METHODS AND SYSTEMS FOR MAKING EFFECTIVE USE OF SYSTEM RESOURCES

• US 20150235144A1
• Filed: 05/04/2015
• Published: 08/20/2015
• Est. Priority Date: 12/10/2010
• Status: Active Grant

• Current Assignee: Salesforce.com, Inc.
• Original Assignee: Salesforce.com, Inc.
• Inventors: Gusev, Andrey, Yang, Ronald, Hansma, Scott, Arbizu, Alan, Collins, Jesse
• Granted Patent: US 10,192,169 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/2455 Query execution
• CPC Class Codes: G06F 2209/5019 Workload prediction
• CPC Class Codes: G06F 9/5011 the resources being hardware resources other than CPUs, Servers and Terminals
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: H04L 43/0876 Network utilisation, e.g. volume of load or congestion level
• CPC Class Codes: H04L 47/125 by balancing the load, e.g. traffic engineering
• CPC Class Codes: H04L 49/90 Buffering arrangements
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/1001 for accessing one among a plurality of replicated servers
• CPC Class Codes: H04L 67/1008 based on parameters of servers, e.g. available memory or workload monitoring of computer activity G06F11/30
• CPC Class Codes: H04L 67/1014 based on the content of a request
• CPC Class Codes: H04L 69/329 in the application layer [OSI layer 7]

Abstract

Methods and systems for making effective use of system resources. A plurality of requests for access to a resource are received. Each request has an associated group of features. The group of features for each request is analyzed to collect observations about the plurality of requests. A function to predict an outcome of a subsequent request is generated based on the observations. Resources are allocated to service the subsequent request based on the function.

Claims

1. A method comprising:

receiving a plurality of requests for access to a resource with a training module of a database system provided by one or more computing devices, each request having an associated group of features to be mapped to classes in a database environment;

generating a statistical model with the training module for each database instance within the database environment;

predicting an execution time and a number of rows for a subsequent request based on the statistical model; and

allocating resources in the database environment to service the subsequent request based on the predicted execution time and number of rows.

2. The method of claim 1 wherein the plurality of requests comprises database queries.

3. The method of claim 1 wherein the group of features comprises:

a number of nested joins in the request;

a number of hash joins in the request; and

a sum of cardinalities for hash joins in the request.

4. The method of claim 1 wherein the group of features comprises:

a cardinality estimate of a main table;

a storage count of the main table; and

a selectivity estimate of the main table.

5. The method of claim 1 wherein the group of features comprises a number of secondary queries in the request.

6. The method of claim 1 wherein the group of features comprises:

an application server processor state; and

an application server memory state.

7. The method of claim 1 wherein the group of features comprises:

a database processor state; and

a number of tables used.

8. A non-transitory computer readable medium having stored there on instructions that, when executed, cause one or more processors to:

receive a plurality of requests for access to a resource with a training module of a database system provided by one or more computing devices, each request having an associated group of features to be mapped to classes in a database environment;

generate a statistical model with the training module for each database instance within the database environment;

predict an execution time and a number of rows for a subsequent request based on the statistical model; and

allocate resources in the database environment to service the subsequent request based on the predicted execution time and number of rows.

9. The non-transitory computer-readable medium of claim 8 wherein the plurality of requests comprises database queries.

10. The non-transitory computer-readable medium of claim 8 wherein the group of features comprises:

a number of nested joins in the request;

a number of hash joins in the request; and

a sum of cardinalities for hash joins in the request.

11. The non-transitory computer-readable medium of claim 8 wherein the group of features comprises:

a cardinality estimate of a main table;

a storage count of the main table; and

a selectivity estimate of the main table.

12. The non-transitory computer-readable medium of claim 8 wherein the group of features comprises a number of secondary queries in the request.

13. The non-transitory computer-readable medium of claim 8 wherein the group of features comprises:

an application server processor state; and

an application server memory state.

14. The non-transitory computer-readable medium of claim 8 wherein the group of features comprises:

a database processor state; and

a number of tables used.

15. A system comprising one or more server systems communicatively coupled with one or more user systems to receive a plurality of requests for access to a resource with a training module of a database system provided by one or more computing devices, each request having an associated group of features to be mapped to classes in a database environment, to generate a statistical model with the training module for each database instance within the database environment, to predict an execution time and a number of rows for a subsequent request based on the statistical model, and to allocate resources in the database environment to service the subsequent request based on the predicted execution time and number of rows.

16. The system of claim 15 wherein the plurality of requests comprises database queries.

17. The system of claim 15 wherein the group of features comprises:

a number of nested joins in the request;

a number of hash joins in the request; and

a sum of cardinalities for hash joins in the request.

18. The system of claim 15 wherein the group of features comprises:

a cardinality estimate of a main table;

a storage count of the main table; and

a selectivity estimate of the main table.

19. The system of claim 15 wherein the group of features comprises a number of secondary queries in the request.

20. The system of claim 15 wherein the group of features comprises:

an application server processor state; and

an application server memory state.

21. The system of claim 15 wherein the group of features comprises:

a database processor state; and

a number of tables used.