SYSTEM, METHOD AND COMPUTER PROGRAM PRODUCT FOR EVALUATING A VIRTUAL MACHINE

• US 20080256533A1
• Filed: 04/10/2007
• Published: 10/16/2008
• Est. Priority Date: 04/10/2007
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Pelleg, Dan, Ben-Yehuda, Shmuel, Spainhower, Lisa F., Harper, Richard E.
• Granted Patent: US 8,055,951 B2
• Time in Patent Office: Days
• US Class Current: 718/1
• CPC Class Codes: G06F 2009/45591 Monitoring or debugging support
• CPC Class Codes: G06F 9/45558 Hypervisor-specific management and integration aspects

Abstract

A method for evaluating a virtual machine, the method includes: monitoring, using an out of band monitor, information exchanged between the virtual machine and a hypervisor; and evaluating a state of the virtual machine by applying a statistical classification process to at least a portion of the monitored information.

Claims

1. A method for evaluating a virtual machine, the method comprises:

monitoring, using an out of band monitor, information exchanged between the virtual machine and a hypervisor; and

evaluating a state of the virtual machine by applying a statistical classification process to at least a portion of the monitored information.

2. The method according to claim 1 wherein the monitoring comprises monitoring, by an out of band monitor included within the hypervisor, information exchanged between the virtual machine and the hypervisor.

3. The method according to claim 1 wherein the monitoring comprises monitoring information representative of resource utilization by the virtual machine.

4. The method according to claim 1 further comprising defining state classes by applying a machine learning process and utilizing the state classes during the appliance of the statistical classification process.

5. The method according to claim 4 further comprising emulating a functional state of the virtual machine and generating a functional state class, by a machine learning entity.

6. The method according to claim 1 wherein the applying comprises defining the state of the virtual machine as being a faulty state or a potentially faulty state if the state of the virtual machine is not in a functional state class.

7. The method according to claim 1 further comprising amending a fault associated with a faulty state, or with a potentially faulty state, if the state of the virtual machine is faulty or potentially faulty.

8. The method according to claim 1 further comprising migrating the virtual machine if the state of the virtual machine is faulty or potentially faulty.

9. The method according to claim 1 wherein the evaluating is responsive to multiple samples of the monitored information acquired during a long monitoring period.

10. The method according to claim 1 wherein the classification is responsive to statistical characteristics of a long term behavior pattern of the virtual machine.

11. A computer program product comprising a computer usable medium including a computer readable program, wherein the computer readable program, when executed on a computer, causes the computer to:

perform out of band monitoring of information exchanged between the virtual machine and a hypervisor; and

evaluate a state of the virtual machine by applying a statistical classification process to at least a portion of the monitored information.

12. The computer program product according to claim 11, wherein the computer readable program, when executed on a computer, causes the computer to monitor information representative of resource utilization by the virtual machine.

13. The computer program product according to claim 11, wherein the computer readable program, when executed on a computer, causes the computer to define state classes by applying a machine learning process and to utilize the state classes during the appliance of the statistical classification process.

14. The computer program product according to claim 11, wherein the computer readable program, when executed on a computer, causes the computer to classify the state of the virtual machine as being in one out of a functional states class, a faulty state class and a potentially faulty state class.

15. The computer program product according to claim 11, wherein the computer readable program, when executed on a computer, causes the computer to define the state of the virtual machine as being a faulty state or a potentially faulty state if the state of the virtual machine is not in a functional state class.

16. The computer program product according to claim 11, wherein the computer readable program, when executed on a computer, causes the computer to amend a fault associated with a faulty state, or with a potentially faulty state, if the state of the virtual machine is faulty or potentially faulty.

17. The computer program product according to claim 11, wherein the computer readable program, when executed on a computer, causes the computer to amend a fault by performing a virtual migration of the virtual machine.

18. The computer program product according to claim 11, wherein the computer readable program, when executed on a computer, causes the computer to evaluate the state of the virtual machine in response to multiple samples of monitored information acquired during a long monitoring period.

19. The computer program product according to claim 11, wherein the computer readable program, when executed on a computer, causes the computer to generate classes of states in response to statistical characteristics of long term behavior patterns of the virtual machine.

21. The system according to claim 19 wherein the out of band monitor is adapted to locate information representative of resource utilization by the virtual machine.

22. The system according to claim 19 wherein the out of band monitor is adapted to locate information representative of virtual machine requests to allocate resources.

23. The system according to claim 19 wherein the statistical classifier is adapted to define state classes by applying a machine learning process and to utilize the state classes during the appliance of the statistical classification process.

24. The system according to claim 19 wherein the statistical classifier is adapted to classify the state of the virtual machine as being in one out of a functional states class, a faulty state class and a potentially faulty state class.

25. The system according to claim 19 wherein the statistical classifier is adapted to define the state of the virtual machine as being a faulty state or a potentially faulty state if the state of the virtual machine is not in a functional state class.

26. The system according to claim 19 further adapted to generate an alert if the state of the virtual machine is faulty or potentially faulty.

27. The system according to claim 19 further adapted to amend a fault associated with a faulty state, or with a potentially faulty state, if the state of the virtual machine is faulty or potentially faulty.

28. The system according to claim 19 wherein the statistical classifier is adapted to evaluate the state of the virtual machine in response to multiple samples of monitored information acquired during a long monitoring period.

29. The system according to claim 19 wherein the statistical classifier is adapted to generate classes of states in response to statistical characteristics of long term behavior patterns of the virtual machine.

31. The method according to claim 29 wherein the monitoring comprises monitoring, by an out of band monitor included within the hypervisor, information exchanged between the virtual machine and the hypervisor.

32. The method according to claim 29 wherein the monitoring comprises monitoring information representative of resource utilization by the virtual machine.

33. The method according to claim 29 further comprising defining state classes by applying a machine learning process and utilizing the state classes during the appliance of the statistical classification process.

34. The method according to claim 32 further comprising emulating a functional state of the virtual machine and generating a functional state class, by a machine learning entity.

35. The method according to claim 29 wherein the applying comprises defining the state of the virtual machine as being a faulty state or a potentially faulty state if the state of the virtual machine is not in a functional state class.

20. A system having virtual machine evaluation capabilities, the system comprises:

an out of band monitor adapted to monitor information exchanged between the virtual machine and a hypervisor; and

a statistical classifier adapted to evaluate a state of the virtual machine by applying a statistical classification process to at least a portion of the monitored information.

30. A method for providing a service to a customer over a network, the method comprises:

receiving, over a network, a request from a customer to evaluate a state of a virtual machine;

monitoring, using an out of band monitor, information exchanged between the virtual machine and a hypervisor;

evaluating a state of the virtual machine by applying a statistical classification process to at least a portion of the monitored information; and

sending, over a network and to the customer, an indication about the state of the virtual machine.