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Energy-Efficient task scheduling in cloud data center- A temperature aware approach

机译：云数据中心中的节能任务调度 - 一种温度意识的方法

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Most of the previous studies based on task scheduling deals with either optimizing computing energy or performance upgradation only. However with advancement of cloud data center the users requirements are being diversified that is not achieved through traditional scheduling algorithm. One of the demand is to minimize cost related to cooling the data center. From the literature survey we found that the cooling energy cost half millions dollar to computing energy cost. In order to provide cost-effective executions in cloud environment, appropriate task scheduling strategy is necessary. Temperature affects not only the reliability but also the performance, power and cost of the embedded system. This paper proposes a thermalaware task allocation and scheduling algorithm for cloud data center. The task scheduling is designed in such a way that not only minimizes computation cost, it also reduces cooling. The rigorous simulation has been performed and compared with state of the art algorithms. The experimental results show that the thermalaware task scheduling algorithm outperforms compared to other strategies.
机译：基于任务调度的大多数研究涉及优化计算能源或性能升级。然而，随着云数据中心的进步，用户需求正在多元化，这是通过传统调度算法实现的。需求之一是最大限度地减少与冷却数据中心相关的成本。从文献调查来看，我们发现冷却能量成本为数百万美元到计算能源成本。为了在云环境中提供具有成本效益的执行，需要适当的任务调度策略。温度不仅影响了嵌入式系统的性能，功率和成本。本文提出了云数据中心的Themalaware任务分配和调度算法。任务调度设计以这样的方式，不仅可以最大限度地减少计算成本，还可以减少冷却。已经进行了严格的模拟，并与最先进的算法进行了比较。实验结果表明，与其他策略相比，Thermalaware任务调度算法优于胜过。

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