DYNAMIC METERING ADJUSTMENT FOR SERVICE MANAGEMENT OF COMPUTING PLATFORM

• US 20160094401A1
• Filed: 10/29/2015
• Published: 03/31/2016
• Est. Priority Date: 09/30/2014
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Sailer, Anca, Schulz, Charles O., Segal, Alla, Anwar, Ali, Kochut, Andrzej
• Granted Patent: US 10,467,036 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 11/00 Error detection; Error correction; Monitoring error detection, correction or monitoring in information storage based on relative movement between record carrier and transducer G11B20/18; monitoring, i.e. supervising the progress of recording or reproducing G11B27/36; in static stores G11C29/00
• CPC Class Codes: G06F 11/3006 where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems multiprogramming arrangements G06F9/46; allocation of resources G06F9/50
• 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 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 9/45558 Hypervisor-specific management and integration aspects
• CPC Class Codes: H04L 41/0843 based on generic templates
• CPC Class Codes: H04L 41/0896 Bandwidth or capacity management, i.e. automatically increasing or decreasing capacities flow or congestion control using dynamic resource allocation, e.g. in-call renegotiation, H04L47/76
• CPC Class Codes: H04L 41/0897 by horizontal or vertical scaling of resources, or by migrating entities, e.g. virtual resources or entities
• CPC Class Codes: H04L 41/142 using statistical or mathematical methods
• CPC Class Codes: H04L 43/024 by adaptive sampling
• CPC Class Codes: H04L 43/0817 by checking functioning
• CPC Class Codes: H04L 43/16 Threshold monitoring

Abstract

Systems and methods are provided for dynamic metering adjustment for service management of a computing platform. For example, a plurality of virtual machines are provisioned across a plurality of computing nodes of a computing platform. Data samples are collected for a metric that is monitored with regard to resource utilization in the computing platform by the virtual machines. The data samples are initially collected at a predefined sampling frequency. The data samples collected over time for the metric are analyzed to determine an amount of deviation in values of the collected data samples. A new sampling frequency is determined for collecting data samples for the metric based on the determined amount of deviation. The new sampling frequency is applied to collect data samples for the metric, wherein the new sampling frequency is less than the predefined sampling frequency.

Claims

1. A method for managing a computing platform, comprising:

provisioning a plurality of virtual machines across a plurality of computing nodes of a computing platform;

collecting data samples corresponding to a metric that is monitored with regard to resource utilization in the computing platform by the virtual machines, wherein the data samples are initially collected at a predefined sampling frequency;

analyzing the data samples collected over time for the metric to determine an amount of deviation in values of the collected data samples;

determining a new sampling frequency for collecting data samples for the metric based on the determined amount of deviation; and

applying the new sampling frequency to collect data samples for the metric, wherein the new sampling frequency is less than the predefined sampling frequency,

wherein the provisioning, collecting, analyzing, determining, and applying are steps that are implemented at least in part by a processor executing program code.

2. The method of claim 1, wherein the metric comprises one of CPU (central processing unit) usage, memory usage, TCP/IP connection rate, and page access per time

3. The method of claim 1, wherein analyzing the data samples collected over time for the metric to determine an amount of deviation in values of the collected data samples, comprises:

generating change point time series data by detecting changes in values of the collected data samples associated with the metric; and

converting the change point time series data into a sequences of symbols which encode a change behavior of the metric.

4. The method of claim 3, wherein determining a new sampling frequency for collecting data samples for the metric based on the determined amount of deviation, comprises:

clustering the sequences of symbols into tiers of metrics with similar sequences;

determining a new polling frequency for each of the tier based on the sequences of symbols that are included within the tier.

5. The method of claim 1, further comprising re-applying the predefined sampling frequency to collect data samples for the metric when there is one of (i) a detected change in behavior of the metric and (ii) a metric policy update for the metric.

6. The method of claim 1, further comprising assigning a metric policy to the metric based on values of one or more metric profile configuration items associated with the metric.

7. The method of claim 6, wherein determining a new sampling frequency for collecting data samples for the metric is further based on metric policy assigned to the metric.

8. The method of claim 6, wherein the metric policy comprises at least one of conservative sampling, conservative storage, aggregated storage, per tier sampling or a combination thereof, wherein the conservative sampling applies the predefined sampling frequency for the metric, wherein conservative storage implies that all collected data samples for the metric are stored, wherein aggregated storage implies that aggregate values of the collected data samples are stored, or that only changes in values of the collected data samples or stored, and wherein pier tier sampling implies that different sampling frequencies are applied for different behaviors of the metric.

9. The method of claim 6, wherein the one or more metric profile configuration items comprises one of an importance item, a usage item, a dependency item, or a combination thereof.

10. The method of claim 1, further comprising:

aggregating values of the collected data samples corresponding to the metric; and

storing the aggregated values of the collected data samples.

11. The method of claim 1, further comprising:

determining if the metric is correlated to another metric;

if the metric is determined to be correlated to another metric, the comparing each new collected data sample for the given metric to a last stored data sample; and

storing the new data sample only if the value of the new sample is different from the value of the last stored data sample.

12. An article of manufacture comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method for managing a computing platform, the method comprising:

provisioning a plurality of virtual machines across a plurality of computing nodes of a computing platform;

collecting data samples corresponding to a metric that is monitored with regard to resource utilization in the computing platform by the virtual machines, wherein the data samples are initially collected at a predefined sampling frequency;

analyzing the data samples collected over time for the metric to determine an amount of deviation in values of the collected data samples;

determining a new sampling frequency for collecting data samples for the metric based on the determined amount of deviation; and

applying the new sampling frequency to collect data samples for the metric, wherein the new sampling frequency is less than the predefined sampling frequency,

wherein the provisioning, collecting, analyzing, determining, and applying are steps that are implemented at least in part by a processor executing program code.

13. The article of manufacture of claim 12, wherein analyzing the data samples collected over time for the metric to determine an amount of deviation in values of the collected data samples, comprises:

generating change point time series data by detecting changes in values of the collected data samples associated with the metric; and

converting the change point time series data into a sequences of symbols which encode a change behavior of the metric.

14. The article of manufacture of claim 13, wherein determining a new sampling frequency for collecting data samples for the metric based on the determined amount of deviation, comprises:

clustering the sequences of symbols into tiers of metrics with similar sequences;

determining a new polling frequency for each of the tier based on the sequences of symbols that are included within the tier.

15. The article of manufacture of claim 12, further comprising assigning a metric policy to the metric based on values of one or more metric profile configuration items associated with the metric.

16. The article of manufacture of claim 15, wherein determining a new sampling frequency for collecting data samples for the metric is further based on metric policy assigned to the metric.

17. The article of manufacture of claim 15, wherein the metric policy comprises at least one of conservative sampling, conservative storage, aggregated storage, per tier sampling or a combination thereof, wherein the conservative sampling applies the predefined sampling frequency for the metric, wherein conservative storage implies that all collected data samples for the metric are stored, wherein aggregated storage implies that aggregate values of the collected data samples are stored, or that only changes in values of the collected data samples or stored, and wherein pier tier sampling implies that different sampling frequencies are applied for different behaviors of the metric, and wherein the one or more metric profile configuration items comprises one of an importance item, a usage item, a dependency item, or a combination.

18. The article of manufacture of claim 12, further comprising:

aggregating values of the collected data samples corresponding to the metric; and

storing the aggregated values of the collected data samples.

19. The article of manufacture of claim 12, further comprising:

determining if the metric is correlated to another metric;

if the metric is determined to be correlated to another metric, the comparing each new collected data sample for the given metric to a last stored data sample; and

storing the new data sample only if the value of the new sample is different from the value of the last stored data sample.

20. A computing system, comprising:

a plurality of computing nodes, each comprising a plurality of computing resources including memory and processors, wherein a plurality of virtual machines are provisioned across the plurality of computing nodes; and

wherein at least one computing mode executes a service management system that is configured to;

collect data samples corresponding to a metric that is monitored with regard to resource utilization in the computing platform by the virtual machines, wherein the data samples are initially collected at a predefined sampling frequency;

analyze the data samples collected over time for the metric to determine an amount of deviation in values of the collected data samples;

determine a new sampling frequency for collecting data samples for the metric based on the determined amount of deviation; and

apply the new sampling frequency to collect data samples for the metric, wherein the new sampling frequency is less than the predefined sampling frequency.