Scalable distributed end-to-end performance delay measurement for segment routing policies

Information

  • Patent Grant
  • 10230605
  • Patent Number
    10,230,605
  • Date Filed
    Tuesday, September 4, 2018
    5 years ago
  • Date Issued
    Tuesday, March 12, 2019
    5 years ago
Abstract
The present technology is directed to a scalable solution for end-to-end performance delay measurement for Segment Routing Policies on both SR-MPLS and SRv6 data planes. The scalability of the solution stems from the use of distributed PM sessions along SR Policy ECMP paths. This is achieved by dividing the SR policy into smaller sections comprised of SPT trees or sub-paths, each of which is associated with a Root-Node. Downstream SID List TLVs may be used in Probe query messages for signaling SPT information to the Root-Nodes Alternatively, this SPT signaling may be accomplished by using a centralized controller. Root-Nodes are responsible for dynamically creating PM sessions and measuring delay metrics for their associated SPT tree section. The root-nodes then send the delay metrics for their local section to an ingress PE node or to a centralized controller using delay metric TLV field of the response message.
Description
TECHNICAL FIELD

The present technology pertains to scalable performance measurement in computer networks. More specifically, it is directed to a scalable implementation of distributed delay measurement for Segment Routing Policies.


BACKGROUND

Segment-routing (SR) technology greatly simplifies network operations and is conducive to a Software-Defined Networking paradigm. Segment Routing may be utilized with both Multi-Protocol Label Switching (SR-MPLS) and Internet Protocol version 6 (SRv6) data-planes. Built-in Performance Measurement (PM) is one of the essential requirements for a successful implementation of this technology.


Segment Routing policies are used to steer traffic through a specific, user-defined path using one or more Segment Identifier (SID) list for Traffic Engineering (TE). In SR network, end-to-end performance delay on SR Policies must be closely measured and monitored in order to ensure that the provisions of Service Level Agreements (SLAs) are met. Service providers are expected to detect and correct delay bound violations for the services in sub-second interval for certain applications such as tele-medicine, on-line gaming, stock market trading and many mission critical applications. In the Segment Routing context, the provision of end-to-end low latency services with rapid performance degradation detection becomes an essential service requirement, especially when considering that Segment Routing based network Slicing may serve as a core technology for implementing 5G ready networks.





BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and other advantages and features of the disclosure can be obtained, a more particular description of the principles briefly described above will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. Understanding that these drawings depict only exemplary embodiments of the disclosure and are not therefore to be considered to be limiting of its scope, the principles herein are described and explained with additional specificity and detail through the use of the accompanying drawings in which:



FIG. 1 illustrates, an example Segment Routing topology for an end-to-end Segment Routing policy, in accordance to some embodiments of the present invention.



FIG. 2 illustrates a Segment Routing Policy with Root-Nodes and sub-path trees (SPT) in accordance to some embodiment of the present technology.



FIG. 3 illustrates an operation involving PM query generation for distributed creation of PM sessions required for implementing distributed performance delay measurement on an SR policy, in accordance to some embodiments of the present technology.



FIG. 4 illustrates an operation involving PM response message generation for distributed performance delay measurement on an SR policy, in accordance to some embodiments of the present technology.



FIG. 5 illustrates an operation involving a PM session set up and PM response processing with a centralized controller, in accordance to some embodiments of the present technology.



FIG. 6 illustrates an operation involving stitching of PM sessions on ingress Line Card for distributed performance delay measurement on SR policy, in accordance to some embodiments of the present technology.



FIG. 7 illustrates an operation involving stitching of PM sessions on different ingress Line Cards for distributed performance delay measurement on SR policy, in accordance to some embodiments of the present technology.



FIG. 8 illustrates an example SR Policy for end-to-end delay computation involving PM session stitching, in accordance to some embodiments of the present technology.



FIG. 9 illustrates an example SR Policy for end-to-end delay computation involving PM session stitching, in accordance to some embodiments of the present technology.





DESCRIPTION OF EXAMPLE EMBODIMENTS

Additional features and advantages of the disclosure will be set forth in the description which follows, and in part will be obvious from the description, or can be learned by practice of the herein disclosed principles. The features and advantages of the disclosure can be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features of the disclosure will become more fully apparent from the following description and appended claims, or can be learned by the practice of the principles set forth herein.


Overview


Systems and methods, are disclosed that provide a highly scalable implementation of delay performance measurement across Segment Routing Policies. Embodiment of the technology are directed to a method comprising a step of partitioning a segment routing policy domain, established between an ingress node and an egress node, into a plurality of sections, wherein each sections includes a Root-Node and one or more paths originating from the Root-Node and spanning the section. Disclosed embodiments further comprise a step of creating one or more local delay measurement sessions at each Root-Node, wherein each of the one or more local delay measurement sessions corresponds to a different path from the one or more paths originating from the Root-Node. Moreover embodiments may include an additional step of calculating one or more end to end delay metrics for the segment routing policy domain by utilizing the one or more local delay measurements from each of the Root-Nodes along the segment routing policy domain. In some embodiments the paths spanning each of the sections and the corresponding Root-Nodes are designated by one or more delay measurement query messages sent by the ingress node. Alternatively designation of the Root-Nodes and the corresponding SPT trees may be provided by a centralized controller entity. In some embodiments of the disclosed technology, end-to-end delay measurement carried out for a Segment Routing Policy also includes the internal switching fabric delay within each of the Root-Node router as well as any delay incurred on the input and output Line Cards of the Root-Node routers.


Example Embodiments

Disclosed are systems, methods, and non-transitory computer-readable storage media for scalable implementation of distributed Performance Delay Measurement for Segment Routing Policy Path. Various embodiments of the disclosure are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without parting from the spirit and scope of the disclosure.


Extended Traffic Engineering (TE) link delay metrics (i.e., minimum delay value) may be used as an optimization metric or an accumulated delay bound to compute paths for SR policies. The actual delay values of an SR policy may be very different than the path computation results (i.e., sum of TE link delay metrics) due to several factors, such as queuing in a router, etc. Therefore, there is a need to monitor the end-to-end delay experienced by the traffic sent over the SR Policy to ensure that the delay does not exceed the requested “upper-bound” and violate SLAs. Furthermore, accurate measurement of end-to-end delay values may be used as important indicators for key operations such as activating candidate-path or segment-list(s) of the SR Policy in forwarding plane. End-to-end delay values may also be used as indication to de-activate the active candidate-path or Segment List(s) of the SR policy in forwarding.



FIG. 1 illustrates an example Segment Routing network topology for implementing a Segment Routing (SR) Policy 100. The SR policy 100 is established between ingress Provider Edge (PE) Node 2 and egress PE Node 6, with ECMP paths on Nodes 2, 3, 4 and 5. In the example SR policy 100, there are 2 ECMPs for each one of the 4 hops to get from the ingress PE Node 2 (through Node 3, 4 and 5) to the egress PE Node 6, which provides (24=16) sixteen end-to-end forwarding paths. The alternative end-to-end path from ingress PE Node 2 to egress PE Node 6 consists of three single path hops (through Node 8, 9 and 5) and one dual path hop (Equal Cost Multiple Paths or ECMP) from Node 5 to egress PE Node 6, which provides (1*1*1*2=2) two end-to-end forwarding paths. Therefore SR topology 100 consists of a total of (16+2=18) eighteen end-to-end forwarding paths that may be used by traffic flows when directed onto this SR Policy.


With reference to SR policy 100, the delay experienced by the traffic flow on each forwarding path may be different. Hence, Performance Measurement (PM) process needs to create 18 different delay measurement sessions on Ingress PE node 2. This results in a scale issue, as PM packets are injected by the control plane on ingress node and punted to the control plane on egress node for processing (although timestamp is provided in hardware). In a large network, there can be up to 128 Equal Cost Multi Paths (ECMP) between two nodes of an SR Policy. This can rapidly lead to a scaling problem as the number of end-to-end paths scales exponentially with the number of hops (‘n’ hops with 128 ECMP per hop=128n end-to-end forwarding paths). The result may be a large number of end-to-end forwarding paths requiring very large number of Performance Measurement (PM) sessions for calculating end-to-end delay performance for all of the forwarding paths. Furthermore, due to policing mechanism such Local Packet Transport Service (LPTS) policer and platform-related Packets Per Second (PPS) rate limits for punting packets in hardware (e.g. 5000 PPS on ASR9K LC), an SR node can only send Performance Measurement (PM) probe packets, for delay/jitter measurements, every few minutes for a certain forwarding path. This may results in very slow detection of SLA degradation. The excessive amount of packet processing required may also result in excessive CPU usage in control-plane.


Performance measurement is about collecting statistical data for minimum, maximum and average delay metrics. IP/UDP based probing may be a potential option for collecting relevant delay metrics for an SR Policy. IP/UDP header source-port/source-address/destination-address can be used to take advantage of the hashing function in forwarding for ECMP paths. However, due to different hashing functions on each node along the SR Policy path, the actual end-to-end forwarding path of the probe packet, for which a delay value is measured, cannot be easily identified. This may limit the corrective action to the SR Policy candidate-path level only. Moreover, this approach does not provide an scalable solution for multi-hop SR Policy with many ECMP paths on each hop (i.e., 32*32*32 total end-to-end ECMP paths). Assuming a probe interval of 10 seconds to measure delay of each ECMP path of the SR Policy (by sending say 10 Probe query messages for it to measure min/max/average delay/jitter), it would take (32*32*32*10/60/60/24=) 3.8 days to measure the delay metric of the SR Policy



FIG. 2 illustrates an exemplary SR policy 200 configured for distributed delay PM measurements, in accordance to some embodiments of the present technology. In the SR policy 200, the ingress PE (Node 2) breaks down the SR Policy into multiple smaller ECMP sub-paths (corresponding to Shortest Path Tree (SPT) constructions) called ECMP Sections. This is achieved, in accordance to the example embodiment of FIG. 2, by designating one or more Root-Nodes and the corresponding SPT trees for each Root-Node in order to distribute the PM delay measurements for the SR Policy. Considering the exponential increase in the number of forwarding paths as a function of hop count, it may be expected that breaking down an SR Policy into 2 or 3 sections (i.e., sub-paths or SPT trees) would be sufficient to significantly reduce the number of forwarding paths and hence the required PM sessions. For example, partitioning a SR path/policy that is 4 hops long and has 32 available ECMPs per hop into two equal sections reduces the number of forwarding paths from (324=) 1048576 paths to (322+322=) 2048 paths.


PM delay measurement query message may include one or more newly defined downstream SID List (D-SID) TLVs in order to identify the SPT tree on a Root-Node. PM probe query packets for session setup are punted on all ingress line cards on Root-Nodes of the SR Policy. Root-Node of each section (sub-path SPT tree) creates PM sessions dynamically for PM delay measurement using the information from the received PM query message. The Root-Node is responsible for delay measurement for its local section and collecting the delay metrics for downstream sections at the same time.


PM probe response packets may contains the newly defined delay metric TLV for each section (sub-path SPT tree) and is sent by the Root-Node to the previous Root-Node which can add its local delay metric TLV therein before sending it upstream. Root-Nodes may alternatively send delay metric TLV directly to the ingress PE node.


The ingress node may build an entire end-to-end forwarding path SPT tree of the SR Policy (using adjacency SIDs) and assign unique Path-ID values to each forwarding path and send this path information in the downstream SID List TLVs to each Root-Node along the SR Policy. By using Path-ID information a Root-Node may separately notify the delay metric of each ECMP path of the local segment. Path-ID also allows a Root-Node that does the aggregation of the delay metrics for all ECMP paths to separately notify the delay metric of the segment when there are more than one downstream Root-Nodes, i.e. Path-ID can identify parts of the SPT tree terminating at a specific downstream Root-Node. However, if a Root-Node has only one other adjacent Root-Node, the ingress Node may simply send the Root-Node, the node/prefix SID of its adjacent Root-Node so that the Root-Node can then compute ECMP SPT tree to its neighboring adjacent Root-Node and create PM sessions for all the equal cost paths dynamically.


Referring back to the example SR topology 200. The ingress PE node (node 2) breaks down the SR Policy 200 into 4 sections, with Nodes 2, 3, 4 and 8 designated as the Root-Nodes for Section-number 1, Section-number 2, Section-number 3 and Section-number 4, respectively. Root-Node 3 in SR topology 200 is adjacent only to downstream Root-Node 4. Therefore the ingress Node 2 may simply send the node/prefix SID of the Root-Node 4 to Root-Node 3. This will enable Root-Node 3 to compute ECMP SPT tree to Root-Node 4 and dynamically create PM sessions for all the equal cost paths. In this case, Path-ID is not used.


Turning back to FIG. 2, the ingress PE node (node 2) creates 3 PM sessions corresponding to its three local ECMP paths terminating at its adjacent Root-Nodes (Node 3 and Node 8). Subsequently, the ingress PE sends out PM query messages to the next Root-Nodes on the SR Policy path (i.e., Node 3, and Node 8 as illustrated in FIG. 2). The PM query message sent to Node 3 may contain relevant downstream SID list (D-SID) TLVs. In case of Root-Node 3 there will be two downstream SID List TLVs, one TLV (i.e., TLV 1) may provide information with regards to available forwarding paths through Section-number 2 and the other TLV (i.e., TLV2) may provide information with regards to available forwarding paths through Section-number 3.


For example TLV1 may comprise:

    • <Section-number2, Path-ID10, 16003, 24134>
    • <Section-number2, Path-ID20, 16003, 24234>


While TLV2 may comprise:

    • <Section-number3, Path-ID100, 16004, 24145, 24156>
    • <Section-number3, Path-ID200, 16004, 24245, 24156>
    • <Section-number3, Path-ID300, 16004, 24145, 24256>
    • <Section-number3, Path-ID400, 16004, 24245, 24256>


In case of Root-Node 8, the PM query message sent by ingress PE Node 2 to Root-Node 8 may comprise of one downstream SID List TLV, (i.e., TLV 3) to provide information with regards to available forwarding paths through Section-number 4.


For example TLV3 may comprise:

    • <Section-number4, Path-ID50, 16008, 16009, 16005, 24156>
    • <Section-number4, Path-ID60, 16008, 16009, 16005, 24256>


Upon receiving the PM query message with downstream SID List TLV, Root-Node 3 identifies itself as a Root-Node based on recognizing the top SID (16003 in TLV 1) as its own prefix-SID. Subsequently Root-node 3 dynamically creates the corresponding PM sessions for the top TLV (TLV1). In this case, Root-Node 3 creates 2 different PM sessions corresponding to the two different paths traversing Section-number 2 (reported in TLV1) and computes a corresponding delay metrics for each of them. PM response generated by Root-Node 3 in response to PM query message containing downstream SID List TLV 1 may comprise:

    • <Section-number2, Path-ID10, 3, 34>
    • <Section-number2, Path-ID20, 3, 34′>


Root-Node 3 then removes the TLV (i.e. TLV1) for which it created the sessions from the received PM query message from ingress Node 2 and forwards the query to the downstream Root-Node 4.


Upon receiving the PM query message with downstream SID List TLV, Root-Node 4 identifies itself as a Root-Node based on recognizing the top SID (16004 in TLV2) as its own prefix-SID. Root-node 4 then dynamically creates the corresponding PM sessions for the top TLV paths (TLV2 paths). In this example, Root-Node 4 creates 4 different PM sessions corresponding to the four different paths traversing Section-number 3 (reported in TLV2 as Path-ID 100, 200, 300 and 400) and computes a corresponding delay metrics for each of them. In this example, PM response generated by Root-Node 4 in response to PM query message containing downstream SID List TLV 2 may comprise:

    • <Section-number3, Path-ID100, 16004, 24145, 24156>
    • <Section-number3, Path-ID200, 16004, 24245′, 24156>
    • <Section-number3, Path-ID300, 16004, 24145, 24256′>
    • <Section-number3, Path-ID400, 16004, 24245′, 24256′>


Root-Node 4 then removes the TLV (i.e. TLV2) for which it created the sessions from the received PM query message, and forwards the query downstream. Root-Node 6 will receive the PM query message and send PM response message back to the ingress PE Node 2, completing the setup.


As described above, in order to carry out the PM measurement, Root-Nodes dynamically creates local PM sessions for all locally originating ECMP paths in response to received PM probe query messages. The Root-Node then starts running probes along these paths in order to measure delay metrics for the said paths. As such, each Root-Node independently measures the PM delay values for each section (sub-path) including all its ECMP paths.



FIG. 3 illustrates an exemplary method, according to some embodiments, for PM query generation and subsequent PM session creation in response to, for example, the generated PM queries. This is illustrated in the context of an exemplary SR topology 300. Turning back to FIG. 3, a PM probe query 304 is generated by the Ingress PE Node (Node 2) and sent to neighboring Root-Node (Node 3). PM query message 304, destined for Node 3, contains sub-path information for Section-number 2 (in TLV1) and Section-number 3 (in TLV2). In response to PM query message 304, Node 3 generates a PM response message 306 containing the delay metrics (d3) for its local Section (sub-path/SPT tree). Node 3 then removes TLV 1 from the header of the Query message 304, which is associated with its local section, and forwards the PM query message (308) containing sub-path information for Section-number 3 (in TLV2) to Node 4. Upon identifying itself as the Root-Node (by verifying the top SID in the downstream SID list in TLV2), Node 4 sends a PM response message 310 with its delay metrics (d2) back to Node 3. Node 4 then removes TLV 2 from the PM query message 308, and forwards a PM query (312) to Node 6. Subsequently Node 6, upon receiving PM query 312 generates a response 314 with its delay metric (d1) and send it to Node 4.


In some embodiment, Root-node may send the downstream measured delay (timestamp t2 minus timestamp t1 i.e. PM response 306) in the upstream probe response message 306 (i.e. for each ECMP path separately using their Path-IDs). In some embodiments Root-node may optionally aggregate the delay metrics for all ECMP paths of the Section SPT tree before sending it upstream to reduce the processing load on the CPU of the ingress PE node. The ingress node and Root-Nodes send the subsequent probe query messages without downstream SID list TLVs.


In some embodiment PM query response may be generated on-demand. In on-demand mode, Root-Node may only respond when a PM probe query message is received. In response to receiving a PM query message, Root-node may send, in its PM probe response, the delay metric of the local Section as well as for all the downstream sections from where it may have received corresponding PM response messages with delay metrics. A Root-Node may locally store delay metrics TLV it has received from downstream Root-Nodes, until it receives a probe query packet from an upstream Root-Node.



FIG. 4 illustrates an exemplary method, according to some embodiments, for PM response generation provided in response to, for example, upstream PM query messages. This is illustrated in FIG. 4 in the context of an exemplary SR topology 400. Turning back to FIG. 4, PM sessions running on Root-Node 3 generates delay measurement (d2) for its local section 2 (Section-number 2) while also collecting delay metric (d1) for section 3 (Section-number3) sent via PM response message 406 from Root-Node 4. Similarly, PM session running on Node 2, does the delay measurement (d3) for its local Section (Section-number1) while also collecting the delay metrics (d2) for Section-number 2 and delay metric (d1) for Section-number 3. Delay metrics d1 and d2 are provided by the PM response message 408 sent from Root-Node 3 (in the same probe response message) as shown in the FIG. 4.


Some embodiments of the present invention are directed to an unsolicited mode of operation. In the unsolicited mode, A Root-Node may send PM probe response message directly to the ingress node only in response to a delay metric crosses a threshold, (i.e., without receiving any probe query message first). This operation mode is conducive to rapid detection of Service Level Agreement violations.


The delay metric for a SR Policy may be computed by adding the delay metrics of all the Sections (SPT trees) of the SR Policy, which may have been collected from all the consecutive Root-Nodes along the path. In an SR Policy configured for distributed delay performance measurement, the ingress PE Node may be aware of the SPT paths of the various Sections as well as the corresponding Root-Nodes. Therefore, the ingress PE Node can correctly add the delay metrics of different Sections to produce end-to-end delay measurement for different SR paths across several ECMP sections (e.g. by adding delay metrics of Section 1, 2 and 3 or by adding delay metrics of Section 1 and 4—with reference to the SR topology illustration in FIG. 1).


The delay metrics of a Section includes the delay across all the relevant links and the internal fabric delay within the routers, as well as both ingress and egress Line Card delays. Using the delay values from each PM probe response packet (which includes delay values for all downstream SPT tress), an ingress PE Node can compute different metrics (i.e., minimum, maximum, average, variance, etc.) for the end-to-end delay parameter. This is illustrated by element 410 in FIG. 4.


With reference to some of the described embodiments, any platform related minor measurement errors on the processing side of the ingress Root-Nodes will be in the order of nanoseconds. Accordingly, embodiments of the present technology allow for scaling up of the delay metrics measurement to accommodate large number of ECMP paths of SR Policy which cannot otherwise be supported by currently existing schemes.


As described above, Root-Nodes dynamically creates PM sessions in response to received PM probe query message(s). In some embodiments, if there is already a PM session present on the Root-Node due to a request for another SR Policy, the Root-Node may re-uses the existing PM session instead of creating a new PM session. This can help significantly reduce the number of PM sessions in the network thereby further improving the scalability of the technology.


According to some embodiment of the present technology, in case of a significant change in a delay metric of a Section, the ingress node of SR policy may be notified quickly with an unsolicited message that can be used to trigger a faster protection switch-over. Furthermore, If delay metric(s) associated with a Section or combination of several Sections exceed a delay bound requested by the SR Policy, the ingress node may immediately invalidate the corresponding segment-list of the SR Policy. This also allows to keep the “link” delay metric threshold value higher so as to avoid excessive Interior Gateway Protocol (IGP) flooding of the link delay metrics in the network. This may be advantageous over using path computation cost which is computed by adding the hop-by-hop link delay metrics from the topology database.


The method proposed, in accordance to some embodiments, may be used to monitor selective Section(s) of an SR Policy that may be more likely to experience performance delay degradation (for example, due to congestion or underlying optical network issues). This obviates a need for the ingress Node to monitor all ECMP paths of the SR Policy and allows the ingress Node to only request a specific Root-Node to measure performance delay metric of its local Section.


In some embodiments of the present technology a centralized controller may be used, instead of PM probe query messages, in order to identify and set up Root-Nodes and Sections with SPT trees for the purpose of implementing a distributed scalable PM delay measurement for an SR Policy. An exemplary centralized controller based distributed delay measurement system 500 is illustrated in FIG. 5. In order to implement distributed delay measurement for an SR Policy, the centralized controller 504 associated with the exemplary system 500 in FIG. 5 identifies and configures Root Nodes with corresponding sub-path SPT trees. Upon being established and subsequently probed by the controller 500, Root-Nodes may send the delay metrics associated with their respective local Sections (sub-path) via PM response messages to the centralized controller 504. The centralized controller 504 may then aggregates the delay metrics from different Sections of the SR Policy to compute one or more end-end delay metrics for the SR Policy. According to some embodiments, Root-Nodes may send the requested metrics to a centralized controller as a message generated in response to a probe query message received from the ingress PE Node. Alternatively, the metric transmission from Root-Nodes to centralized controller may be unsolicited, triggered instead by other external/internal factors (i.e., when a specified threshold is crossed.)


According to some embodiments, distributed performance delay measurement may be implemented as Node-SID (Prefix-SID) based performance delay measurement. In this variation, each node in the network measures performance delay metrics based on all ECMP forwarding paths to the next-hop (or next several hops) Node-SIDs (Prefix-SIDs). This delay measurement may then be used for detecting performance delay degradation between any two nodes in the network.


As described above, the delay metrics of a Section includes delay of all links as well as internal fabric delay within the routers, in addition to the ingress and egress Line Card delays. In order to ensure accuracy for end-to-end delay measurement, root-node does stitching of PM sessions to be able to get punt time-stamp and inject time-stamp as close as possible. The stitching of PM session on the ingress Line Card, in accordance to some embodiment of the present technology, will be described in reference to FIG. 6.


Turning now to FIG. 6, the transmit (TX) timestamp (t1) on the PM query is added by the ingress Line Card on the querier Node (Node generating a probe or PM query message). This means the fabric queue delay and egress Line Card (outgoing) queue processing delay are accounted for in determination of timestamp (t1). The receive (RX) timestamp (t2) is added in the probe query packet on the ingress Line Card on the responder node after the packet has undergone ingress queuing. This means that ingress (incoming) queue processing delay is accounted for in determination of timestamp (t2).


Turning back to FIG. 6, the Line Card on a Root-Node where the received PM probe query packet is punted (612) with Receive (RX) timestamp (t2), injects the PM query packet back (614) on the same Line Card to get the Transmit (TX) timestamp (t1) for the downstream Segment, as shown in the FIG. 6 (this phenomena is referred to as stitching).


Some embodiments involve stitching of PM sessions on different ingress Line Cards. An illustrative example is provided in FIG. 7. In order to ensure accuracy for end-to-end delay measurement, root-node does stitching of PM sessions to be able to get punt time-stamp and inject time-stamp as close as possible.


Root-nodes need to account for the queuing delay between the ingress LC towards the fabric and to the egress LC. In other words, delay measurement will include all fabric congestion between any two Line Cards on the path of the SR Policy. This is because PM probe packets will traverse these paths. For this, Root-Nodes needs to create PM sessions starting from each ingress Line Card on the node where incoming PM sessions are terminated. For example, if there are N number of ECMP paths in the Section on Root-Node, and there are M ingress Line-Cards where incoming PM sessions are terminated, then Root-Nodes need to create N*M number of PM sessions. This prescription for the number of required PM sessions may be alleviated as Root-Nodes are likely to re-use the PM sessions on the ECMP paths in its Section across multiple SR Policies (sharing the forwarding paths).


An example of End-to-end computation stitching is provided in FIG. 8. The exemplary SR policy 800, illustrated in FIG. 8, is implemented on nodes A-B-C. There are 2 ECMP paths (801 and 802) between Nodes A and B and there is one ECMP path (803) between Nodes B and C. B1 and B2 represent ingress Line Cards on Root-Node B while B3 represents the egress Line Card on Root-Node B. As illustrated in FIG. 8, Node B creates 2 PM sessions (804, 806) as there are two ingress Line cards B1 and B2 where PM session stitching is required. As indicated by PM session 804 in FIG. 8, the performance delay associated with the Forwarding path A-B1-B3-C1 (includes delay from Line Card B1 to Fabric queue on Node B) is equal to (2+5=) 7 μsec. Similarly, as indicated by PM session 806, the performance delay associated with the Forwarding path A-B2-B3-C1 (includes delay from Line Card B2 to Fabric queue on Node B) is equal to (4+5=) 9 μsec.


Another example pertaining to SR Policy/Topology 900 is illustrated in FIG. 9. The example SR Policy/Topology 900 of FIG. 9 is similarly implemented on nodes A-B-C. There are 2 ECMP paths 901 and 902 between Nodes A and B and there are 2 ECMP paths 903 and 904 between nodes B and C. B1 and B2 are ingress Line Cards and B3 and B4 are egress Line Cards on Root-Node B. C1 and C2 are ingress Line Cards on node C. Node B creates 4 PM sessions as there are two ingress Line cards B1 and B2 where PM session stitching is required and there are 2 ECMP paths in its section. As indicated by 906 and 908 in FIG. 9, the performance delay associated with the Forwarding path A-B1-B3-C1 and A-B2-B3-C1 is equal to (3+5=) 8 μsec and (4+6=) 10 μsec, respectively. Similarly, as indicated by 910 and 912, the performance delay associated with the Forwarding path A-B1-B4-C2 and A-B2-B4-C2 is equal to (5+3=) 8 μsec and (4+6=) 10 μsec, respectively.


In 5G network with network slicing, there is a need to measure performance delay metrics of SR Policies for different traffic types using QoS fields (e.g. EXP/DSCP in IPv4/IPv6 header). As such, Some embodiment of the present technology provide a Quality of Service (QoS) aware delay measurement system. PM probes can be used with a requested QoS field (EXP/DSCP) to measure the performance delay values for the corresponding traffic. In some embodiments QoS field in the PM probe packets may be used to enable forwarding/sending probe packets to certain forwarding/hardware queues along the path. Some forwarding paths may be used for PM probe query messages with certain QoS field. Accordingly, QoS aware delay measurement, in accordance to some embodiments, may be used to steer the corresponding traffic away from the SR Policy when there is a degradation.


It should be noted that a SR policy may be created for SR-MPLS or SRv6 data-planes and although, PM probes packets may be shown with SR-MPLS encoding, it would be apparent to a Person of Ordinary Skill in the Arts that the solution applies equally to SRv6 data-plane, for which PM probe packets use IP/UDP packet encoding.


For clarity of explanation, in some instances the present technology may be presented as including individual functional blocks including functional blocks comprising devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software.


In some embodiments the computer-readable storage devices, mediums, and memories can include a cable or wireless signal containing a bit stream and the like. However, when mentioned, non-transitory computer-readable storage media expressly exclude media such as energy, carrier signals, electromagnetic waves, and signals per se.


Methods according to the above-described examples can be implemented using computer-executable instructions that are stored or otherwise available from computer readable media. Such instructions can comprise, for example, instructions and data which cause or otherwise configure a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Portions of computer resources used can be accessible over a network. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, firmware, or source code. Examples of computer-readable media that may be used to store instructions, information used, and/or information created during methods according to described examples include magnetic or optical disks, flash memory, USB devices provided with non-volatile memory, networked storage devices, and so on.


Devices implementing methods according to these disclosures can comprise hardware, firmware and/or software, and can take any of a variety of form factors. Typical examples of such form factors include laptops, smart phones, small form factor personal computers, personal digital assistants, and so on. Functionality described herein also can be embodied in peripherals or add-in cards. Such functionality can also be implemented on a circuit board among different chips or different processes executing in a single device, by way of further example.


The instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are means for providing the functions described in these disclosures.


Although a variety of examples and other information was used to explain aspects within the scope of the appended claims, no limitation of the claims should be implied based on particular features or arrangements in such examples, as one of ordinary skill would be able to use these examples to derive a wide variety of implementations. Further and although some subject matter may have been described in language specific to examples of structural features and/or method steps, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to these described features or acts. For example, such functionality can be distributed differently or performed in components other than those identified herein. Rather, the described features and steps are disclosed as examples of components of systems and methods within the scope of the appended claims.

Claims
  • 1. A computer-implemented method for implementing distributed delay measurement comprising: dividing a segment routing policy domain, established between an ingress node and an egress node, into a plurality of sections, wherein each section comprises a Root-Node and one or more paths originating from the Root-Node and spanning the section;creating one or more local delay measurement sessions at each Root-Node, wherein each of the one or more local delay measurement sessions corresponds to a different path from the one or more paths originating from the Root-Node; andcalculating one or more end to end delay metrics for the segment routing policy domain by utilizing the one or more local delay measurements from each of the Root-Nodes along the segment routing policy domain.
  • 2. The computer-implemented method of claim 1, wherein the one or more end to end delay metrics are associated with one or more end-to-end paths between the ingress node and the egress node.
  • 3. The computer-implemented method of claim 1, wherein the sections, the paths spanning each of the sections and the corresponding Root-Nodes are determined by one or more delay measurement query messages sent by the ingress node.
  • 4. The computer-implemented method of claim 3, wherein an extended link delay metric is used in the one or more delay measurement query messages sent by the ingress node.
  • 5. The computer-implemented method of claim 4, wherein the extended link delay metric provides information that identifies the Root-Node for each downstream section and delay metrics for each path spanning the corresponding downstream section.
  • 6. The computer-implemented method of claim 5, wherein the delay measurement query messages comprising the extended link delay metric are sent, by the ingress node, to one or more adjacent Root-Nodes and forwarded thereon to each of one or more downstream Root-Nodes.
  • 7. The computer-implemented method of claim 1, wherein a centralized controller is used to setup the sections, Root-Nodes and the one or more paths spanning each of the sections in the segment routing policy domain.
  • 8. The computer-implemented method of claim 1, wherein the one or more local delay measurements at each Root-Nodes include an internal fabric delay within the Root-Node.
  • 9. The computer-implemented method of claim 1, wherein the one or more local delay measurements at each of the Root-Nodes include one or more input/output line card delays.
  • 10. The computer-implemented method of claim 1, wherein the local delay measurement sessions are created at select Root-Nodes, such that the distributed delay performance measurement is carried out only for select Sections along the segment routing policy domain.
  • 11. A non-transitory computer-readable storage medium comprising instructions stored therein, which when executed by one or more processors, cause the processors to perform operations comprising: partition a segment routing policy domain, established between an ingress node and an egress node, into a plurality of Sections, wherein each section comprises a Root-Node and one or more paths originating from the Root-Node and spanning the Section;create one or more local delay measurement sessions at each Root-Node, wherein each of the one or more local delay measurement sessions corresponds to a different path from the one or more paths originating from the Root-Node and spanning the Section; andcalculate one or more end to end delay metrics for the segment routing policy domain by utilizing the one or more local delay measurements from each of the Root-Nodes along the segment routing policy domain.
  • 12. The one or more non-transitory computer-readable medium of claim 11, wherein the one or more end to end delay metrics are associated with one or more end-to-end paths between the ingress node and the egress node.
  • 13. The one or more non-transitory computer-readable medium of claim 11, wherein the Sections, the paths spanning each of the Sections and the corresponding Root-Nodes are determined by one or more delay measurement query messages sent by the ingress node.
  • 14. The one or more non-transitory computer-readable medium of claim 13, wherein an extended link delay metric is used in the one or more delay measurement query messages sent by the ingress node.
  • 15. The one or more non-transitory computer-readable medium of claim 14, wherein the extended link delay metric provides information that identifies the Root-Node for each downstream section and delay metrics for each path spanning the corresponding downstream section.
  • 16. The one or more non-transitory computer-readable medium of claim 15, wherein the one or more delay measurement query messages are sent, by the ingress node, to one or more adjacent Root-Nodes and forwarded thereon to each of one or more downstream Root-Nodes.
  • 17. The one or more non-transitory computer-readable medium of claim 11, wherein a centralized controller is used to setup the sections, Root-Nodes and the one or more paths spanning each of the sections in the segment routing policy domain.
  • 18. The one or more non-transitory computer-readable medium of claim 11, wherein the one or more local delay measurements sessions at each Root-Node include an internal fabric delay within the Root-Node.
  • 19. The one or more non-transitory computer-readable medium of claim 11, wherein the one or more local delay measurements sessions at each Root-Node include one or more input/output line card delays.
  • 20. A system comprising: a controller unit in communication with one or more segment routing capable nodes associated with a segment routing policy processors;a communication interface disposed between the controller unit and the one or more segment routing capable nodes, wherein the communication interface facilitate communication between the controller unit and the one or more segment routing capable nodes, wherein the controller unit, acting through the communication interface, designates one or more segment routing capable nodes as Root-Nodes, whereinthe controller unit initiate one or more Root-Nodes to establish one or more local delay measurement sessions, whereineach of the one or more local delay measurement sessions corresponds to a different path established between the Root-Node and one or more adjacent downstream Root-Nodes, whereinthe controller unit calculates one or more end to end delay metrics for the segment routing policy domain by utilizing the one or more local delay measurements from each of the Root-Nodes along the segment routing policy domain.
US Referenced Citations (187)
Number Name Date Kind
4236068 Walton Nov 1980 A
5642303 Small et al. Jun 1997 A
5751223 Turner May 1998 A
6812824 Goldinger et al. Nov 2004 B1
D552603 Tierney Oct 2007 S
7573862 Chambers et al. Aug 2009 B2
D637569 Desai et al. May 2011 S
7975262 Cozmei Jul 2011 B2
8010079 Mia et al. Aug 2011 B2
8102814 Rahman et al. Jan 2012 B2
8260320 Herz Sep 2012 B2
8284748 Borghei Oct 2012 B2
8300594 Bernier et al. Oct 2012 B1
8325626 Tóth et al. Dec 2012 B2
8396485 Grainger et al. Mar 2013 B2
8446899 Lei et al. May 2013 B2
8457145 Zimmerman et al. Jun 2013 B2
8458184 Dorogusker et al. Jun 2013 B2
D691636 Bunton Oct 2013 S
8549638 Aziz Oct 2013 B2
8553634 Chun et al. Oct 2013 B2
8644301 Tamhankar et al. Feb 2014 B2
8650279 Mehta et al. Feb 2014 B2
8669902 Pandey et al. Mar 2014 B2
8676182 Bell et al. Mar 2014 B2
8682279 Rudolf et al. Mar 2014 B2
8693367 Chowdhury et al. Apr 2014 B2
8718644 Thomas et al. May 2014 B2
8761174 Jing et al. Jun 2014 B2
8768389 Nenner et al. Jul 2014 B2
8849283 Rudolf et al. Sep 2014 B2
8909698 Parmar et al. Dec 2014 B2
8958318 Hastwell et al. Feb 2015 B1
9060352 Chan et al. Jun 2015 B2
9130859 Knappe Sep 2015 B1
9173084 Foskett Oct 2015 B1
9173158 Varma Oct 2015 B2
D744464 Snyder et al. Dec 2015 S
9270709 Shatzkamer et al. Feb 2016 B2
9271216 Friman et al. Feb 2016 B2
9281955 Moreno et al. Mar 2016 B2
D757424 Phillips et al. May 2016 S
D759639 Moon et al. Jun 2016 S
9369387 Filsfils et al. Jun 2016 B2
9389992 Gataullin et al. Jul 2016 B2
9426305 De Foy et al. Aug 2016 B2
D767548 Snyder et al. Sep 2016 S
9467918 Kwan Oct 2016 B1
D776634 Lee et al. Jan 2017 S
9544337 Eswara et al. Jan 2017 B2
9569771 Lesavich et al. Feb 2017 B2
9609504 Karlqvist et al. Mar 2017 B2
9615268 Navarro et al. Apr 2017 B2
9634952 Gopinathan et al. Apr 2017 B2
9642167 Snyder et al. May 2017 B1
9654344 Chan et al. May 2017 B2
9712444 Bolshinsky et al. Jul 2017 B1
9713114 Yu Jul 2017 B2
9736056 Vasseur Aug 2017 B2
9762683 Karampurwala et al. Sep 2017 B2
9772927 Gounares et al. Sep 2017 B2
9820105 Snyder et al. Nov 2017 B2
D804450 Spiel et al. Dec 2017 S
9858559 Raleigh et al. Jan 2018 B2
9860151 Ganichev et al. Jan 2018 B2
9933224 Dumitriu et al. Feb 2018 B2
9923780 Rao et al. Mar 2018 B2
9961560 Farkas et al. May 2018 B2
9967906 Verkaik et al. May 2018 B2
9980220 Snyder et al. May 2018 B2
9985837 Rao et al. May 2018 B2
9998368 Chen et al. Jun 2018 B2
20030087645 Kim et al. May 2003 A1
20030116634 Tanaka Jun 2003 A1
20040203572 Aerrabotu et al. Oct 2004 A1
20050090225 Muehleisen et al. Apr 2005 A1
20050169193 Black et al. Aug 2005 A1
20050186904 Kowalski et al. Aug 2005 A1
20060022815 Fischer et al. Feb 2006 A1
20060030290 Rudolf et al. Feb 2006 A1
20060092964 Park et al. May 2006 A1
20060126882 Deng et al. Jun 2006 A1
20060187866 Werb et al. Aug 2006 A1
20070037605 Logan Feb 2007 A1
20070239854 Janakiraman et al. Oct 2007 A1
20080037715 Prozeniuk et al. Feb 2008 A1
20080084888 Yadav et al. Apr 2008 A1
20080101381 Sun et al. May 2008 A1
20080163207 Reumann et al. Jul 2008 A1
20080233969 Mergen Sep 2008 A1
20090129389 Halna DeFretay et al. May 2009 A1
20090203370 Giles et al. Aug 2009 A1
20090282048 Ransom et al. Nov 2009 A1
20090298511 Paulson Dec 2009 A1
20090307485 Weniger et al. Dec 2009 A1
20100039280 Holm et al. Feb 2010 A1
20100097969 De Kimpe et al. Apr 2010 A1
20110087799 Padhye et al. Apr 2011 A1
20110142053 Van Der Merwe et al. Jun 2011 A1
20110182295 Singh et al. Jul 2011 A1
20110194553 Sahin et al. Aug 2011 A1
20110228779 Goergen Sep 2011 A1
20120023552 Brown et al. Jan 2012 A1
20120054367 Ramakrishnan et al. Mar 2012 A1
20120088476 Greenfield Apr 2012 A1
20120115512 Grainger et al. May 2012 A1
20120157126 Rekimoto Jun 2012 A1
20120167207 Beckley et al. Jun 2012 A1
20120182147 Forster Jul 2012 A1
20120311127 Kandula et al. Dec 2012 A1
20120324035 Cantu et al. Dec 2012 A1
20130029685 Moshfeghi Jan 2013 A1
20130039391 Skarp Feb 2013 A1
20130057435 Kim Mar 2013 A1
20130077612 Khorami Mar 2013 A1
20130088983 Pragada et al. Apr 2013 A1
20130107853 Pettus et al. May 2013 A1
20130108263 Srinivas et al. May 2013 A1
20130115916 Herz May 2013 A1
20130145008 Kannan et al. Jun 2013 A1
20130155906 Nachum et al. Jun 2013 A1
20130191567 Rofougaran et al. Jul 2013 A1
20130203445 Grainger et al. Aug 2013 A1
20130217332 Altman et al. Aug 2013 A1
20130232433 Krajec et al. Sep 2013 A1
20130273938 Ng et al. Oct 2013 A1
20130317944 Huang et al. Nov 2013 A1
20130322438 Gospodarek et al. Dec 2013 A1
20130343198 Chhabra et al. Dec 2013 A1
20130347103 Veteikis et al. Dec 2013 A1
20140007089 Bosch et al. Jan 2014 A1
20140016926 Soto et al. Jan 2014 A1
20140025770 Warfield et al. Jan 2014 A1
20140052508 Pandey et al. Feb 2014 A1
20140059655 Beckley et al. Feb 2014 A1
20140087693 Walby et al. Mar 2014 A1
20140105213 A K et al. Apr 2014 A1
20140118113 Kaushik et al. May 2014 A1
20140148196 Bassan-Eskenazi et al. May 2014 A1
20140179352 V.M. et al. Jun 2014 A1
20140191868 Ortiz et al. Jul 2014 A1
20140198808 Zhou Jul 2014 A1
20140222997 Mermoud et al. Aug 2014 A1
20140233460 Pettus et al. Aug 2014 A1
20140269321 Kamble et al. Sep 2014 A1
20140302869 Rosenbaum et al. Oct 2014 A1
20140337824 St. John et al. Nov 2014 A1
20140341568 Zhang et al. Nov 2014 A1
20150016286 Ganichev et al. Jan 2015 A1
20150016469 Ganichev et al. Jan 2015 A1
20150023176 Korja et al. Jan 2015 A1
20150030024 Venkataswami et al. Jan 2015 A1
20150043581 Devireddy et al. Feb 2015 A1
20150063166 Sif et al. Mar 2015 A1
20150065161 Ganesh et al. Mar 2015 A1
20150087330 Prechner et al. Mar 2015 A1
20150103818 Kuhn et al. Apr 2015 A1
20150163192 Jain et al. Jun 2015 A1
20150172391 Kasslin et al. Jun 2015 A1
20150223337 Steinmacher-Burow Aug 2015 A1
20150256972 Markhovsky et al. Sep 2015 A1
20150264519 Mirzaei et al. Sep 2015 A1
20150280827 Adiletta et al. Oct 2015 A1
20150288410 Adiletta et al. Oct 2015 A1
20150326704 Ko et al. Nov 2015 A1
20150358777 Gupta Dec 2015 A1
20150362581 Friedman et al. Dec 2015 A1
20160007315 Lundgreen et al. Jan 2016 A1
20160044627 Aggarwal et al. Feb 2016 A1
20160099847 Melander et al. Apr 2016 A1
20160100395 Xu et al. Apr 2016 A1
20160105408 Cooper et al. Apr 2016 A1
20160127875 Zampini, II May 2016 A1
20160146495 Malve et al. May 2016 A1
20160330045 Tang et al. Nov 2016 A1
20160344641 Javidi et al. Nov 2016 A1
20170026974 Dey et al. Jan 2017 A1
20170195205 Li et al. Jul 2017 A1
20170202000 Fu et al. Jul 2017 A1
20170214551 Chan et al. Jul 2017 A1
20170272317 Singla Sep 2017 A1
20170317997 Smith et al. Nov 2017 A1
20170332421 Sternberg et al. Nov 2017 A1
20170339706 Andreoli-Fang et al. Nov 2017 A1
20180063018 Bosch et al. Mar 2018 A1
20180069311 Pallas et al. Mar 2018 A1
20180084389 Snyder et al. Mar 2018 A1
Foreign Referenced Citations (9)
Number Date Country
WO 2013020126 Feb 2013 WO
WO 2014098556 Jun 2014 WO
WO 2015131920 Sep 2015 WO
WO 2017078657 May 2017 WO
WO 2017187011 Nov 2017 WO
WO 2018009340 Jan 2018 WO
WO 2018028777 Feb 2018 WO
WO 2018053271 Mar 2018 WO
WO 2018066362 Apr 2018 WO
Non-Patent Literature Citations (43)
Entry
Cheng et al., “Path Segment in MPLS Based Sement Routing Network,” Network Working Group, Oct. 2017, pp. 1-10.
Ali et al., “Performance Measurement in Segment Routing Networks with IPv6 Data Plane (SRv6),” Spring Working Group, Feb. 26, 2018, pp. 1-17.
“Cisco 10000 Series Router Quality of Service Configuration Guide, Chapter 20: Configuring Quality of Service for MPLS Traffic,” Cisco Systems, Inc., Updated Nov. 17, 2013, pp. 1-34.
“Cisco ASR 5x00 Mobility Management Entity Administration Guide,” Version 15.0, Last updated Jun. 13, 2014, Cisco, 1-266.
“Enterprise Mobility 7.3 Design Guide, Chapter 11: Cisco Mobility Services Engine,” Cisco Systems, Inc., Updated Apr. 20, 2015, 8 pages.
“I Love WiFi, The difference between L2 and L3 Roaming Events,” Apr. 1, 2010, 6 pages.
“Quality of Service Regulation Manual,” ITU 2017, pp. 1-174.
“Wi-Fi Location-Based Services 4.1 Design Guide,” May 20, 2008, 206 pages.
Afolabi, Ibrahim, et al., “Network Slicing & Softwarization: A Survey on Principles, Enabling Technologies & Solutions,” Mar. 21, 2018, pp. 1-24.
An, Xueli, et al., “Virtualization of Cellular Network EPC Gateways based on a Scalable SDN Architecture,” IEEE, Feb. 12, 2015, pp. 1-7.
Antonioli, Roberto, et al., “Dual Connectivity for LTE-NR Cellular Networks,” Research Gate, Sep. 3-6, 2017, pp. 171-175.
Bekan, Adnan, et al., “D5.1: Machine Learning Algorithms Development and Implementation,” 2016-2018 eWINE Consortium, 23, 2016, pp. 1-72.
Bogale, Tadilo Endeshaw, et al., “Machine Intelligence Techniques for Next-Generation Context-Aware Wireless Networks,” arxiv.org, Jan. 12, 2018, pp. 1-10.
Carter, Steve Sr., “E911 VolP Essentials for Enterprise Deployments,” XO Communications, LLC, 2012, 9 pages.
Chalise, Batu K., et al., “MIMO Relaying for Multiaccess Communication in Cellular Networks,” Sensor Array and MultiChannel Signal Processing Workshop, 2008, SAM 2008, 5th IEEE, Jul. 21, 2008, pp. 146-150.
Christidis, Konstantinos, et al., “Blockchains and Smart Contracts for the Internet of Things,” IEEE Access, Special Section on the of Research in Internet of Things (IoT), vol. 4, May 10, 2016, pp. 1-12.
Cox, Jacob H. Jr., et al., “Advancing Software-Defined Networks: A Survey,” IEEE, Oct. 12, 2017, pp. 25487-25526.
Cui, Wenzhi et al., “DiFS: Distributed Flow Scheduling for Data Center Networks,” Nanjing University, China, Jul. 28, 2013, 10 pages.
de Sousa, Nathan F. Saraiva, et al., “Network Service Orchestration: A Survey,” IEEE Communications Surveys & Tutorials, Mar. 23, 2018, pp. 1-30.
Doyle, Matthew G., “An IP Address Management Solution for a Server Solution Provider,” A Dissertation Submitted to The University of Liverpool, Sep. 28, 2005, 116 pages.
Galvan T., Carlos E., et al., “Wifi bluetooth based combined positioning algorithm,” International Meeting of Electrical Engineering Research ENIINVIE 2012, Procedia Engineering 35 (2012 ), pp. 101-108.
Geller, Michael, et al. , “5G Security Innovation with Cisco,” Whitepaper Cisco Public, Jun. 8, 2018, pp. 1-29.
Gesbert, David, “Advances in Multiuser MIMO Systems (Tutorial Part II) Emerging Topics in Multiuser MIMO Networks,” IEEE PIMRC Conference, Sep. 2007, 107 pages.
Halperin, Daniel, et al., “Augmenting Data Center Networks with Multi-Gigabit Wireless Links,” Aug. 15-19, 2011, SIGCOMM'11, ACM 978-1-4503-0797-0/11/08, pp. 38-49.
Herb, Daniel, et al., “ROAUM: How to Unblock Roaming IoT Using BLockchain,” available at https://uploads-ssl.webflow.com/5987a06baeea4300016b7bd9/5a7a6d6cee5bc400010a08f2_Roaum_Roaming_IoT_Whitepaper.pdf, pp. 1-14.
Hsieh, Cynthia, “Location Awareness in VMware View 4.5 and Above,” VMware, 2011, 8 pages.
Husain, Syed, et al., “Mobile Edge Computing with Network Resource Slicing for Internet-of-Things,” IEEE 2017, pp. 1-7.
Jero, Samuel, et al., “Identifier Binding Attacks and Defenses in Software-Defined Networks,” USENIX, The Advanced Computing Systems Association, Aug. 16-18, 2017, 19 pages.
Ji, Philip N., et al., “Demonstration of High-Speed MIMO OFDM Flexible Bandwidth Data Center Network,” Optical Society of America, 2012, 2 pages.
Kandula, Srikanth, et al., “Flyways to De-Congest Data Center Networks,” Microsoft Research, Oct. 23, 2009, 6 pages.
Katayama, Y. et al., “MIMO Link Design Strategy for Wireless Data Center Applications,” IEEE Wireless Communications and Networking Conference: Services, Applications, and Business, 2012, 5 pages.
Leary, Jonathan, et al., “Wireless LAN Fundamentals: Mobility,” Jan. 9, 2004, Cisco Press, 15 pages.
Leonhardt, Ulf, “Supporting Location-Awareness in Open Distributed Systems,” May 1998, 186 pages.
Morozov, Yury, “Blockchain Telecom: Bubbletone Blockchain,” Dec. 29, 2017, pp. 1-33.
Network Heresy, “NVGRE, VXLAN and What Microsoft is Doing Right,” Oct. 3, 2011, 5 pages.
Norwegian National Security Authority, “N-03 Security guidance for switches and routers,” Sep. 26, 2012, pp. 1-24.
Saraiva de Sousa, Nathan F., et al., “Network Service Orchestration: A Survey,” IEEE Communications Surveys & Tutorials, Mar. 23, 2018, pp. 1-30.
Savvides, Andreas, et al., “Dynamic Fine-Grained Localization in Ad-Hoc Networks of Sensors”, Proceeding MobiCom '01 Proceedings of the 7th annual international conference on Mobile computing and networking, Jul. 2001, pp. 166-179.
Shwetha, D., et al.,“A Bandwidth Request Mechanism for QoS Enhancement in Mobile WiMAX Networks,” International Journal of Advanced Research in Electrical Electronics and Instrumentation Engineering, vol. 3, Issue 1, Jan. 2014, pp. 1-8.
Sun, et al., “The future of Wi-Fi,” IEEE Communications Magazine, vol. 52, No. 11, Nov. 21, 2014, 166 pages.
Ventre, Pier Luigi, et al., “Performance Evaluation and Tuning of Virtual Infrastructure Managers for (Micro) Virtual Network Functions,” ieee.org, Nov. 7-10, 2016, pp. 1-7.
Wright, Joshua, “Detecting Wireless LAN MAC Address Spoofing,” Jan. 21, 2003, pp. 1-20.
Zickau, Sebastian, et al., “Enabling Location-based Policies in a Healthcare Cloud Computing Environment,” 2014 IEEE 3rd International Conference on Cloud Networking (Cloudnet), Oct. 2014, pp. 353-358.