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中文摘要：
现有的FIFO、Fair、Capacity、LATE及Deadline Constraint等Map Reduce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Task Tracker当前温度状态的考虑。实验表明,当Task Tracker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。
英文摘要：
The main difference among the existing Map Reduce task schedulers such as FIFO, Fair, Capacity, LATE and Deadline Constraint is their choice of operation strategy of the queue 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 Task Tracker. The experiments show that when the Task Tracker 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 directly 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 decision-making information to avoid bad impact on the overall progress 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 effectively shorten the job completion time, reduce energy consumption of job execution and improve system stability.
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