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.
