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Self-learning load prediction based cluster on-demand starting method

A load forecasting and self-learning technology, which is applied in resource allocation, multi-programming devices, sustainable buildings, etc., can solve problems such as inability to improve accuracy, and achieve the effect of reducing energy consumption and power consumption level

Active Publication Date: 2012-08-01
NAT UNIV OF DEFENSE TECH
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the accuracy of the above prediction methods cannot be improved as the program runs

Method used

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  • Self-learning load prediction based cluster on-demand starting method
  • Self-learning load prediction based cluster on-demand starting method
  • Self-learning load prediction based cluster on-demand starting method

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Embodiment Construction

[0071] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0072] Such as figure 1 As shown, the cluster on-demand startup method based on self-learning load prediction of the present invention is through the following steps:

[0073] In this embodiment, the steps of the present invention are executed by setting corresponding software or hardware modules in the management node and computing node. Such as figure 2 As shown, the task receiving module, the load forecasting module and the on-demand starting module are set in the management node; the task running module and the frequency adjustment module are set in the computing node. Execute and implement the following steps through modularization:

[0074] 1. Task reception: The management node receives tasks from users. In this embodiment, this step is implemented by the task receiving module residing on the management node, specifically ...

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Abstract

The invention discloses a self-learning load prediction based cluster on-demand starting method, which includes the following steps of receiving a task from a user by management nodes and predicting load of the task according to historic load information; computing increased CPU (central processing unit) utilization rate after the task is dispatched to any one of computing nodes; searching and selecting an underloading computing node in the computing nodes; selecting a dormant computing node to wake up if the underloading computing node is not found; dispatching the task on the selected computing node; building a new thread to execute the task after the computing node receives the task; feeding back execution results of the task to the management nodes after the task is completed, feeding load information of the task back to the management nodes to be stored in historic load information; and setting the computing nodes without loading tasks in a certain time into dormancy by the management nodes at regular intervals. Power consumption of the integral cluster is reduced without affecting task performances by the self-learning load prediction based cluster on-demand starting method.

Description

technical field [0001] The invention relates to a computer cluster system, in particular to a method for managing power consumption of the cluster system, in particular to a method for starting a cluster on demand. Background technique [0002] Such as figure 1 As shown, the cluster system is composed of computing nodes, management nodes and the Internet. Among them, computing nodes are used to process task loads; management nodes monitor and control the status of computing nodes; the Internet connects computing nodes and management nodes together. The cluster system has the characteristics of high cost performance, stable performance and simple maintenance, so it has been widely used. [0003] In actual use, the utilization of the cluster is low. Not only expensive hardware devices are not well utilized, but additional power consumption is wasted. Therefore, in recent years, more and more attention has been paid to the power consumption management of the cluster. The m...

Claims

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Application Information

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IPC IPC(8): G06F9/50G06F9/46
CPCY02B60/142Y02B60/167Y02D10/00
Inventor 吴庆波谭郁松汤慧明戴华东杨沙洲任怡刘晓健易晓东
Owner NAT UNIV OF DEFENSE TECH
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