Temperature aware energy-efficient task scheduling strategies for mapreduce

Bin LIAO; Tao ZHANG; Jiong YU; Ji LIU; tong YINLu; Gang GUO

doi:10.11959/j.issn.1000-436x.2016008

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Temperature aware energy-efficient task scheduling strategies for mapreduce

Academic paper | 更新时间：2024-06-05

- Temperature aware energy-efficient task scheduling strategies for mapreduce
- Journal on Communications Vol. 37, Issue 1, Pages: 61-75(2016)
- 作者机构：
  
  1. 新疆财经大学统计与信息学院，新疆乌鲁木齐 830012
  2. 新疆医科大学医学工程技术学院，新疆乌鲁木齐 830011
  3. 新疆大学软件学院，新疆乌鲁木齐 830008
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61562078);The National Natural Science Foundation of China(61262088);The National Natural Science Foundation of China(71261025);The Doctoral Research Foundation of Xinjiang University of Finance and Economics(2015BS007)
- DOI：10.11959/j.issn.1000-436x.2016008
  CLC： TP393.09
- Online First：2016-01，
  
  Published：25 January 2016
- 稿件说明：
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Bin LIAO, Tao ZHANG, Jiong YU, et al. Temperature aware energy-efficient task scheduling strategies for mapreduce[J]. Journal on Communications, 2016, 37(1): 61-75.
DOI：

Bin LIAO, Tao ZHANG, Jiong YU, et al. Temperature aware energy-efficient task scheduling strategies for mapreduce[J]. Journal on Communications, 2016, 37(1): 61-75. DOI： 10.11959/j.issn.1000-436x.2016008.

摘要

现有的FIFO、Fair、Capacity、LATE及Deadline Constraint等MapReduce任务调度器的主要区别在于队列与作业选择策略的不同，而任务选择策略基本相同，都是将数据的本地性(data-locality)作为选择的主要因素，忽略了对TaskTracker 当前温度状态的考虑。实验表明，当TaskTracker处于高温状态时，一方面使CPU利用率变高，导致节点能耗增大，任务处理速度下降，导致任务完成时间增加；另一方面，易发的宕机现象将直接导致任务的失败，推测执行(speculative execution)机制容易使运行时任务被迫中止。继而提出温度感知的节能任务调度策略，将节点 CPU 温度纳入任务调度的决策信息，以避免少数高温任务执行节点对作业整体进度的影响。实验结果表明，算法能够避免任务分配到高温节点，从而有效地缩短作业完成时间，减小作业执行能耗，提高系统稳定性。

Abstract

The main difference among the existing MapReduce task chedulers such as FIFO

Fair

Capacity

LATE and Deadline Constraint is their choice of operation strat ue and job. On the count of the task selection strategies of these task schedulers are basically the same

taking the data-locality as the key factor of selection

they all ignore the current state of the temperature of the TaskTracker. The experiments show that when the TaskTracker is in a state of high temperature it will cause some negative results. On one hand

utilization of the CPU becomes higher

which means more energy is consumed at each node. And as a result of task processing speed dropping off

more time will be needed to complete the same task.On the other hand

the prone downtime phenomenon will ectly lead to the failure of the task

and speculative execution mechanism is easy to make the runtime task suspend. Temperature aware energy-efficient task scheduling strategy is put forward to solve the problem. CPU temperature of the node was put into the task scheduling deci-sion-making information to avoid bad impact on the overall ogress of the job form the task execution nodes with a high temperature. The experimental results show that the algorithm can avoid allocating task to high temperature nodes

which ef-fectively shorten the job completion time

reduce energy consumption of job execution and improve system stability.

关键词

Keywords

references

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