DATA CENTER BATCH JOB QUALITY OF SERVICE CONTROL

• US 20100306776A1
• Filed: 05/28/2009
• Published: 12/02/2010
• Est. Priority Date: 05/28/2009
• Status: Active Grant

• Current Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Original Assignee: Palo Alto Research Center, Inc. (Xerox Holdings Corp.)
• Inventors: Greene, Daniel H.
• Granted Patent: US 8,190,744 B2
• Time in Patent Office: Days
• US Class Current: 718/101
• 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: G06Q 10/10 Office automation; Time management
• CPC Class Codes: H04L 41/5003 Managing SLA; Interaction between SLA and QoS
• CPC Class Codes: H04L 41/5012 determining service availability, e.g. which services are available at a certain point in time
• CPC Class Codes: H04L 41/5016 based on statistics of service availability, e.g. in percentage or over a given time
• CPC Class Codes: H04L 41/5019 Ensuring fulfilment of SLA
• CPC Class Codes: H04L 43/08 Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
• CPC Class Codes: H04L 43/091 Measuring contribution of individual network components to actual service level
• CPC Class Codes: H04L 47/805 QOS or priority aware
• CPC Class Codes: H04L 47/821 Prioritising resource allocation or reservation requests
• CPC Class Codes: H04L 47/822 Collecting or measuring resource availability data
• CPC Class Codes: H04L 47/83 based on usage prediction

Abstract

A machine-controlled method can include determining an extended interval quality of service (QoS) specification for a batch job and determining a remaining data center resource requirement for the batch job based on the extended interval QoS specification. The machine-controlled method can also include determining an immediate QoS specification for the batch job based on the remaining data center resource requirement.

Claims

1. A machine-controlled method, comprising:

determining an extended interval quality of service (QoS) specification for a given batch job;

for at least a first one of a plurality of time slices, determining a remaining data center resource requirement for the batch job based at least in part on the extended interval QoS specification; and

determining an immediate QoS specification for the batch job based at least in part on the remaining data center resource requirement.

2. The machine-controlled method of claim 1, further comprising, for at least a second one of the plurality of time slices, determining the immediate QoS specification based at least in part on a progress of the batch job, wherein the second one of the plurality of time slices occurs after the first one of the plurality of time slices and before a completion time of the batch job.

3. The machine-controlled method of claim 1, wherein determining the extended interval QoS specification comprises determining an allowed probability of failure q.

4. The machine-controlled method of claim 1, wherein determining the immediate QoS specification comprises determining a data center resource reservation S.

5. The machine-controlled method of claim 1, wherein determining the immediate interval QoS specification comprises determining an immediate interval QoS failure tolerance p.

6. The machine-controlled method of claim 4, wherein determining the immediate QoS specification comprises increasing the data center resource reservation S responsive to a determination that a progress of the batch job is slower than a predetermined amount.

7. The machine-controlled method of claim 4, wherein determining an immediate QoS specification comprises decreasing the data center resource reservation S responsive to a determination that a progress of the batch job is faster than a predetermined amount.

8. The machine-controlled method of claim 5, further comprising adjusting the immediate interval QoS failure tolerance p for at least a second one of the plurality of time slices based on a progress of the batch job, wherein the second one of the plurality of time slices occurs after the first one of the plurality of time slices and before a completion time of the batch job.

9. The machine-controlled method of claim 8, wherein adjusting the immediate interval QoS failure tolerance p comprises decreasing the immediate interval QoS failure tolerance p responsive to a determination that the progress of the batch job is slower than a predetermined amount.

10. The machine-controlled method of claim 8, wherein adjusting the immediate interval QoS failure tolerance p comprises increasing the immediate interval QoS failure tolerance p responsive to a determination that the progress of the batch job is faster than a predetermined amount.

11. The machine-controlled method of claim 1, wherein determining the remaining data center resource requirement comprises estimating an amount of data center resources needed to complete the batch job based at least in part on registration information for a data center customer.

12. The machine-controlled method of claim 1, wherein determining the remaining data center resource requirement comprises determining a stochastic model of data center resources needed to complete the batch job.

13. The machine-controlled method of claim 4, wherein determining the immediate interval QoS specification further comprises determining an immediate interval QoS failure tolerance p.

14. The machine-controlled method of claim 13, further comprising adjusting the data center resource reservation S and the immediate interval QoS failure tolerance p for at least a second one of the plurality of time slices based on a progress of the batch job, wherein the second one of the plurality of time slices occurs after the first one of the plurality of time slices and before a completion time of the batch job.

15. A data center management system, comprising:

a data center customer profile corresponding to a data center customer, the data center customer profile comprising;

a service level agreement (SLA); and

a data center resource usage model;

a batch job quality of service (QoS) control module operable to determine an immediate QoS specification for a batch job of the data center customer; and

a data center resource optimization module operable to determine a data center resource allocation for the data center customer based at least in part on the data center customer profile and on the immediate QoS specification.

16. The data center management system of claim 15, further comprising a data center customer cost determination module operable to determine a data center customer cost representing a cost to the data center of providing data center resources to the data center customer.

17. The data center management system of claim 16, wherein the batch job QoS control module is operable to determine the immediate QoS specification for the batch job based at least in part on the data center customer cost.

18. The data center management system of claim 15, wherein the batch job QoS control module is further operable to adjust the immediate QoS specification for the batch job responsive to a progress of the batch job.

19. The data center management system of claim 18, further comprising a data center management interface operable to alert a data center manager to the adjustment of the immediate QoS specification for the batch job.

20. A machine-controlled method, comprising:

determining an extended interval quality of service (QoS) specification for a data center batch job, wherein determining the extended interval QoS specification comprises determining an allowed probability of failure q;

determining a remaining data center resource requirement for the data center batch job based at least in part on the extended interval QoS specification; and

determining an immediate QoS specification for the data center batch job based at least in part on the remaining data center resource requirement, wherein determining the immediate QoS specification comprises;

determining a data center resource reservation S; and

determining a failure tolerance p; and

adjusting at least one of the data center resource reservation S and the failure tolerance p based at least in part on a progress of the batch job and the allowed probability of failure q.