METHOD FOR DEPLOYING STORAGE SYSTEM RESOURCES WITH LEARNING OF WORKLOADS APPLIED THERETO

• US 20170242729A1
• Filed: 02/24/2016
• Published: 08/24/2017
• Est. Priority Date: 02/24/2016
• Status: Active Grant

• Current Assignee: ProphetStor Data Services, Inc.
• Original Assignee: ProphetStor Data Services, Inc.
• Inventors: CHEN, Wen Shyen, HUANG, Ming Jen
• Granted Patent: US 10,013,286 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 9/50 Allocation of resources, e.g. of the central processing unit [CPU]
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/048 Fuzzy inferencing
• CPC Class Codes: H04L 41/5025 by proactively reacting to service quality change, e.g. by reconfiguration after service quality degradation or upgrade

Abstract

A method for deploying storage system resources with learning of workloads applied to a storage system is disclosed. The method utilizes state-action fuzzy rules and action-reward fuzzy rules for the storage system to learn a best dynamic deployment of resources from the parameters of workloads. No more human interference is required. It reduces work loading of the storage system administrator and enhances performance of the storage system.

Claims

1. A method for deploying storage system resources with learning of workloads applied to a storage system, comprising the steps of:

A. setting state-action fuzzy rules for deviated percentages of parameters of a storage system from SLAs (Service Level Agreement) of workloads under specific scenarios and adjustments of resources, and action-reward fuzzy rules for adjustments of resources and reward values, wherein the scenario is a specific relation between a deviated direction of the parameters and change of a corresponding resource;

B. providing an experience matrix where entries in each row refer to reward values under a specific state, and entries in each column refer to reward values for an adjustment of at least one resource, wherein the experience matrix is re-zeroed for all entries and the state is a specific combination of deviated percentages of parameters;

C. collecting current deviated percentages of parameters from one of the workloads, and providing predicted deviated percentages of parameters for said workload in a plurality of later time points;

D. randomly choosing one scenario and processing fuzzification, fuzzy inference, and result aggregation by inputting the collected deviated percentages of parameters of said workload to membership functions of the state-action fuzzy rules of the chosen scenario to have a first action range;

E. defuzzifying the first action range to have an adjusted amount for at least one resource;

F. executing the adjusted amount in the storage system for the workload;

G. processing fuzzification, fuzzy inference, and result aggregation by inputting the provided predicted percentages of parameters of said workload to membership functions of the action-reward fuzzy rules to have a reward range;

H. defuzzifying the reward range to have a deviated reward value;

I. for the rows of the experience matrix corresponding to the states of the predicted deviated percentages of parameters, searching for the maximum value in each of rows;

J. accumulating a deviated reward value and chosen values in a previous time point from the step I as an updated reward value and replacing the entry of the experience matrix under the state of the deviated percentages of parameters and action amount of the previous time point with the updated reward value;

K. repeating step C to step J until each entry satisfies a converged condition, wherein the step D is processed for all workloads in turns; and

L. choosing a row in the experience matrix corresponding to observed deviated percentages of parameters and executing the specific adjustment of the resources corresponding to the maximum value among the entries in the row in the storage system.

2. The method according to claim 1, wherein if there is no previous time point for the step I, the updated reward value is set to be 0.

3. The method according to claim 1, wherein the parameter comprises latency, throughput, CPU (Central Processing Unit) load, memory usage, or TOPS (Input/Output Per Second).

4. The method according to claim 1, wherein the resource comprises read SSD (Solid State Disk) cache size, write SSD cache size, CPU share, RAM (Random Access Memory) size, block IO (Input/Output) bandwidth, number of VM (Virtual Machine), or number of provisioned storage.

5. The method according to claim 1, wherein the number of time points is 4 or 5.

6. The method according to claim 1, wherein the converged condition is for all entries, the reward value stays the same, or varies within a limited interval, where the interval remains the same or decreases with time, after a learning stage has passed by.

7. The method according to claim 6, wherein the learning stage is longer than 5 hours.

8. The method according to claim 6, wherein a mean value between the upper and lower bounds is used to compare with other reward values in the same row to find out a maximum one for the adjustment of resource if the reward value in each entry does not keep the same with time.

9. The method according to claim 1, further comprising a step E1 after the step E:

E1. keeping the amount of the resources and raising an alarm if an amount of the adjustment of one resource exceeds the remaining capacity of the resource.

10. The method according to claim 1, wherein a closer value of adjustment of the resource is chosen if one action causes an amount of adjustment of one resource falls between two consequent values of adjustments of the resource.

11. The method according to claim 1, wherein setting state-action fuzzy rules in the step A is obtained by the sub-steps of:

defining a linguistic value for each parameter;

constructing membership functions to depict degrees of all linguistic value; and

constructing the state-action fuzzy rules based on the membership functions.

12. The method according to claim 1, wherein action-reward fuzzy rules in the step A is obtained by the sub-steps of:

defining a linguistic value for each range of changed percentage of resource, each changed percentage of parameters and each amount of reward value;

constructing membership functions to depict degrees of all ranges of the deviated percentage of parameters, the adjustments of resources, and the reward values; and

constructing the action-reward fuzzy rules based on the membership functions.

13. The method according to claim 1, wherein the deviated reward value is given higher if a certain amount of at least one resource is released from the workload processed in the step F.

14. The method according to claim 1, wherein a gamma value times to each reward values before accumulating with the deviated reward value in the step J.

15. The method according to claim 14, wherein the gamma value ranges from 0 to 1.