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Data center task temperature prediction and scheduling method based on RBF neural network

A neural network and data center technology, applied in electrical digital data processing, digital data processing components, error detection/correction, etc., to reduce average temperature, reduce energy consumption, and ensure safety

Active Publication Date: 2019-02-22
XI AN JIAOTONG UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

[0013] The technical problem to be solved by the present invention is to provide a data center task temperature prediction and scheduling method based on RBF neural network to solve the problem of balancing and reducing the overall temperature when the data center is working normally. Avoid hot spots when the data center is running under high load

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  • Data center task temperature prediction and scheduling method based on RBF neural network
  • Data center task temperature prediction and scheduling method based on RBF neural network
  • Data center task temperature prediction and scheduling method based on RBF neural network

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Embodiment Construction

[0053] The invention provides a data center task temperature prediction and scheduling method based on the RBF neural network. The temperature of the incoming task is predicted based on the RBF neural network, and then the task allocation is based on the predicted temperature.

[0054] Before implementing the control method, for large-scale data centers, according to the principle of thermal locality, some servers that are concentrated together and related to large temperature changes are partitioned. All partitions should cover the entire computer room. Deploy the temperature control module.

[0055] see figure 1 , a temperature control method for data center task temperature prediction based on RBF neural network, the main work focuses on the following three parts:

[0056] S1. Task temperature prediction module:

[0057] Carry out temperature prediction modeling on the arrival task of a specific server, and use the RBF neural network prediction model. First, the task is ...

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Abstract

The invention discloses a data center task temperature prediction and scheduling method based on an RBF neural network, which includes adopting the RBF neural network to establish a task temperature prediction model; establishing a temperature-controlled load balancing module, determining a safe temperature and an alert temperature of the server, and judging and selecting with any server Si; the monitoring feedback module real-time monitors and feeds server operating temperature back to the temperature control load balancing module, the temperature control load balancing module is used for scheduling control. The invention carries out task scheduling through active temperature prediction, and has better flexibility than the method of determining task scheduling only based on feedback temperature. By setting two active scheduling strategies in different temperature ranges, the energy consumption can be reduced and the safe operation of the data center can be ensured.

Description

technical field [0001] The invention belongs to the technical field of data center safety and energy saving, and in particular relates to a method for predicting and scheduling task temperature of a data center based on an RBF neural network. Background technique [0002] With the rapid development of data centers, the security requirements and high energy consumption of data centers are becoming more and more prominent. The potential safety hazards and high energy consumption problems caused by poor temperature control in data centers have become a research focus and difficulty. [0003] The first is the issue of potential safety hazards. At present, the power density of cabinets in data centers continues to increase. A survey shows that up to 1 / 10 of the cabinets operate at a temperature higher than the allowable range recommended by the guidelines for equipment reliability. The frequent occurrence of hot spots will affect the reliability and performance of IT equipment. ...

Claims

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Application Information

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IPC IPC(8): G06F9/48G06F11/30G06F1/20G06F1/329
CPCG06F1/20G06F1/329G06F9/4881G06F11/3058Y02D10/00
Inventor 伍卫国徐一轩王思敏苏远崎王今雨
Owner XI AN JIAOTONG UNIV
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