System and method to reduce platform power utilization

• US 20060129675A1
• Filed: 11/22/2004
• Published: 06/15/2006
• Est. Priority Date: 11/22/2004
• Status: Abandoned Application

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Zimmer, Vincent J., Rothman, Michael A.
• Application Number:
  US10/996,010
• Publication Number:
  US 20060129675A1
• Time in Patent Office: Days
• US Class Current: 709/225
• CPC Class Codes: G06F 1/3209 Monitoring remote activity, e.g. over telephone lines or network connections
• CPC Class Codes: H04L 61/4511 using domain name system [DNS]
• 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 67/1017 based on a round robin mechanism

Abstract

In some embodiments, the invention involves utilizing an enhanced round robin DNS (eRR-DNS) scheme to maximize throughput while minimizing power consumption in a network of computers. In at least one embodiment, the present invention is intended to balance the work load of network platforms in order to minimize or optimize power utilization. Other embodiments are described and claimed.

Claims

1. A system, comprising:

a front-end server to assign jobs to a plurality of back-end servers, wherein the front-end server determines which of the plurality of back-end servers is to receive a next job based on an analysis of throughput and power consumption of the plurality of back-end servers; and

the plurality of back-end servers to receive jobs assigned by the front end-server, wherein the plurality of back-end servers are communicatively coupled to the front-end server, and wherein each of the plurality of back-end servers at least one of reduces and increases power consumption when requested to do so by the front-end server.

2. The system as recited in claim 1, wherein the front-end server assigns jobs using a round-robin technique enhanced by power policy criteria.

3. The system as recited in claim 2, wherein the power policy criteria maximizes transactions per unit time and minimizes power consumption of the plurality of back-end servers.

4. The system as recited in claim 3, wherein the power consumption of a back-end server is communicated to the front-end server via an out-of-band protocol.

5. The system as recited in claim 3, wherein the power consumption of a back-end server is communicated to the front-end server via an in-band protocol.

6. The system as recited in claim 1, wherein reducing power consumption comprises at least one power reduction means selected from a group of power reduction means consisting of putting a back-end server into sleep mode and throttling down clock speed of a back-end server.

7. The system as recited in claim 1, further comprising a plurality of sensors communicatively coupled to a baseboard management controller (BMC), wherein the BMC is communicatively coupled to the front-end server to communicate readings from the plurality of sensors.

8. The system as recited in claim 1, wherein the front-end server receives policy parameters from a remote agent.

9. A method comprising:

setting a set-point P as criteria for desired power consumption in a network of a plurality of back-end servers;

registering the plurality of back-end servers as available for job assignment;

determining a current load and a current power consumption for each back-end server registered;

receiving a job request from a remote party;

determining a back-end server of the plurality of back-end servers to which the received job request is to be assigned, the assignment based on an analysis of transaction rates and power consumption of each back-end server.

10. The method as recited in claim 9, wherein determining a current load and a current power consumption comprises receiving sensor data from each back-end server via a baseboard management controller coupled to each respective back-end server.

11. The method as recited in claim 10, wherein determining a current load and a current power consumption further comprises identifying a transaction rate for each back-end server using job start and job end times for previous job assignments.

12. The method as recited in claim 11, wherein identifying a transaction rate for each back-end server further comprises weighting job assignments by difficulty.

13. The method as recited in claim 9, wherein analysis of transaction rates and power consumption of each back-end server maximize a number of transactions per server and minimize overall power consumption, wherein a the plurality of back-end servers are weighted by power and throughput efficiency to determine a best server to assign the job request.

14. A machine accessible medium having instructions that when accessed cause the machine to:

set a set-point P as criteria for desired power consumption in a network of a plurality of back-end servers;

register the plurality of back-end servers as available for job assignment;

determine a current load and a current power consumption for each back-end server registered;

receive a job request from a remote party;

determine a back-end server of the plurality of back-end servers to which the received job request is to be assigned, the assignment based on an analysis of transaction rates and power consumption of each back-end server.

15. The medium as recited in claim 14, wherein determining a current load and a current power consumption comprises receiving sensor data from each back-end server via a baseboard management controller coupled to each respective back-end server.

16. The medium as recited in claim 15, wherein determining a current load and a current power consumption further comprises identifying a transaction rate for each back-end server using job start and job end times for previous job assignments.

17. The method as recited in claim 16, wherein identifying a transaction rate for each back-end server further comprises weighting job assignments by difficulty.

18. The method as recited in claim 14, wherein analysis of transaction rates and power consumption of each back-end server maximize a number of transactions per server and minimize overall power consumption, wherein a the plurality of back-end servers are weighted by power and throughput efficiency to determine a best server to assign the job request.