IP Library › Granted Patent US 12,656,842
Granted Patent B2
US 12,656,842 · App. 19/009,969 · Granted Jun 16, 2026

Energy aware processing load distribution system and method

Inventor: Kanad Ghose (Vesta, NY)
Assignee: The Research Foundation for The State University of New York
G06F1/206B03B1/00B03B5/02B03B5/58B03B11/00G05D23/19G06F1/20G06F1/3203G06F1/3206G06F1/3209G06F1/3228G06F9/45558G06F9/4893G06F9/5094G06F13/409G06K19/0723G06K19/07705G06K19/07722H04L69/329H05K7/20836G06F2009/4557Y02D10/00
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Quick Facts
Patent No.
US 12,656,842
App. No.
19/009,969
Granted
Jun 16, 2026
Kind
B2
Abstract

A method for controlling a data center, comprising a plurality of server systems, each associated with a cooling system and a thermal constraint, comprising: a concurrent physical condition of a first server system; predicting a future physical condition based on a set of future states of the first server system; dynamically controlling the cooling system in response to at least the input and the predicted future physical condition, to selectively cool the first server system sufficient to meet the predetermined thermal constraint; and controlling an allocation of tasks between the plurality of server systems to selectively load the first server system within the predetermined thermal constraint and selectively idle a second server system, wherein the idle second server system can be recruited to accept tasks when allocated to it, and wherein the cooling system associated with the idle second server system is selectively operated in a low power consumption state.

Claims (52)

1 . A method of operating a datacenter comprising plurality of racks, each rack having a plurality of servers, comprising:

maintaining empirical thermal models responsive to activity of each server;

predicting a thermal trend of each server dependent on at least the empirical thermal models and a workload of each server;

predicting a thermal trend of each rack dependent on at least the predicted thermal trend of each server in a respective rack;

defining a separate thermal limit for each rack; and

allocating tasks from a queue of unexecuted tasks to respective ones of the plurality of servers, in dependence on at least the thermal limit for each rack, the predicted thermal trend of each server, an actual server utilization, and a predicted energy efficiency of the processing of the allocated tasks and a cooling required for each rack; and

proactively adjusting the cooling for a respective rack with a cooling system, prior to execution at least one allocated task, responsive to at least the predicted thermal trend of the respective rack, and the allocated tasks to the respective ones of the plurality of servers within the respective rack.

2 . The method according to claim 1 , further comprising:

determining an energy budget for each server as a function of at least:

the predicted thermal trend of the respective server;

actual utilization of the respective server; and

temperature and airflow measurements from sensors; and

controlling the server to process the tasks allocated to the server within the determined energy budget.

3 . The method according to claim 1 , wherein the empirical models responsive to the activity of each server comprise hardware activity counters and software-readable registers.

4 . The method according to claim 1 , wherein each server has a controllable performance characteristic for performing tasks, and the tasks are allocated to the plurality of servers in dependence on characteristics of each task and the controllable performance characteristic for performing tasks of each server.

5 . The method according to claim 1 , wherein the proactively adjusting the cooling for a respective rack based on an algorithm that presumes that a hot server is more efficiently cooled than a cool server.

6 . The method according to claim 1 , wherein the tasks are allocated by a global scheduler for the plurality of racks.

7 . The method according to claim 1 , wherein an independently controllable cooling system is provided for each rack to provide the proactive cooling.

8 . A method of operating a plurality of servers in a plurality of racks, comprising:

maintaining an empirical thermal model of energy dissipation for each server, responsive to activity counters and software-accessible registers of each server;

predicting a thermal trend of each server dependent on at least the empirical thermal model of energy dissipation of each server, and a workload of tasks allocated to each server; and

allocating tasks from a queue of unexecuted tasks to respective ones of the plurality of servers, in dependence on at least a predicted effect of the allocated task on each server according to the empirical thermal model of energy dissipation for each server, an operating temperature of each server, and an energy efficiency of processing of the allocated tasks and the controllable proactive cooling for each rack dependent on at least the allocation of tasks from the queue of unexecuted tasks to respective ones of the plurality of servers; and

proactively adjusting a cooling for each rack, responsive to at least a predicted thermal trend of each rack and the allocation of tasks, wherein the predicted thermal trend of each rack is dependent on at least the tasks to be performed by the servers in each rack and the empirical models of energy dissipation of the servers in each rack.

9 . The method according to claim 8 , further comprising:

determining an energy budget for each server, dependent on at least the predicted thermal trend of each server, an actual utilization of each server, and temperature and airflow measurements from sensors; and

controlling each server to process the tasks allocated to the respective server within the determined energy budget.

10 . The method according to claim 8 , wherein each server has a controllable performance characteristic for performing tasks, and the tasks are allocated to the plurality of servers in dependence on characteristics of each task and the controllable performance characteristic for performing tasks of each server.

11 . The method according to claim 8 , wherein each server has a controllable performance characteristic for performing tasks, and the allocation of tasks to respective ones of the plurality of servers is further in dependence on characteristics of each task and the controllable performance characteristic for performing tasks of each server.

12 . The method according to claim 8 , wherein the tasks are allocated by a global scheduler to the plurality of servers in the plurality of racks.

13 . A datacenter, comprising:

a plurality of racks;

a plurality of servers in each rack;

a cooling system configured to cool each rack; and

a scheduling system configured to:

maintain empirical thermal models responsive to activity of each server;

predict a thermal trend of each server dependent on at least the empirical thermal models, and a utilization of each server; and

allocate tasks from a queue of unexecuted tasks to the plurality of servers, in dependence on at least a predicted thermal effect of processing the task on each server, and

a predicted energy efficiency of the processing of the allocated task and cooling of each rack; and

adjust the cooling system for each rack to proactively cool each rack based on at least one task in the unexecuted queue of tasks prior to execution, responsive to at least a predicted thermal trend of each rack comprising the predicted thermal trends of the plurality of servers in each rack, and the empirical thermal models.

14 . The datacenter according to claim 13 , wherein the scheduling system is further configured to:

determine an energy budget for each server, dependent on at least the predicted thermal trend of each server, an actual utilization of each server, and temperature and airflow measurements from sensors; and

allocate the tasks further in dependence on the determined energy budget for each server; and

each server is configured to process the tasks allocated to the respective server within the determined energy budget.

15 . The datacenter according to claim 13 , wherein the cooling system comprises a separately controlled cooling system for a proper subset of the plurality of racks.

16 . The datacenter according to claim 13 , wherein the plurality of servers and the plurality of racks are configured such that the efficiency of processing of the allocated tasks and cooling a respective rack increases as workload increases from idle until the respective rack approaches the thermal limit for the respective rack.

17 . The datacenter according to claim 13 , wherein each server has a controllable performance characteristic for performing tasks, and the scheduling system is further configured to allocate the tasks in dependence on characteristics of each task and the controllable performance characteristic for performing tasks of each server.

18 . The datacenter according to claim 15 , wherein the empirical models responsive to the activity of each server comprise hardware activity counters and software-readable registers.

19 . The datacenter according to claim 13 , wherein the predicted thermal trend of each server is further dependent on the tasks to be performed by the respective server based on:

the allocation of tasks from the queue of unexecuted tasks to the plurality of servers,

a cooling of the respective server, and

a performance setting of the respective server.

20 . The datacenter according to claim 13 , further comprising a thermal sensor, wherein the cooling system is controlled to both independently proactively cool each rack dependent on at least the predicted thermal trends, and reactively cool each rack based on a signal from the thermal sensor.

Continuity (7)
Continuation 18387052 · Nov 5, 2023
Continuation 17543122 · Dec 6, 2021
Division 15694459 · Sep 1, 2017
Continuation 14153540 · Jan 13, 2014
Continuation 12841169 · Jul 21, 2010
Provisional Application 61227361 · Jul 21, 2009
Related Publication 20250138609A1 · May 1, 2025
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