Patent Application
20200026875
This disclosure relates to computing platform management, and more particularly to techniques for managing metadata pertaining to protected health information stored in distributed computing systems.
Electronic medical record (EMR) or electronic health record (EHR) systems and applications host and/or access sensitive patient data classified as protected health information (PHI). PHI is any information about health status, provision of health care, or payment for health care that is created or collected by a “covered entity” (e.g., health care provider, health plan, public health authority, employer, life insurer, school or university, etc.) that can be linked to a specific individual. Such covered entities are required to maintain compliance with various privacy laws and regulations such as the Health Insurance Portability and Accountability Act (HIPAA) established in 1996, when managing (e.g., storing, accessing, distributing, etc.) PHI. Procedures and practices for handling of existing and newly created PHI is audited regularly to maintain such compliance. For example, storage of and access to existing PHI pertaining to a given EMR application or applications is audited for compliance. PHI accessed and/or created for ephemeral tasks is also to be compliant. For example, PHI associated with EMR application testing, development and/or training must be compliant with any applicable regulations.
Many modern EMR applications are implemented in hyperconverged distributed computing systems to take advantage of the efficient and cost-effective scaling of distributed computing resources, distributed data storage resources, distributed networking resources, and/or other resources facilitated by such hyperconverged systems. Hyperconverged distributed computing systems have evolved in such a way that incremental linear scaling can be accomplished in many dimensions.
The resources in a given distributed system are often grouped into resource subsystems such as clusters, datacenters, or sites. The resource subsystems can be defined by logical and/or physical boundaries. For example, a cluster might comprise a logically bounded set of nodes associated with a department of an enterprise, while a datacenter might be associated with a particular physical geographical location. Modern clusters in hyperconverged distributed computing systems might support over one hundred nodes (or more) that in turn support as many as several thousands (or more) autonomous virtualized entities (VEs). The VEs in hyperconverged distributed computing systems might be virtual machines (VMs) and/or executable containers, in hypervisor-assisted virtualization environments and/or in operating system virtualization environments, respectively. The clusters further comprise multiple tiers of storage in a storage pool for storing various data and metadata, such as data and metadata pertaining to PHI.
Unfortunately, legacy approaches might create a duplicate copy and/or propagate access through logical unit numbers (LUNs) of a certain set of PHI associated with a particular EMR application to facilitate development or testing of a new version of the application. In this case, the copy and/or the data accessed through the propagated LUN also contains PHI and is subject to compliance with any applicable law or regulation, which might include restrictions as to the physical and/or logical storage location of the copy. For example, such PHI might be restricted to storage facilities or portions of storage facilities (e.g., datastores) deemed HIPAA compliant. Removal of the PHI copy when testing is completed can further be subject to certain mandatory procedures to maintain compliance. Practices of these legacy approaches introduce compliance violation risks that might negatively impact the reputation and/or continued operations of the healthcare providers and/or the IT systems providers. Protection of PHI in compliance with rules and regulations demands technological solutions for managing PHI under a wide range of settings, including in various development and/or testing settings.
What is needed is a technique or techniques to improve over legacy techniques and/or over other considered approaches. Some of the approaches described in this background section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
The present disclosure provides a detailed description of techniques used in systems, methods, and in computer program products for protected health information in distributed computing systems, which techniques advance the relevant technologies to address technological issues with legacy approaches. More specifically, the present disclosure provides a detailed description of techniques used in systems, methods, and in computer program products for protected health information in distributed computing systems. Certain embodiments are directed to technological solutions for generating an ephemeral datastore and a clone of the metadata associated with a protected health information (PHI) source datastore to facilitate performance of tasks pertaining to the PHI.
The disclosed embodiments modify and improve over legacy approaches. In particular, the herein-disclosed techniques provide technical solutions that address the technical problems attendant to maintaining regulatory compliance of protected health information accessed for specialized tasks (e.g., training tasks, development tasks, etc.) in a hyperconverged distributed computing system. Various applications of the herein-disclosed improvements in computer functionality serve to reduce the demand for computer memory, reduce the demand for computer processing power, reduce network bandwidth use, and reduce the demand for inter-component communication, all while still protecting patient health information.
Further details of aspects, objectives, and advantages of the technological embodiments are described herein and in the drawings and claims.
The drawings described below are for illustration purposes only. The drawings are not intended to limit the scope of the present disclosure.
Embodiments in accordance with the present disclosure address the problem of maintaining regulatory compliance of protected health information accessed for training and development tasks in a distributed computing system. Some embodiments are directed to approaches for generating an ephemeral datastore and a clone of the metadata associated with a protected health information (PHI) source datastore to facilitate performance of tasks pertaining to the PHI without modifying or propagating the PHI. Further, the accompanying figures and discussions herein present example environments, systems, methods, and computer program products for protected health information in hyperconverged distributed computing systems.
Disclosed herein are techniques for generating an ephemeral datastore and a clone of metadata associated with a protected health information (PHI) source datastore. The ephemeral datastore and cloned metadata can both be maintained ephemerally, for use only during execution of ephemeral tasks, such as application development or testing, pertaining to the PHI. The ephemeral datastore and cloned metadata can be operated over during such tasks without modifying or propagating the PHI. In certain embodiments, instructions are received that request access to the PHI source datastore for performing various ephemeral tasks such as EMR application testing, development, or training. The clone of the metadata associated with the PHI source datastore is generated to provide read-only access to the PHI source datastore. The ephemeral datastore is created to facilitate read-write operations pertaining to the ephemeral task. When the ephemeral task or tasks are completed, the cloned metadata and the ephemeral datastore are deleted. In certain embodiments, the storage location of the cloned metadata and/or the ephemeral datastore is determined based on a set of protection domain rules. In some embodiments, snapshotting techniques are used to generate the cloned metadata for accessing the PHI source datastore and/or the ephemeral datastore. In some embodiments, a protection domain can comprise PHI source datastores, nodes, hard disk drives, solid state disk drives, virtual machines, virtual disks, and/or EMR applications.
Definitions and Use of Figures
Some of the terms used in this description are defined below for easy reference. The presented terms and their respective definitions are not rigidly restricted to these definitions—a term may be further defined by the term's use within this disclosure. The term “exemplary” is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs. Rather, use of the word exemplary is intended to present concepts in a concrete fashion. As used in this application and the appended claims, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless specified otherwise, or is clear from the context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A, X employs B, or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. As used herein, at least one of A or B means at least one of A, or at least one of B, or at least one of both A and B. In other words, this phrase is disjunctive. The articles “a” and “an” as used in this application and the appended claims should generally be construed to mean “one or more” unless specified otherwise or is clear from the context to be directed to a singular form.
Various embodiments are described herein with reference to the figures. It should be noted that the figures are not necessarily drawn to scale and that elements of similar structures or functions are sometimes represented by like reference characters throughout the figures. It should also be noted that the figures are only intended to facilitate the description of the disclosed embodiments—they are not representative of an exhaustive treatment of all possible embodiments, and they are not intended to impute any limitation as to the scope of the claims. In addition, an illustrated embodiment need not portray all aspects or advantages of usage in any particular environment.
An aspect or an advantage described in conjunction with a particular embodiment is not necessarily limited to that embodiment and can be practiced in any other embodiments even if not so illustrated. References throughout this specification to “some embodiments” or “other embodiments” refer to a particular feature, structure, material or characteristic described in connection with the embodiments as being included in at least one embodiment. Thus, the appearance of the phrases “in some embodiments” or “in other embodiments” in various places throughout this specification are not necessarily referring to the same embodiment or embodiments. The disclosed embodiments are not intended to be limiting of the claims.
The protected healthcare information management technique 100 presents one embodiment of steps and/or operations executed according to the herein disclosed techniques to manage protected health information in a distributed computing system 120. Specifically, the protected healthcare information management technique 100 might commence with identifying a set of protected health information (PHI) such as protected health information 142 in a PHI source datastore 124 (step 102). PHI is any information about health status, provision of health care, or payment for health care that is created or collected by a “covered entity” (e.g., health care provider, health plan, public health authority, employer, life insurer, school or university, etc.) that can be linked to a specific individual.
PHI is often organized and/or stored in a tabular structure (e.g., relational database table) having rows corresponding to a unique identifier of a particular individual, and columns corresponding to information associated with that individual. Various collections of PHI can be referred to as electronic medical records (EMRs). The datastores (e.g., PHI source datastore 124) for storing PHI are logical or physical portions (e.g., segments, volumes, disks, virtual disks, containers, etc.) of one or more storage facilities allocated for storing the PHI. For example, a datastore might comprise a 1 TB storage segment of a 100 TB storage volume or storage pool.
As can be observed in
The EMR tasks as discussed herein are sets of operations that rely on access to at least some portion of PHI. For example, an EMR task might comprise instantiating a certain EMR application for the purposes of testing or developing the application. Specifically, examples of an EMR task can comprise an application development task, an application testing task, an application training task, and/or another task. In any case, the EMR task might be required to be executed without modifying, duplicating, or propagating the corresponding PHI so as to comply with various PHI regulations (e.g., HIPAA).
The herein disclosed techniques can address such restrictions by detecting any EMR task commands (e.g., EMR task commands 136) issued to operate over the protected health information 142 (step 104). A set of cloned metadata 126 (e.g., cloned from source metadata 122) and an ephemeral datastore 128 are generated to facilitate certain EMR task operations (step 106). The cloned metadata 126 and the ephemeral datastore 128 can be used to perform read and write operations associated with the EMR tasks, yet without modifying the protected health information 142 in the PHI source datastore 124 (step 108). Specifically, the cloned metadata 126 can point to the PHI source datastore 124 for read-only operations 1381, and can point to the ephemeral datastore 128 for modifying operations (e.g., read/write operations 1342). The ephemeral datastore 128 will hold any modified protected health information 144 produced as a result of an EMR task, while the PHI source datastore 124 remains unaffected by the execution of the EMR tasks. When an EMR task is complete, the cloned metadata 126 and the ephemeral datastore 128 can be deleted (step 110).
One embodiment of a subsystem and corresponding data flows for implementing any of the herein disclosed techniques is shown and described as pertaining to
The embodiment shown in
Other instances of the protected data manager and/or the storage I/O controller at other nodes (e.g., node 252NM) in hyperconverged distributed system 250 are possible. Various applications (e.g., EMR app 2661, . . . , EMR app 266K) and/or tasks (e.g., EMR task 268) interact with storage I/O controller 26211 and protected data manager 26411 to access various data (e.g., PHI) stored in a set of distributed storage resources 270 in the hyperconverged distributed system 250. Specifically, the applications and/or tasks can issue various instances of commands 232 that are transformed into a corresponding set of storage I/O operations 234 by the storage I/O controller 26211 and/or the protected data manager 26411. The storage I/O operations 234 are used to access the PHI source datastore 124 or the ephemeral datastore 128 using metadata 272 (e.g., source metadata 122, cloned metadata 126, etc.) earlier described. The foregoing datastores, metadata, and a set of PHI rules 274 can be stored locally at node 25211 and/or distributed across multiple nodes in the distributed storage resources 270.
The protected health information rules (e.g., PHI rules 274) comprise various constraints that are applied to certain aspects pertaining to managing protected health information. For example, PHI rules 274 might comprise constraints as to the location (e.g., node, storage facility, virtualized entity, etc.) used to operate an EMR application (e.g., EMR app 2661) and/or constraints pertaining to an EMR task (e.g., EMR task 268), and/or constraints as to the locations used to allocate storage for storing PHI (e.g., PHI source datastore 124, ephemeral datastore 128, etc.) and/or constraints pertaining to the PHI-related or PHI-derived information (e.g., source metadata 122, cloned metadata 126, etc.). Such location or domain constraints are often organized and/or stored in a tabular structure (e.g., a relational database table). PHI rules 274 might further comprise constraints in the form of conditional logic to facilitate other aspects pertaining to managing protected health information. For example, conditional logic might be applied to commands 232 to determine the metadata and/or datastores to access in the distributed storage resources 270.
As further shown, the working environment 2A00 shown in
The components and data flows shown in
The storage facility generation technique 2B00 depicts one embodiment of the steps and/or operations implemented at the protected data manager 26411 for generating the cloned metadata 126 and ephemeral datastore 128 according to the herein disclosed techniques. As can be observed, the storage facility generation technique 2B00 can continually process (see “No” path of decision 204) EMR application commands (step 202) until an EMR task is detected (decision 204). For example, and as shown, EMR application commands 132 can reference the source metadata 122 to access the PHI source datastore 124. The source metadata 122 holds virtual or logical representations of the physical data at the PHI source datastore 124 in a set of logical files 2761 (e.g., virtual disks or vDisks, etc.). A set of block maps 2781 can also be stored in source metadata 122 to map the logical data blocks of the logical files 2761 to their corresponding instances of physical data blocks in the PHI source datastore 124.
Responsive to detecting an EMR task (see “Yes” path of decision 204), a set of cloned metadata 126 can be generated from a snapshot of the source metadata 122 (step 206). Specifically, a metadata snapshot 282 can be executed to replicate the then-current logical files and block maps of the source metadata 122 to create an instance of the cloned metadata 126. No PHI is included in the cloned metadata 126. Further, no data (e.g., PHI) from the PHI source datastore 124 is duplicated or propagated.
As shown in
In some embodiments, generating the cloned metadata and/or ephemeral datastore can be scheduled according to various resource usage metrics collected from the hyperconverged distributed system. For example, performance metrics for compute resources, storage resources, network resources, and/or other resources can be analyzed to determine a time and/or location (e.g., node, virtualized entity, hard disk drive, etc.) for invoking EMR tasks, executing EMR commands, generating cloned metadata (e.g., snapshotting), generating ephemeral datastores, and/or performing other operations.
Further details regarding general approaches to resource scheduling in hyperconverged distributed computing system are described in U.S. application Ser. No. 15/341,549 titled, “LONG-RANGE DISTRIBUTED RESOURCE PLANNING USING WORKLOAD MODELING IN HYPERCONVERGED COMPUTING CLUSTERS” filed on Nov. 2, 2016, which is hereby incorporated by reference in its entirety.
In some cases, the resources in a hyperconverged distributed system that serve protected health information is limited. Such a collection of resources can be referred to as a protection domain. Protection domains are a set of logically and/or physically bounded resources associated with a respective set or sets of PHI. The resources comprising a protection domains can include one or more nodes, one or more virtualized entities (e.g., VMs, containers, etc.), one or more datastores, one or more applications (e.g., EMR applications, EMR tasks, etc.), and/or other types of resources. An example of a protection domain in a hyperconverged distributed computing environment is shown and described as pertaining to
The embodiment shown in
In certain embodiments, the resources associated with protection domain 374 can be identified and/or otherwise described in a set of PHI rules 274. Multiple protection domains comprising the resources from the hyperconverged distributed system 250 can be characterized in the PHI rules 274. As earlier described, a protection domain is often associated with a respective set of PHI. In the example shown in
The aforementioned PHI rules 274 can be applied to various operations facilitated by the herein disclosed techniques. Examples of applying the PHI rules 274 are shown and described as pertaining to
The embodiment shown in
The EMR application associated with an EMR task to be launched can be identified by or associated with protection domain attributes 338 (step 306). For example, an EMR task might be a “test” task associated with EMR application “emrAppX”. The protection domain attributes 338 are further applied to determine a set of feasible EMR task operational environments (step 308). For example, the protection domain attributes 338 indicate the application “emrAppX” corresponds to protection domain “PD123” which, in turn, comprises node “N11”, node “N12”, and node “N18” (but not node “N16”). As illustrated in
The embodiment shown in
The EMR command management rules 340 are applied to commands 232 (step 318) to direct the commands 232 to operate over the source metadata 122 and PHI source datastore 124 using a set of read-only operations 1382, or operate over the cloned metadata 126 and ephemeral datastore 128 using a set of read/write operations 1343 (step 320). For example, as shown in the EMR command management rules 340, if a received instance of the commands 232 comprises an “operation” that is a “read” (e.g., read-only) operation, then the operation will “read” from PHI source datastore 124 (e.g., “phi_source_store”). As further shown, if the “operation” is not a “read” operation, the EMR command management rules 340 will direct the operation to “write” a new block (e.g., “new_block”) to the ephemeral datastore 128 (e.g., “ephemeral_store”) and “update” the cloned metadata 126 (e.g., “cloned_metadata”) to point to the “new block”. As can be appreciated, computing resources are conserved using this technique, at least in that for the aforementioned class of read-only operations, only metadata is cloned; the PHI itself is not cloned.
One embodiment of an environment for implementing any of the herein disclosed techniques is shown and described as pertaining to
The shown hyperconverged distributed computing environment depicts various components associated with one instance of a distributed virtualization system comprising a distributed storage system 460 that can be used to implement the herein disclosed techniques. Specifically, the hyperconverged distributed computing environment 400 comprises multiple clusters (e.g., cluster 4501, . . . , cluster 450N) comprising multiple nodes that have multiple tiers of storage in a storage pool. Representative nodes (e.g., node 25211, . . . , node 2521M) and storage pool 470 associated with cluster 4501 are shown. Each node can be associated with one server, multiple servers, or portions of a server. The nodes can be associated (e.g., logically and/or physically) with the clusters. As shown, the multiple tiers of storage include storage that is accessible through a network 464, such as a networked storage 475 (e.g., a storage area network or SAN, network attached storage or NAS, etc.). The multiple tiers of storage further include instances of local storage (e.g., local storage 47211, . . . , local storage 4721M). For example, the local storage can be within or directly attached to a server and/or appliance associated with the nodes. Such local storage can include solid state drives (SSD 47311, . . . , SSD 4731M), hard disk drives (HDD 47411, . . . , HDD 4741M), and/or other storage devices.
As shown, the nodes in hyperconverged distributed computing environment 400 can implement one or more user virtualized entities (e.g., VE 458111, . . . , VE 45811K, . . . , VE 4581M1, . . . , VE 4581MK) such as virtual machines (VMs) and/or containers. The VMs can be characterized as software-based computing “machines” implemented in a hypervisor-assisted virtualization environment that emulates the underlying hardware resources (e.g., CPU, memory, etc.) of the nodes. For example, multiple VMs can operate on one physical machine (e.g., node host computer) running a single host operating system (e.g., host operating system 45611, . . . , host operating system 4561M), while the VMs run multiple applications on various respective guest operating systems. Such flexibility can be facilitated at least in part by a hypervisor (e.g., hypervisor 45411, . . . , hypervisor 4541M), which hypervisor is logically located between the various guest operating systems of the VMs and the host operating system of the physical infrastructure (e.g., node).
As an example, hypervisors can be implemented using virtualization software (e.g., VMware ESXi, Microsoft Hyper-V, RedHat KVM, Nutanix AHV, etc.) that includes a hypervisor. In comparison, the containers (e.g., application containers or ACs) are implemented at the nodes in an operating system virtualization environment or container virtualization environment. The containers comprise groups of processes and/or resources (e.g., memory, CPU, disk, etc.) that are isolated from the node host computer and other containers. Such containers directly interface with the kernel of the host operating system (e.g., host operating system 45611, . . . , host operating system 4561M) with, in most cases, no hypervisor layer. This lightweight implementation can facilitate efficient distribution of certain software components such as applications or services (e.g., micro-services). As shown, hyperconverged distributed computing environment 400 can implement both a hypervisor-assisted virtualization environment and a container virtualization environment for various purposes.
Hyperconverged distributed computing environment 400 also comprises at least one instance of a virtualized controller to facilitate access to storage pool 470 by the VMs and/or containers.
As used in these embodiments, a virtualized controller is a collection of software instructions that serve to abstract details of underlying hardware or software components from one or more higher-level processing entities. A virtualized controller can be implemented as a virtual machine as a container (e.g., a Docker container), or within a layer (e.g., such as a hypervisor).
Multiple instances of such virtualized controllers can coordinate within a cluster to form the distributed storage system 460 which can, among other operations, manage the storage pool 470. This architecture further facilitates efficient scaling of the distributed virtualization system. The foregoing virtualized controllers can be implemented in hyperconverged distributed computing environment 400 using various techniques. Specifically, an instance of a virtual machine at a given node can be used as a virtualized controller in a hypervisor-assisted virtualization environment to manage storage and I/O activities. In this case, for example, the virtualize entities at node 25211 can interface with a controller virtual machine (e.g., virtualized controller 46211) through hypervisor 45411 to access the storage pool 470. In such cases, the controller virtual machine is not formed as part of specific implementations of a given hypervisor. Instead, the controller virtual machine can run as a virtual machine above the hypervisor at the various node host computers. When the controller virtual machines run above the hypervisors, varying virtual machine architectures and/or hypervisors can operate with the distributed storage system 460.
For example, a hypervisor at one node in the distributed storage system 460 might correspond to VMware ESXi software, and a hypervisor at another node in the distributed storage system 460 might correspond to Nutanix AHV software. As another virtualized controller implementation example, containers (e.g., Docker containers) can be used to implement a virtualized controller (e.g., virtualized controller 4621M) in an operating system virtualization environment at a given node. In this case, for example, the virtualized entities at node 2521M can access the storage pool 470 by interfacing with a controller container (e.g., virtualized controller 4621M) through hypervisor 4541M and/or the kernel of host operating system 4561M.
In certain embodiments, one or more instances of a protected data manager can be implemented in distributed storage system 460 to facilitate the herein disclosed techniques. Specifically, protected data manager 26411 can be implemented in virtualized controller 46211, and protected data manager 2641M can be implemented in virtualized controller 4621M. Such instances of the protected data manager and/or the virtualized controller can be implemented in any node in any cluster. Actions taken by one or more instances of the protected data manager and/or virtualized controller can apply to a node (or between nodes) and/or to a cluster (or between clusters) and/or between any resources or subsystems accessible by the virtualized controller or their agents (e.g., protected data manager).
As further shown, any of the foregoing virtualized entities can host the earlier described EMR applications and/or EMR tasks. For example, EMR app 2661 might run on VE 45811i and EMR task 268 might run on VE 4581M1. As can be observed, the metadata and datastores associated with the herein disclosed techniques can be stored in various storage facilities in the storage pool 470. As an example, source metadata 122 might be stored at SSD 47311, PHI source datastore 124 might be stored at HDD 47411, cloned metadata 126 might be stored at SSD 4731M, and ephemeral datastore 128 might be stored in non-persistent volatile memory or at HDD 4741M. The particular resources in the hyperconverged distributed computing environment 400 selected to host the EMR applications, EMR tasks, metadata, datastores, and/or other resource consumers related to the herein disclosed techniques might be determined based on the PHI rules 274 (e.g., protection domain attributes, EMR command management rules, etc.) stored in the networked storage 475.
The system 500 comprises at least one processor and at least one memory, the memory serving to store program instructions corresponding to operations for performing one or more tasks associated with a set of protected healthcare information in a hyperconverged distributed system. As shown, an operation can be implemented in whole or in part using program instructions accessible by a module. The modules are connected to a communication path 505, and any operation can communicate with other operations over communication path 505. The modules of the system can, individually or in combination, perform method operations within system 500. Any operations performed within system 500 may be performed in any order unless as may be specified in the claims.
The shown embodiment implements a portion of a computer system, presented as system 500, comprising one or more computer processors to execute a set of program code instructions (module 510), and modules for accessing memory to hold program code instructions to perform: receiving one or more commands corresponding to the tasks to access the protected health information, wherein the protected health information is stored in at least one protected health information source datastore that is logically represented by a set of source metadata (module 520); generating a set of cloned metadata, wherein the cloned metadata is a clone of the source metadata (module 530); executing, responsive to one or more of the commands, at least one read-only operation over the protected health information by referencing the cloned metadata to access the protected health information source datastore (module 540); generating at least one ephemeral datastore (module 550); and executing, responsive to one or more of the commands, at least one write operation over the protected health information by referencing the cloned metadata to store a set of modified protected health information in the ephemeral datastore, wherein the read-only operation at the protected health information source datastore and the write operation at the ephemeral datastore perform the tasks with no modification of the protected health information at the protected health information source datastore (module 560).
Variations of the foregoing may include more or fewer of the shown modules. Certain variations may perform more or fewer (or different) steps, and/or certain variations may use data elements in more, or in fewer (or different) operations.
A hyperconverged system coordinates efficient use of compute and storage resources by and between the components of the distributed system. Adding a hyperconverged unit to a hyperconverged system expands the system in multiple dimensions. As an example, adding a hyperconverged unit to a hyperconverged system can expand in the dimension of storage capacity while concurrently expanding in the dimension of computing capacity and also in the dimension of networking bandwidth. Components of any of the foregoing distributed systems can comprise physically and/or logically distributed autonomous entities.
Physical and/or logical collections of such autonomous entities can sometimes be referred to as nodes. In some hyperconverged systems, compute and storage resources can be integrated into a unit of a node. Multiple nodes can be interrelated into an array of nodes, which nodes can be grouped into physical groupings (e.g., arrays) and/or into logical groupings or topologies of nodes (e.g., spoke-and-wheel topologies, rings, etc.). Some hyperconverged systems implement certain aspects of virtualization. For example, in a hypervisor-assisted virtualization environment, certain of the autonomous entities of a distributed system can be implemented as virtual machines. As another example, in some virtualization environments, autonomous entities of a distributed system can be implemented as containers. In some systems and/or environments, hypervisor-assisted virtualization techniques and operating system virtualization techniques are combined.
As shown, the virtual machine architecture 6A00 comprises a collection of interconnected components suitable for implementing embodiments of the present disclosure and/or for use in the herein-described environments. Moreover, the shown virtual machine architecture 6A00 includes a virtual machine instance in a configuration 601 that is further described as pertaining to the controller virtual machine instance 630. A controller virtual machine instance receives block I/O (input/output or JO) storage requests as network file system (NFS) requests in the form of NFS requests 602, and/or internet small computer storage interface (iSCSI) block JO requests in the form of iSCSI requests 603, and/or Samba file system (SMB) requests in the form of SMB requests 604. The controller virtual machine (CVM) instance publishes and responds to an internet protocol (IP) address (e.g., CVM IP address 610). Various forms of input and output (I/O or JO) can be handled by one or more JO control handler functions (e.g., IOCTL functions 608) that interface to other functions such as data JO manager functions 614 and/or metadata manager functions 622. As shown, the data JO manager functions can include communication with a virtual disk configuration manager 612 and/or can include direct or indirect communication with any of various block JO functions (e.g., NFS JO, iSCSI JO, SMB JO, etc.).
In addition to block JO functions, the configuration 601 supports JO of any form (e.g., block JO, streaming JO, packet-based JO, HTTP traffic, etc.) through either or both of a user interface (UI) handler such as UI JO handler 640 and/or through any of a range of application programming interfaces (APIs), possibly through the shown API JO manager 645.
The communications link 615 can be configured to transmit (e.g., send, receive, signal, etc.) any types of communications packets comprising any organization of data items. The data items can comprise a payload data, a destination address (e.g., a destination IP address) and a source address (e.g., a source IP address), and can include various packet processing techniques (e.g., tunneling), encodings (e.g., encryption), and/or formatting of bit fields into fixed-length blocks or into variable length fields used to populate the payload. In some cases, packet characteristics include a version identifier, a packet or payload length, a traffic class, a flow label, etc. In some cases the payload comprises a data structure that is encoded and/or formatted to fit into byte or word boundaries of the packet.
In some embodiments, hard-wired circuitry may be used in place of or in combination with software instructions to implement aspects of the disclosure. Thus, embodiments of the disclosure are not limited to any specific combination of hardware circuitry and/or software. In embodiments, the term “logic” shall mean any combination of software or hardware that is used to implement all or part of the disclosure.
The term “computer readable medium” or “computer usable medium” as used herein refers to any medium that participates in providing instructions to a data processor for execution. Such a medium may take many forms including, but not limited to, non-volatile media and volatile media. Non-volatile media includes any non-volatile storage medium, for example, solid state storage devices (SSDs) or optical or magnetic disks such as disk drives or tape drives. Volatile media includes dynamic memory such as a random access memory. As shown, the controller virtual machine instance 630 includes a content cache manager facility 616 that accesses storage locations, possibly including local dynamic random access memory (DRAM) (e.g., through the local memory device access block 618) and/or possibly including accesses to local solid state storage (e.g., through local SSD device access block 620).
Common forms of computer readable media includes any non-transitory computer readable medium, for example, floppy disk, flexible disk, hard disk, magnetic tape, or any other magnetic medium; CD-ROM or any other optical medium; punch cards, paper tape, or any other physical medium with patterns of holes; or any RAM, PROM, EPROM, FLASH-EPROM, or any other memory chip or cartridge. Any data can be stored, for example, in any form of external data repository 631, which in turn can be formatted into any one or more storage areas, and which can comprise parameterized storage accessible by a key (e.g., a filename, a table name, a block address, an offset address, etc.). An external data repository 631 can store any forms of data, and may comprise a storage area dedicated to storage of metadata pertaining to the stored forms of data. In some cases, metadata, can be divided into portions. Such portions and/or cache copies can be stored in the external storage data repository and/or in a local storage area (e.g., in local DRAM areas and/or in local SSD areas). Such local storage can be accessed using functions provided by a local metadata storage access block 624. The external data repository 631 can be configured using a CVM virtual disk controller 626, which can in turn manage any number or any configuration of virtual disks.
Execution of the sequences of instructions to practice certain embodiments of the disclosure are performed by a one or more instances of a software instruction processor, or a processing element such as a data processor, or such as a central processing unit (e.g., CPU1, CPU2). According to certain embodiments of the disclosure, two or more instances of a configuration 601 can be coupled by a communications link 615 (e.g., backplane, LAN, PTSN, wired or wireless network, etc.) and each instance may perform respective portions of sequences of instructions as may be required to practice embodiments of the disclosure.
The shown computing platform 606 is interconnected to the Internet 648 through one or more network interface ports (e.g., network interface port 6231 and network interface port 6232). The configuration 601 can be addressed through one or more network interface ports using an IP address. Any operational element within computing platform 606 can perform sending and receiving operations using any of a range of network protocols, possibly including network protocols that send and receive packets (e.g., network protocol packet 6211 and network protocol packet 6212).
The computing platform 606 may transmit and receive messages that can be composed of configuration data, and/or any other forms of data and/or instructions organized into a data structure (e.g., communications packets). In some cases, the data structure includes program code instructions (e.g., application code) communicated through the Internet 648 and/or through any one or more instances of communications link 615. Received program code may be processed and/or executed by a CPU as it is received and/or program code may be stored in any volatile or non-volatile storage for later execution. Program code can be transmitted via an upload (e.g., an upload from an access device over the Internet 648 to computing platform 606). Further, program code and/or results of executing program code can be delivered to a particular user via a download (e.g., a download from the computing platform 606 over the Internet 648 to an access device).
The configuration 601 is merely one sample configuration. Other configurations or partitions can include further data processors, and/or multiple communications interfaces, and/or multiple storage devices, etc. within a partition. For example, a partition can bound a multi-core processor (e.g., possibly including embedded or co-located memory), or a partition can bound a computing cluster having plurality of computing elements, any of which computing elements are connected directly or indirectly to a communications link. A first partition can be configured to communicate to a second partition. A particular first partition and particular second partition can be congruent (e.g., in a processing element array) or can be different (e.g., comprising disjoint sets of components).
A cluster is often embodied as a collection of computing nodes that can communicate between each other through a local area network (e.g., LAN or VLAN) or a backplane. Some clusters are characterized by assignment of a particular set of the aforementioned computing nodes to access a shared storage facility that is also configured to communicate over the local area network or backplane. In many cases, the physical bounds of a cluster are defined by a mechanical structure such as a cabinet or such as a chassis or rack that hosts a finite number of mounted-in computing units. A computing unit in a rack can take on a role as a server, or as a storage unit, or as a networking unit, or any combination therefrom. In some cases, a unit in a rack is dedicated to provision of power to the other units. In some cases, a unit in a rack is dedicated to environmental conditioning functions such as filtering and movement of air through the rack, and/or temperature control for the rack. Racks can be combined to form larger clusters. For example, the LAN of a first rack having 32 computing nodes can be interfaced with the LAN of a second rack having 16 nodes to form a two-rack cluster of 48 nodes. The former two LANs can be configured as subnets, or can be configured as one VLAN. Multiple clusters can communicate between one module to another over a WAN (e.g., when geographically distal) or LAN (e.g., when geographically proximal).
A module as used herein can be implemented using any mix of any portions of memory and any extent of hard-wired circuitry including hard-wired circuitry embodied as a data processor. Some embodiments of a module include one or more special-purpose hardware components (e.g., power control, logic, sensors, transducers, etc.). A data processor can be organized to execute a processing entity that is configured to execute as a single process or configured to execute using multiple concurrent processes to perform work. A processing entity can be hardware-based (e.g., involving one or more cores) or software-based, and/or can be formed using a combination of hardware and software that implements logic, and/or can carry out computations and/or processing steps using one or more processes and/or one or more tasks and/or one or more threads or any combination thereof.
Some embodiments of a module include instructions that are stored in a memory for execution so as to implement algorithms that facilitate operational and/or performance characteristics pertaining to managing protected health information in distributed computing systems. In some embodiments, a module may include one or more state machines and/or combinational logic used to implement or facilitate the operational and/or performance characteristics pertaining to managing protected health information in distributed computing systems.
Various implementations of the data repository comprise storage media organized to hold a series of records or files such that individual records or files are accessed using a name or key (e.g., a primary key or a combination of keys and/or query clauses). Such files or records can be organized into one or more data structures (e.g., data structures used to implement or facilitate aspects of managing protected health information in hyperconverged distributed computing systems). Such files or records can be brought into and/or stored in volatile or non-volatile memory. More specifically, the occurrence and organization of the foregoing files, records, and data structures improve the way that the computer stores and retrieves data in memory, for example, to improve the way data is accessed when the computer is performing operations pertaining to protected health information in hyperconverged distributed computing systems, and/or for improving the way data is manipulated when performing computerized operations pertaining to generating an ephemeral datastore and/or a clone of metadata associated with a protected health information (PHI) source datastore.
Further details regarding general approaches to managing data repositories are described in U.S. Pat. No. 8,601,473 titled “ARCHITECTURE FOR MANAGING I/O AND STORAGE FOR A VIRTUALIZATION ENVIRONMENT”, issued on Dec. 3, 2013 which is hereby incorporated by reference in its entirety.
Further details regarding general approaches to managing and maintaining data in data repositories are described in U.S. Pat. No. 8,549,518 titled “METHOD AND SYSTEM FOR IMPLEMENTING A MAINTENANCE SERVICE FOR MANAGING I/O AND STORAGE FOR A VIRTUALIZATION ENVIRONMENT”, issued on Oct. 1, 2013, which is hereby incorporated by reference in its entirety.
The operating system layer can perform port forwarding to any container (e.g., container instance 650). A container instance can be executed by a processor. Runnable portions of a container instance sometimes derive from a container image, which in turn might include all, or portions of any of, a Java archive repository (JAR) and/or its contents, and/or a script or scripts and/or a directory of scripts, and/or a virtual machine configuration, and may include any dependencies therefrom. In some cases a configuration within a container might include an image comprising a minimum set of runnable code. Contents of larger libraries and/or code or data that would not be accessed during runtime of the container instance can be omitted from the larger library to form a smaller library composed of only the code or data that would be accessed during runtime of the container instance. In some cases, start-up time for a container instance can be much faster than start-up time for a virtual machine instance, at least inasmuch as the container image might be much smaller than a respective virtual machine instance. Furthermore, start-up time for a container instance can be much faster than start-up time for a virtual machine instance, at least inasmuch as the container image might have many fewer code and/or data initialization steps to perform than a respective virtual machine instance.
A container instance (e.g., a Docker container) can serve as an instance of an application container. Any container of any sort can be rooted in a directory system, and can be configured to be accessed by file system commands (e.g., “ls” or “ls-a”, etc.). The container might optionally include operating system components 678, however such a separate set of operating system components need not be provided. As an alternative, a container can include a runnable instance 658, which is built (e.g., through compilation and linking, or just-in-time compilation, etc.) to include all of the library and OS-like functions needed for execution of the runnable instance. In some cases, a runnable instance can be built with a virtual disk configuration manager, any of a variety of data IO management functions, etc. In some cases, a runnable instance includes code for, and access to, a container virtual disk controller 676. Such a container virtual disk controller can perform any of the functions that the aforementioned CVM virtual disk controller 626 can perform, yet such a container virtual disk controller does not rely on a hypervisor or any particular operating system so as to perform its range of functions.
In some environments multiple containers can be collocated and/or can share one or more contexts. For example, multiple containers that share access to a virtual disk can be assembled into a pod (e.g., a Kubernetes pod). Pods provide sharing mechanisms (e.g., when multiple containers are amalgamated into the scope of a pod) as well as isolation mechanisms (e.g., such that the namespace scope of one pod does not share the namespace scope of another pod).
In the foregoing specification, the disclosure has been described with reference to specific embodiments thereof. It will however be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the disclosure. For example, the above-described process flows are described with reference to a particular ordering of process actions. However, the ordering of many of the described process actions may be changed without affecting the scope or operation of the disclosure. The specification and drawings are to be regarded in an illustrative sense rather than in a restrictive sense.
