et al.	Sep 2001	A1
20010043614	Viswanadham et al.	Nov 2001	A1
20020093952	Gonda	Jul 2002	A1
20020112060	Kato	Aug 2002	A1
20020178356	Mattila	Nov 2002	A1
20020194369	Rawlins et al.	Dec 2002	A1
20030041170	Suzuki	Feb 2003	A1
20030058850	Rangarajan et al.	Mar 2003	A1
20030067497	Pichon	Apr 2003	A1
20030197702	Turner et al.	Oct 2003	A1
20040024879	Dingman et al.	Feb 2004	A1
20040073659	Rajsic et al.	Apr 2004	A1
20040098505	Clemmensen	May 2004	A1
20040186914	Shimada	Sep 2004	A1
20040267866	Carollo et al.	Dec 2004	A1
20040267897	Hill et al.	Dec 2004	A1
20050018669	Arndt et al.	Jan 2005	A1
20050027881	Figueira et al.	Feb 2005	A1
20050053079	Havala	Mar 2005	A1
20050083953	May	Apr 2005	A1
20050105524	Stevens et al.	May 2005	A1
20050111445	Wybenga et al.	May 2005	A1
20050120160	Plouffe et al.	Jun 2005	A1
20050132044	Guingo et al.	Jun 2005	A1
20050149604	Navada	Jul 2005	A1
20050182853	Lewites et al.	Aug 2005	A1
20050220030	Nagami et al.	Oct 2005	A1
20050220096	Friskney et al.	Oct 2005	A1
20050232230	Nagami et al.	Oct 2005	A1
20060002370	Rabie et al.	Jan 2006	A1
20060026225	Canali et al.	Feb 2006	A1
20060028999	Iakobashvili et al.	Feb 2006	A1
20060029056	Perera et al.	Feb 2006	A1
20060037075	Frattura et al.	Feb 2006	A1
20060174087	Hashimoto et al.	Aug 2006	A1
20060187908	Shimozono et al.	Aug 2006	A1
20060193266	Siddha et al.	Aug 2006	A1
20060206655	Chappell et al.	Sep 2006	A1
20060218447	Garcia et al.	Sep 2006	A1
20060221961	Basso et al.	Oct 2006	A1
20060282895	Rentzis et al.	Dec 2006	A1
20060291388	Amdahl et al.	Dec 2006	A1
20070050763	Kagan et al.	Mar 2007	A1
20070055789	Claise et al.	Mar 2007	A1
20070064673	Bhandaru et al.	Mar 2007	A1
20070097982	Wen et al.	May 2007	A1
20070156919	Potti et al.	Jul 2007	A1
20070260721	Bose et al.	Nov 2007	A1
20070286185	Eriksson et al.	Dec 2007	A1
20070297428	Bose et al.	Dec 2007	A1
20080002579	Lindholm et al.	Jan 2008	A1
20080002683	Droux et al.	Jan 2008	A1
20080021925	Sweeney	Jan 2008	A1
20080049614	Briscoe et al.	Feb 2008	A1
20080049621	McGuire et al.	Feb 2008	A1
20080049752	Grant	Feb 2008	A1
20080049786	Ram et al.	Feb 2008	A1
20080059556	Greenspan et al.	Mar 2008	A1
20080071900	Hecker et al.	Mar 2008	A1
20080086726	Griffith et al.	Apr 2008	A1
20080112551	Forbes et al.	May 2008	A1
20080159301	Heer	Jul 2008	A1
20080240095	Basturk	Oct 2008	A1
20090010254	Shimada	Jan 2009	A1
20090100298	Lange et al.	Apr 2009	A1
20090109973	Ilnicki	Apr 2009	A1
20090116497	Varma et al.	May 2009	A1
20090150527	Tripathi et al.	Jun 2009	A1
20090245138	Sapsford et al.	Oct 2009	A1
20090248895	Archer et al.	Oct 2009	A1
20090249213	Murase et al.	Oct 2009	A1
20090292858	Lambeth et al.	Nov 2009	A1
20090327903	Smith et al.	Dec 2009	A1
20100128623	Dunn et al.	May 2010	A1
20100131636	Suri et al.	May 2010	A1
20100188976	Rahman et al.	Jul 2010	A1
20100214949	Smith et al.	Aug 2010	A1
20100232435	Jabr et al.	Sep 2010	A1
20100254385	Sharma et al.	Oct 2010	A1
20100275199	Smith et al.	Oct 2010	A1
20100306408	Greenberg et al.	Dec 2010	A1
20100332626	Jönsson et al.	Dec 2010	A1
20110022695	Dalal et al.	Jan 2011	A1
20110055389	Bley	Mar 2011	A1
20110055710	Kirkby et al.	Mar 2011	A1
20110063979	Matthews et al.	Mar 2011	A1
20110075664	Lambeth et al.	Mar 2011	A1
20110085557	Gnanasekaran et al.	Apr 2011	A1
20110085559	Chung et al.	Apr 2011	A1
20110085563	Kotha et al.	Apr 2011	A1
20110107271	Borchardt et al.	May 2011	A1
20110128959	Bando et al.	Jun 2011	A1
20110137602	Desineni et al.	Jun 2011	A1
20110194567	Shen	Aug 2011	A1
20110202920	Takase	Aug 2011	A1
20110261825	Ichino	Oct 2011	A1
20110265023	Loomis et al.	Oct 2011	A1
20110299413	Chatwani et al.	Dec 2011	A1
20110299534	Koganti et al.	Dec 2011	A1
20110299537	Saraiya et al.	Dec 2011	A1
20110305167	Koide	Dec 2011	A1
20110317559	Kern et al.	Dec 2011	A1
20110317696	Aldrin et al.	Dec 2011	A1
20120079478	Galles et al.	Mar 2012	A1
20120151352	Ramprasad et al.	Jun 2012	A1
20120159454	Barham et al.	Jun 2012	A1
20120182992	Cowart et al.	Jul 2012	A1
20120191432	Khataniar et al.	Jul 2012	A1
20120275331	Benköet al.	Nov 2012	A1
20120287791	Xi et al.	Nov 2012	A1
20120311131	Arrasvuori	Dec 2012	A1
20120314599	Vilke et al.	Dec 2012	A1
20130010600	Jocha et al.	Jan 2013	A1
20130019008	Jorgenson et al.	Jan 2013	A1
20130024579	Zhang et al.	Jan 2013	A1
20130031233	Feng et al.	Jan 2013	A1
20130041934	Annamalaisami et al.	Feb 2013	A1
20130044636	Koponen et al.	Feb 2013	A1
20130054625	Bhagwan	Feb 2013	A1
20130054761	Kempf et al.	Feb 2013	A1
20130058346	Sridharan et al.	Mar 2013	A1
20130067067	Miri et al.	Mar 2013	A1
20130097329	Alex et al.	Apr 2013	A1
20130125120	Zhang et al.	May 2013	A1
20130136127	Hill	May 2013	A1
20130163427	Beliveau et al.	Jun 2013	A1
20130163475	Beliveau et al.	Jun 2013	A1
20130294249	Lin et al.	Nov 2013	A1
20130332602	Nakil et al.	Dec 2013	A1
20130332983	Koorevaar et al.	Dec 2013	A1
20130339544	Mithyantha	Dec 2013	A1
20130346487	Tanimoto	Dec 2013	A1
20140019639	Ueno	Jan 2014	A1
20140029451	Nguyen	Jan 2014	A1
20140115578	Cooper et al.	Apr 2014	A1
20140119203	Sundaram et al.	May 2014	A1
20140126418	Brendel et al.	May 2014	A1
20140157405	Joll et al.	Jun 2014	A1
20140177633	Manula et al.	Jun 2014	A1
20140195666	Dumitru et al.	Jul 2014	A1
20140207926	Benny	Jul 2014	A1
20140219086	Cantu' et al.	Aug 2014	A1
20140281030	Cui et al.	Sep 2014	A1
20140282823	Rash et al.	Sep 2014	A1
20140297846	Hoja et al.	Oct 2014	A1
20140304393	Annamalaisami et al.	Oct 2014	A1
20140317313	Okita et al.	Oct 2014	A1
20140348184	Kure	Nov 2014	A1
20140351415	Harrigan et al.	Nov 2014	A1
20140380176	Arquie et al.	Dec 2014	A1
20150016286	Ganichev et al.	Jan 2015	A1
20150016287	Ganichev et al.	Jan 2015	A1
20150016298	Ganichev et al.	Jan 2015	A1
20150016460	Zhang	Jan 2015	A1
20150016469	Ganichev et al.	Jan 2015	A1
20150043378	Bardgett et al.	Feb 2015	A1
20150163330	Ni et al.	Jun 2015	A1
20150180755	Zhang et al.	Jun 2015	A1
20150195169	Liu et al.	Jul 2015	A1
20150263899	Tubaltsev et al.	Sep 2015	A1
20150271011	Neginhal et al.	Sep 2015	A1
20150281036	Sun et al.	Oct 2015	A1
20160105333	Lenglet et al.	Apr 2016	A1
20160119204	Murasato et al.	Apr 2016	A1
20160142291	Polland	May 2016	A1
20160149784	Zhang et al.	May 2016	A1
20160149791	Ganichev et al.	May 2016	A1
20160205174	Pitio et al.	Jul 2016	A1
20160226741	Ganichev et al.	Aug 2016	A1
20160226742	Apathotharanan et al.	Aug 2016	A1
20160274558	Strohmenger et al.	Sep 2016	A1
20160323166	Pandey et al.	Nov 2016	A1
20160359712	Attar et al.	Dec 2016	A1
20160359740	Parandehgheibi et al.	Dec 2016	A1
20160359880	Pang et al.	Dec 2016	A1
20170026270	Shankar	Jan 2017	A1
20170093677	Skerry et al.	Mar 2017	A1
20170126728	Beam et al.	May 2017	A1
20170171159	Kumar et al.	Jun 2017	A1
20170180423	Nimmagadda et al.	Jun 2017	A1
20170222881	Holbrook et al.	Aug 2017	A1
20170288991	Ganesh	Oct 2017	A1
20170302530	Wolting	Oct 2017	A1
20170310574	Wolting	Oct 2017	A1
20170317954	Masurekar et al.	Nov 2017	A1
20170324778	Register et al.	Nov 2017	A1
20170358111	Madsen	Dec 2017	A1
20170373950	Szilagyi et al.	Dec 2017	A1
20180041470	Schultz et al.	Feb 2018	A1
20180062939	Kulkarni et al.	Mar 2018	A1
20180063188	Karin et al.	Mar 2018	A1
20180091388	Levy et al.	Mar 2018	A1
20180102959	Ganichev et al.	Apr 2018	A1
20180113790	Chunduri	Apr 2018	A1
20180123903	Holla et al.	May 2018	A1
20180124171	Han et al.	May 2018	A1
20180136798	Aggour et al.	May 2018	A1
20180181754	Gunda	Jun 2018	A1
20180219751	Cavuto et al.	Aug 2018	A1
20180262447	Nhu et al.	Sep 2018	A1
20180262594	Nhu et al.	Sep 2018	A1
20180309637	Gill et al.	Oct 2018	A1
20180373961	Wang et al.	Dec 2018	A1
20180375728	Gangil et al.	Dec 2018	A1
20190014029	Burgio et al.	Jan 2019	A1
20190109769	Jain et al.	Apr 2019	A1
20190129738	Ekbote et al.	May 2019	A1
20190140931	Ganichev et al.	May 2019	A1
20190149525	Gunda et al.	May 2019	A1
20190158377	Chau	May 2019	A1
20190190804	Tang et al.	Jun 2019	A1
20190306060	Sharma	Oct 2019	A1
20200014663	Chen et al.	Jan 2020	A1
20200036636	Holla et al.	Jan 2020	A1
20200067799	Lenglet et al.	Feb 2020	A1
20200076734	Naveen et al.	Mar 2020	A1
20200106744	Miriyala et al.	Apr 2020	A1
20200136943	Banyai et al.	Apr 2020	A1
20200169475	Nagarkar et al.	May 2020	A1
20200169476	Vela et al.	May 2020	A1
20200186453	Zhang et al.	Jun 2020	A1
20200204457	Hu et al.	Jun 2020	A1
20200210195	Lampert et al.	Jul 2020	A1
20200304389	Bauan et al.	Sep 2020	A1
20200313985	Jayakumar et al.	Oct 2020	A1
20200322243	Xi et al.	Oct 2020	A1
20200322249	Liu et al.	Oct 2020	A1
20200336387	Suzuki et al.	Oct 2020	A1
20200342346	Wulff et al.	Oct 2020	A1
20210014157	Zhou	Jan 2021	A1
20210028996	Mordani et al.	Jan 2021	A1
20210029059	Nhu et al.	Jan 2021	A1
20210051100	Chitalia et al.	Feb 2021	A1
20210051109	Chitalia et al.	Feb 2021	A1
20210092064	Sidebottom et al.	Mar 2021	A1
20210194931	Parashar et al.	Jun 2021	A1
20210216908	Lu et al.	Jul 2021	A1
20210218630	Lu et al.	Jul 2021	A1
20210218652	Raut et al.	Jul 2021	A1
20210224183	Péan et al.	Jul 2021	A1
20210226875	Chen et al.	Jul 2021	A1
20210226880	Ramamoorthy et al.	Jul 2021	A1
20210226898	Chen et al.	Jul 2021	A1
20210266259	Renner, III et al.	Aug 2021	A1
20210311764	Rosoff et al.	Oct 2021	A1
20210328891	Cherkas	Oct 2021	A1
20210367927	Selvaraj et al.	Nov 2021	A1
20210377288	Kaidi	Dec 2021	A1
20220014451	Naik et al.	Jan 2022	A1
20220021616	Amal et al.	Jan 2022	A1
20220038368	Shen et al.	Feb 2022	A1
20220038501	Shen et al.	Feb 2022	A1
20220103452	Ganichev et al.	Mar 2022	A1
20220103460	Yu et al.	Mar 2022	A1
20220150136	Chen	May 2022	A1
20220155948	Chaudhari et al.	May 2022	A1
20220165035	Cui et al.	May 2022	A1
20220210120	Nigam et al.	Jun 2022	A1
20220217068	Chen et al.	Jul 2022	A1
20220224620	Chhabra et al.	Jul 2022	A1
20220263721	Bogado et al.	Aug 2022	A1
20230006886	Parashar et al.	Jan 2023	A1
20230023956	Gajjar et al.	Jan 2023	A1
20230039791	Paladugu et al.	Feb 2023	A1
20230052974	Thoria et al.	Feb 2023	A1
20230087143	Cheng et al.	Mar 2023	A1
20230087454	Cheng et al.	Mar 2023	A1
20230101764	Cheng et al.	Mar 2023	A1
20230123136	Deb et al.	Apr 2023	A1
20230198859	Parashar et al.	Jun 2023	A1
20230280880	Parashar et al.	Sep 2023	A1

Foreign Referenced Citations (10)

Number	Date	Country
1154601	Nov 2001	EP
1931084	Jun 2008	EP
2002141905	May 2002	JP
2003069609	Mar 2003	JP
2003124976	Apr 2003	JP
2003318949	Nov 2003	JP
9506989	Mar 1995	WO
2012126488	Sep 2012	WO
2013184846	Dec 2013	WO
2015005968	Jan 2015	WO

Non-Patent Literature Citations (4)

Entry
Levin, Anna, et al., “Network Monitoring in Federated Cloud Environment,” 2017 IEEE International Conference on Smart Computing, May 29-31, 2017, 6 pages, IEEE, Hong Kong, China.
Non-Published Commonly Owned International Patent Application PCT/CN2020/105533 with similar specification, filed Jul. 29, 2020, 70 pages, VMware, Inc.
Phaal, Peter, et al., “sFlow Version 5,” Jul. 2004, 46 pages, available at http://www.sflow.org/sflow_version_5.txt.
Phan, Doantam, et al., “Visual Analysis of Network Flow Data with Timelines and Event Plots,” VizSEC 2007, Month Unknown 2007, 16 pages.

Related Publications (1)

	Number	Date	Country
	20230179513 A1	Jun 2023	US

Continuations (1)

	Number	Date	Country
Parent	17006845	Aug 2020	US
Child	18102699		US

Flow tracing operation in container cluster

Information

Patent Number

Date Filed

Date Issued

Inventors

Original Assignees

Examiners

Agents

CPC

Field of Search

US

International Classifications

Disclaimer

Term Extension

Abstract