Virtual computing environments

• US 9,069,588 B2
• Filed: 11/30/2012
• Issued: 06/30/2015
• Est. Priority Date: 10/19/2007
• Status: Expired due to Fees

• Current Assignee: Kubisys, Inc.
• Original Assignee: Kubisys, Inc.
• Inventors: Safari, Tigran, Acharya, Soubir, Urano, Shinichi
• Primary Examiner(s): Truong, Camquy
• Application Number:
  US13/690,321
• Publication Number:
  US 20130166716A1
• Time in Patent Office: 942 Days
• US Class Current: 1/1
• CPC Class Codes: G06F 11/1433 during software upgrading
• CPC Class Codes: G06F 11/1471 involving logging of persistent data for recovery
• CPC Class Codes: G06F 11/302 where the computing system component is a software system
• CPC Class Codes: G06F 11/3664 Environments for testing or debugging software
• CPC Class Codes: G06F 11/3684 for test design, e.g. generating new test cases
• CPC Class Codes: G06F 11/3688 for test execution, e.g. scheduling of test suites
• CPC Class Codes: G06F 11/3692 for test results analysis
• CPC Class Codes: G06F 11/3696 Methods or tools to render software testable
• CPC Class Codes: G06F 16/80 of semi-structured data, e.g. markup language structured data such as SGML, XML or HTML content-based retrieval of web data G06F16/95
• CPC Class Codes: G06F 2009/45583 Memory management, e.g. access or allocation
• CPC Class Codes: G06F 2009/45591 Monitoring or debugging support
• CPC Class Codes: G06F 2009/45595 Network integration; Enabling network access in virtual machine instances
• CPC Class Codes: G06F 2201/815 Virtual middleware or OS functionality using virtual machines to implement generic software techniques for error detection or fault masking G06F11/1484
• CPC Class Codes: G06F 2201/84 Using snapshots, i.e. a logical point-in-time copy of the data
• CPC Class Codes: G06F 9/455 Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
• CPC Class Codes: G06F 9/45533 Hypervisors; Virtual machine monitors
• CPC Class Codes: G06F 9/45558 Hypervisor-specific management and integration aspects
• CPC Class Codes: G06F 9/542 Event management; Broadcasting; Multicasting; Notifications
• CPC Class Codes: H04L 41/00 Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
• CPC Class Codes: H04L 41/14 Network analysis or design
• CPC Class Codes: H04L 41/50 : Network service management, e.g. ensuring proper service fulfilment according to agreements
• CPC Class Codes: H04L 47/70 : Admission control; Resource allocation
• CPC Class Codes: H04L 67/1097 : for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
• CPC Class Codes: H04L 67/51 : Discovery or management thereof, e.g. service location protocol [SLP] or web services

Abstract

Methods, systems, and apparatus, including computer program products, for discovering entities in a first portion of a data center network, examining information associated with the discovered entities to determine relationships that exist between pairs of entities in the first portion of the data center network, and generating a specification of the relationships that exist between pairs of entities in the first portion of the data center network based on results of the examining.

Claims

1. A computer-implemented method comprising:

detecting a trigger event indicative of a selection of a first entity in a first portion of a data center network to be represented in a second portion of the data center network;

examining a specification of relationships that exist between pairs of entities in the first portion of the data center network to identify a set of entities with which the first entity has respective relationships, and identify a first set of logical disks that comprise a storage footprint of the first entity and each respective entity of the set of the entities;

establishing a communication with each storage type entity in the first portion of the data center network that contains a logical disk of the first set of logical disks, and generating a snapshot of a state of each respective logical disk of the first set of logical disks;

generating a time-fixed data image representative of the state of the first set of logical disks at a particular point in time; and

initiating a cloned instance of the first entity in the second portion of the data center network based at least in part on the time-fixed data image.

2. The computer-implemented method of claim 1, wherein the set of logical disks are contained within a single storage type entity in the first portion of the data center network.

3. The computer-implemented method of claim 1, wherein the snapshot of the state of each respective logical disk of the set is generated at a storage container level.

4. The computer-implemented method of claim 1, wherein the time-fixed data image is generated based at least in part on a plurality of storage check points.

5. The computer-implemented method of claim 1, wherein initiating a cloned instance of the first entity comprises:

examining the specification of relationships that exist between pairs of entities in the first portion of the data center network to identify a second set of logical disks that comprise data sufficient to enable each of one or more applications running on the first entity in the first portion of the data center network to be started in the second portion of the data center network; and

generating a set of virtual machines using data within the first set of logical disks, the second set of logical disks, and the time-fixed data image.

6. The computer-implemented method of claim 5, further comprising:

adapting the data within the first set of logical disks, the second set of logical disks, and the time-fixed data image for consumption by a hypervisor; and

instructing the hypervisor to run a set of virtual machines in the second portion of the data center network, wherein the set of virtual machines includes the cloned instance of the first entity.

7. The computer-implemented method of claim 1, further comprising:

facilitating network communication between the cloned instance of the first entity and a cloned instance of a second entity in the second portion of the data center network.

8. The computer-implemented method of claim 1, further comprising:

enabling a user to selectively modify a configuration of one or more applications running on the cloned instance of the first entity to examine changes in its performance.

9. The computer-implemented method of claim 1, further comprising:

enabling a user to run a series of tests in association with the cloned instance of the first entity to examine its performance.

10. The computer-implemented method of claim 1, further comprising:

enabling a user to selectively modify a configuration of an entity of the set of entities with which the first entity has respective relationships to examine changes in a performance of the first entity, any entity of the set, or some combination thereof.

11. A machine-readable medium that stores executable instructions to cause a machine to:

discover entities in a first portion of a data center network, the discovered entities including at least one storage end point, at least one server end point, and at least one server application instance;

examine information associated with the discovered entities to determine relationships that exist between pairs of entities in the first portion of the data center network, wherein examining the information comprises;

examine meta information of a storage end point to identify a plurality of logical disks located in storage associated with the storage end point, and

examine data within each logical disk of the plurality to identify one or more server end points having access to that logical disk and one or more server application instances having a storage footprint within that logical disk; and

generate a specification of the relationships that exist between pairs of entities in the first portion of the data center network based on results of the examining.

12. A machine-readable medium that stores executable instructions to cause a machine to:

detect a trigger event indicative of a selection of a first entity in a first portion of a data center network to be represented in a second portion of the data center network;

examine a specification of relationships that exist between pairs of entities in the first portion of the data center network to identify a set of entities with which the first entity has respective relationships, and identify a first set of logical disks that comprise a storage footprint of the first entity and each respective entity of the set of the entities;

establish a communication with each storage type entity in the first portion of the data center network that contains a logical disk of the first set of logical disks, and generating a snapshot of a state of each respective logical disk of the first set of logical disks;

generate a time-fixed data image representative of the state of the first set of logical disks at a particular point in time; and

initiate a cloned instance of the first entity in the second portion of the data center network based at least in part on the time-fixed data image.

13. The machine-readable medium of claim 12, wherein the set of logical disks are contained within a single storage type entity in the first portion of the data center network.

14. The machine-readable medium of claim 12, wherein the snapshot of the state of each respective logical disk of the set is generated at a storage container level.

15. The machine-readable medium of claim 12, wherein the time-fixed data image is generated based at least in part on a plurality of storage check points.

16. The machine-readable medium of claim 12, wherein the instructions to cause the machine to initiate a cloned instance of the first entity comprises instructions to:

examine the specification of relationships that exist between pairs of entities in the first portion of the data center network to identify a second set of logical disks that comprise data sufficient to enable each of one or more applications running on the first entity in the first portion of the data center network to be started in the second portion of the data center network; and

generate a set of virtual machines using data within the first set of logical disks, the second set of logical disks, and the time-fixed data image.

17. The machine-readable medium of claim 16, wherein the stored executable instructions further comprise executable instructions to cause a machine to:

adapt the data within the first set of logical disks, the second set of logical disks, and the time-fixed data image, for consumption by a hypervisor; and

instruct the hypervisor to run a set of virtual machines in the second portion of the data center network, wherein the set of virtual machines includes the cloned instance of the first entity.

18. The machine-readable medium of claim 12, wherein the stored executable instructions further comprise executable instructions to cause a machine to:

facilitate network communication between the cloned instance of the first entity and a cloned instance of a second entity in the second portion of the data center network.

19. The machine-readable medium of claim 12, wherein the stored executable instructions further comprise executable instructions to cause a machine to:

enable a user to selectively modify a configuration of one or more applications running on the cloned instance of the first entity to examine changes in its performance.

20. The machine-readable medium of claim 12, wherein the stored executable instructions further comprise executable instructions to cause a machine to:

enable a user to run a series of tests in association with the cloned instance of the first entity to examine its performance.

21. The machine-readable medium of claim 12, wherein the stored executable instructions further comprise executable instructions to cause a machine to:

enable a user to selectively modify a configuration of an entity of the set of entities with which the first entity has respective relationships to examine changes in a performance of the first entity, any entity of the set, or some combination thereof.