CAPACITY RISK MANAGEMENT FOR VIRTUAL MACHINES

• US 20150378764A1
• Filed: 06/30/2014
• Published: 12/31/2015
• Est. Priority Date: 06/30/2014
• Status: Active Grant

• Current Assignee: BMC Software Incorporated (KKR & Co., Inc.)
• Original Assignee: BMC Software Incorporated (KKR & Co., Inc.)
• Inventors: SIVASUBRAMANIAN, Jeyashree, APTE, Sudheer
• Granted Patent: US 9,483,299 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 11/30 Monitoring
• CPC Class Codes: G06F 11/301 where the computing system is a virtual computing platform, e.g. logically partitioned systems virtual machines G06F9/45533; logical partitioning of resources G06F9/5077
• CPC Class Codes: G06F 11/3065 Monitoring arrangements determined by the means or processing involved in reporting the monitored data error or fault reporting or logging G06F11/0766
• CPC Class Codes: G06F 11/3086 where the reporting involves the use of self describing data formats, i.e. metadata, markup languages, human readable formats
• CPC Class Codes: G06F 11/3419 by assessing time
• CPC Class Codes: G06F 11/3433 for load management allocation of a server based on load conditions G06F9/505; load rebalancing G06F9/5083; redistributing the load in a network by a load balancer H04L67/1029
• CPC Class Codes: G06F 11/3442 for planning or managing the needed capacity
• CPC Class Codes: G06F 11/3452 Performance evaluation by statistical analysis
• CPC Class Codes: G06F 2009/45591 Monitoring or debugging support
• CPC Class Codes: G06F 2201/81 Threshold
• 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 2209/5019 Workload prediction
• CPC Class Codes: G06F 2209/5022 Workload threshold
• CPC Class Codes: G06F 9/45558 Hypervisor-specific management and integration aspects
• CPC Class Codes: G06F 9/5061 Partitioning or combining of resources
• CPC Class Codes: G06F 9/5077 Logical partitioning of resources; Management or configuration of virtualized resources specific details on emulation or internal functioning of virtual machines G06F9/455
• CPC Class Codes: G06F 9/5083 Techniques for rebalancing the load in a distributed system

Abstract

An access data collector collects access assignment data characterizing active access assignment operations of a hypervisor in assigning host computing resources among virtual machines for use in execution of the virtual machines. Then, a capacity risk indicator calculator calculates a capacity risk indicator characterizing a capacity risk of the host computing resources with respect to meeting a prospective capacity demand of the virtual machines, based on the access assignment data

Claims

1. A system including instructions stored on a non-transitory computer readable storage medium and executable by at least one semiconductor processor, the system comprising:

an access data collector configured to cause the at least one semiconductor processor to collect access assignment data characterizing active access assignment operations of a hypervisor in assigning host computing resources among virtual machines for use in execution of the virtual machines; and

a capacity risk indicator calculator configured to cause the at least one semiconductor processor to calculate a capacity risk indicator characterizing a capacity risk of the host computing resources with respect to meeting a prospective capacity demand of the virtual machines, based on the access assignment data.

2. The system of claim 1, wherein the capacity risk indicator calculator includes at least one pressure index calculator configured to calculate at least one pressure index characterizing the active access assignments of the hypervisor relative to a corresponding threshold, and further wherein the capacity risk indicator calculator is configured to calculate the capacity risk indicator, based on the at least one pressure index.

3. The system of claim 2, wherein the capacity risk indicator calculator includes at least two pressure index calculators configured to calculate corresponding pressure indexes, and is configured to calculate the capacity risk indicator based on a weighted combination of the corresponding pressure indexes.

4. The system of claim 2, comprising a utilization monitor configured to collect utilization data characterizing utilization levels of the host computing resources, wherein the capacity risk indicator calculator is configured to calculate the capacity risk indicator based on the utilization data and the at least one pressure index.

5. The system of claim 4, wherein the capacity risk indicator calculator is configured to calculate the capacity risk indicator including adding the at least one pressure index as a penalty to a corresponding utilization level of the utilization levels.

6. The system of claim 2, wherein the at least one pressure index calculator includes a memory pressure index calculator, and the at least one pressure index includes a memory pressure index characterizing memory assignments of the hypervisor relative to a memory threshold.

7. The system of claim 6, wherein the memory pressure index is calculated based on at least one of ballooning and swapping operations of the hypervisor included in the memory assignments.

8. The system of claim 2, wherein the at least one pressure index calculator includes a central processor unit (CPU) pressure index calculator, and the at least one pressure index includes a CPU pressure index characterizing a wait time experienced by the virtual machines for access to CPU resources of the host computing resources, relative to a CPU threshold.

9. The system of claim 2, wherein the at least one pressure index calculator includes a storage pressure index calculator, and the at least one pressure index includes a storage pressure index characterizing an input/output (I/O) latency experienced by the virtual machines for access to storage resources of the host computing resources, relative to a storage threshold.

10. The system of claim 1, wherein the active access assignment data characterizes inclusion of at least one of the virtual machines within at least one access queue of the hypervisor.

11. A method comprising:

collecting, using at least one semiconductor processor, access assignment data characterizing active access assignment operations of a hypervisor in assigning host computing resources among virtual machines for use in execution of the virtual machines; and

calculating, using at least one semiconductor processor, a capacity risk indicator characterizing a capacity risk of the host computing resources with respect to meeting a prospective capacity demand of the virtual machines, based on the access assignment data.

12. The method of claim 11, wherein the calculating comprises:

calculating at least one pressure index characterizing the active access assignments of the hypervisor relative to a corresponding threshold; and

calculating the capacity risk indicator, based on the at least one pressure index.

13. The method of claim 12, comprising collecting utilization data characterizing utilization levels of the host computing resources, wherein the calculating of the capacity risk indicator includes calculating the capacity risk indicator based on the utilization data and the at least one pressure index.

14. The method of claim 12, wherein the at least one pressure index characterizes overhead activity of the hypervisor in assigning the host computing resources among the virtual machines.

15. The method of claim 11, wherein the capacity risk indicator characterizes a saturation level of at least a portion of the host computing resources.

16. A computer program product including instructions recorded on a non-transitory computer readable storage medium and configured, when executed by at least one semiconductor processor, to cause the at least one semiconductor processor to:

collect access assignment data characterizing active access assignment operations of a hypervisor in assigning host computing resources among virtual machines for use in execution of the virtual machines; and

calculate a capacity risk indicator characterizing a capacity risk of the host computing resources with respect to meeting a prospective capacity demand of the virtual machines, based on the access assignment data.

17. The computer program product of claim 16, wherein the instructions, when executed, are further configured to cause the at least one semiconductor processor to:

calculate at least one pressure index characterizing the active access assignments of the hypervisor relative to a corresponding threshold, and

calculate the capacity risk indicator, based on the at least one pressure index.

18. The computer program product of claim 17, wherein the instructions, when executed, are further configured to cause the at least one semiconductor processor to:

calculate at least two pressure indexes; and

calculate the capacity risk indicator based on a weighted combination of the at least two pressure indexes.

19. The computer program product of claim 17, wherein the instructions, when executed, are further configured to cause the at least one semiconductor processor to

collect utilization data characterizing utilization levels of the host computing resources;

and

calculate the capacity risk indicator based on the utilization data and the at least one pressure index.

20. The computer program product of claim 17, wherein the at least one pressure index includes:

a memory pressure index characterizing memory assignments of the hypervisor relative to a memory threshold;

a central processor unit (CPU) pressure index characterizing a wait time experienced by the virtual machines for access to CPU resources of the host computing resources, relative to a CPU threshold; and

a storage pressure index characterizing an input/output (I/O) latency experienced by the virtual machines for access to storage resources of the host computing resources, relative to a storage threshold.