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Online energy consumption management method and apparatus of large-scale server cluster

A server cluster and energy management technology, which is applied in the fields of instruments, energy-saving computing, data processing power, etc., and can solve problems such as difficulty in solving planning problems online, large number of variables, and inability to achieve online real-time management.

Inactive Publication Date: 2017-02-22
SHANTOU UNIV
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  • Application Information

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Problems solved by technology

However, they all define planning variables for a single server. When the cluster scale is large, the number of variables is huge, which brings difficulties to the online solution of the planning problem, so that online real-time management cannot be achieved.

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  • Online energy consumption management method and apparatus of large-scale server cluster

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Embodiment Construction

[0041] In order to make the purpose, technical solution and advantages of the present invention clearer, the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0042] Such as figure 1 As shown, the present invention provides a method for online energy management of large-scale server clusters, including the following specific steps:

[0043] Step S1: Measure the load and power consumption corresponding to each frequency of each model server in the server cluster under the premise of ensuring that the CPU utilization rate is equal to a given target value, where the load is the average request rate.

[0044] Step S2: Predict the load of the next management cycle according to the load history information.

[0045] Step S3: Define variables for each server model in the server cluster, take the minimum energy consumption as the goal, take the load of all servers equal to the predicted load as the constraint conditi...

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Abstract

The embodiments of the invention disclose an online energy consumption management method of a large-scale server cluster. Under the condition that it is ensured that the CPU utilization rate of starting servers is equal to a given target value, servers in a cluster are dynamically managed according to load conditions so as to enable energy consumption of the cluster to be the lowest. Variables are defined for each server model in the cluster, an energy consumption management problem is described into a planning problem, and accordingly, the switch state, the work frequency and the load of each server are determined according to a solving result of the planning problem. The planning variable defining mode used by the invention can greatly reduce the quantity of the variables, even if the method is applied to a large-scale cluster, the planning problem can still be solved in an online mode. The method allows the server frequency to be switched between two adjacent discrete frequencies so as to prevent performance waste, and at the same time, the quantity of the servers needing to be switched between the two frequencies is also reduced as much as possible so that the switching cost is decreased. The invention further discloses an online energy consumption management apparatus of a large-scale server cluster.

Description

technical field [0001] The invention relates to the technical field of server energy consumption management, in particular to an online energy consumption management method and device for a large-scale server cluster. Background technique [0002] Network service operators usually deploy the scale of server clusters (referred to as clusters) according to the peak load, but the load during normal operation is usually much lower than the peak load, so the scale of the cluster must be dynamically managed according to the actual load status to save energy. Reduce operating costs. In order to maximize energy saving, many existing methods describe the switching and CPU frequency of each server in the cluster by defining variables, describe the energy management problem as a planning problem, and then solve it. However, they all define planning variables for a single server. When the cluster size is large, the number of variables is huge, which brings difficulties to the online so...

Claims

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Application Information

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IPC IPC(8): H04L12/24H04L29/08G06F1/32G06F11/34
CPCG06F1/3234G06F11/3433H04L41/0833H04L67/1008Y02D10/00
Inventor 熊智
Owner SHANTOU UNIV
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