Method and system for monitoring and analyzing quality of service in a storage system

• US 9,542,103 B2
• Filed: 11/07/2014
• Issued: 01/10/2017
• Est. Priority Date: 01/14/2014
• Status: Active Grant

• Current Assignee: NetApp, Inc.
• Original Assignee: NetApp, Inc.
• Inventors: Faulkner, Kevin, Fairbanks, Leon, Wu, Siu, Pai, Vinod Vasant, Pai, Ulhas
• Primary Examiner(s): Yi, David X
• Assistant Examiner(s): PUCHE, TAHILBA O
• Application Number:
  US14/535,587
• Publication Number:
  US 20150199139A1
• Time in Patent Office: 795 Days
• US Class Current: 1/1
• CPC Class Codes: G06F 11/3034 where the computing system component is a storage system, e.g. DASD based or network based digital input from or digital output to record carriers G06F3/06; digital recording or reproducing G11B20/18; 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], H04L67/1097
• CPC Class Codes: G06F 11/3419 by assessing time
• CPC Class Codes: G06F 11/3452 Performance evaluation by statistical analysis
• CPC Class Codes: G06F 11/3485 for I/O devices
• CPC Class Codes: G06F 3/0613 in relation to throughput
• CPC Class Codes: G06F 3/0653 Monitoring storage devices or systems
• CPC Class Codes: G06F 3/067 Distributed or networked storage systems, e.g. storage area networks [SAN], network attached storage [NAS]
• CPC Class Codes: G06F 3/0689 Disk arrays, e.g. RAID, JBOD

Abstract

Methods and systems for monitoring quality of service (QOS) data for a plurality of storage volumes are provided. QOS data is collected for the plurality of storage volumes and includes a response time in which each of the plurality of storage volumes respond to an input/output (I/O) request. The process determines an average of N collected QOS data points at any given time; and iteratively analyzes each QOS data point to detect if a step-up or a step-down function has occurred, where a step-up function represents an unpredictable increase in value of a data point and a step-down function is an unpredictable decrease in value of the data point. A subset of the N QOS data points based on when the step-up function or step-down function occurs is selected for analysis and an expected range for future QOS data based on the subset of the N QOS data points is generated.

Claims

1. A machine implemented method, comprising:

collecting quality of service (QOS) data for a plurality of storage volumes from a storage operating system of a storage system having a plurality of resources;

wherein the QOS data includes a response time in which each of the plurality of storage volumes respond to an input/output (I/O) request;

a wait time for each I/O request at each of the resources of the storage system; and

a number of visits for each I/O request at each of the resources of the storage system;

determining an average of N collected QOS data points at any given time;

iteratively analyzing each QOS data point to detect if a step-up or a step-down function has occurred, where a step-up function represents an unpredictable increase in value of a data point and a step-down function is an unpredictable decrease in value of the data point;

selecting a subset of the N QOS data points based on when the step-up function or step-down function occurs;

generating an expected range for future QOS data based on the subset of the N QOS data points, where the expected range is a range of measured performance activity of a workload over a period of time for predicting future QOS data behavior of the storage volumes; and

monitoring QOS data for each storage volume for determining whether a current QOS data for each storage volume is within the expected range.

2. The method of claim 1, wherein the expected range provides a dynamic threshold value to a processor executable performance manager for predicting an incident associated with any of the plurality of storage volumes.

3. The method of claim 1, wherein the expected range provides a dynamic threshold value to a processor executable performance manager for determining if any of the resource is in contention between any of the plurality of storage volumes resulting in a storage volume becoming a victim due to overuse by a bully storage volume.

4. The method of claim 1, wherein the resources at the storage system are categorized as a service center that services a request and QOS data for the service center includes a number of visits, wait time per visit and service time and a delay center that only moves the request to a next destination whose QOS data includes the number of visits and the wait time per visit.

5. The method of claim 4, wherein the resources include a processor for a network module that interfaces with clients and operates as a service center and a processor for a storage module that operates as a service center and manages storage devices where information is stored.

15. The method of claim 4, wherein a performance manager tracks QOS data for each service center and delay center to predict the expected range.

6. A non-transitory machine readable storage medium having stored thereon instructions for performing a method, comprising machine executable code which when executed by at least one machine, causes the machine to:

collect quality of service (QOS) data for a plurality of storage volumes from a storage operating system of a storage system having a plurality of resources;

wherein the QOS data includes a response time in which each of the plurality of storage volumes respond to an input/output (I/O) request;

a wait time for each I/O request at each of the resources of the storage system; and

a number of visits for each I/O request at each of the resources of the storage system;

determine an average of N collected QOS data points at any given time;

iteratively analyze each QOS data point to detect if a step-up or a step-down function has occurred, where a step-up function represents an unpredictable increase in value of a data point and a step-down function is an unpredictable decrease in value of the data point;

select a subset of the N QOS data points based on when the step-up function or step-down function occurs;

generate an expected range for future QOS data based on the subset of the N QOS data points, where the expected range is a range of measured performance activity of a workload over a period of time for predicting future QOS data behavior of the storage volumes; and

monitor QOS data for each storage volume for determining whether a current QOS data for each storage volume is within the expected range.

7. The storage medium of claim 6, wherein the expected range provides a dynamic threshold value to a processor executable performance manager for predicting an incident associated with any of the plurality of storage volumes.

8. The storage medium of claim 6, wherein the expected range provides a dynamic threshold value to a processor executable performance manager for determining if any of the resource is in contention between any of the plurality of storage volumes resulting in a storage volume becoming a victim due to overuse by a bully storage volume.

9. The storage medium of claim 6, wherein the resources at the storage system are categorized as a service center that services a request and QOS data for the service center includes a number of visits, wait time per visit and service time and a delay center that only moves the request to a next destination whose QOS data includes the number of visits and the wait time per visit.

10. The storage medium of claim 9, wherein the resources include a processor for a network module that interfaces with clients and operates as a service center and a processor for a storage module that operates as a service center and manages storage devices where information is stored.

16. The storage medium of claim 9, wherein a performance manager tracks QOS data for each service center and delay center to predict the expected range.

11. A system, comprising:

a memory containing machine readable medium comprising machine executable code having stored thereon instructions; and

a processor module coupled to the memory, the processor module configured to execute the machine executable code to;

collect quality of service (QOS) data for a plurality of storage volumes from a storage operating system of a storage system having a plurality of resources;

wherein the QOS data includes a response time in which each of the plurality of storage volumes respond to an input/output (I/O) request;

a wait time for each I/O request at each of the resources of the storage system; and

a number of visits for each I/O request at each of the resources of the storage system;

determine an average of N collected QOS data points at any given time;

iteratively analyze each QOS data point to detect if a step-up or a step-down function has occurred, where a step-up function represents an unpredictable increase in value of a data point and a step-down function is an unpredictable decrease in value of the data point;

select a subset of the N QOS data points based on when the step-up function or step-down function occurs;

generate an expected range for future QOS data based on the subset of the N QOS data points, where the expected range is a range of measured performance activity of a workload over a period of time for predicting future QOS data behavior of the storage volumes; and

monitor QOS data for each storage volume for determining whether a current QOS data for each storage volume is within the expected range.

12. The system of claim 11, wherein the expected range provides a dynamic threshold value to a processor executable performance manager for predicting an incident associated with any of the plurality of storage volumes.

13. The system of claim 11, wherein the expected range provides a dynamic threshold value to a processor executable performance manager for determining if any of the resource is in contention between any of the plurality of storage volumes resulting in a storage volume becoming a victim due to overuse by a bully storage volume.

14. The system of claim 11, wherein the resources at the storage system are categorized as a service center that services a request and QOS data for the service center includes a number of visits, wait time per visit and service time and a delay center that only moves the request to a next destination whose QOS data includes the number of visits and the wait time per visit.

17. The system of claim 14, wherein a performance manager tracks QOS data for each service center and delay center to predict the expected range.

18. The storage system of claim 14, wherein the resources include a processor for a network module that interfaces with clients and operates as a service center.

19. The system of claim 14, wherein the resources include a processor for a storage module that operates as a service center and manages storage devices where information is stored.