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Towards energy-aware task scheduling (EATS) framework for divisible-load applications in cloud computing infrastructure

机译：迈向云计算基础架构中可分负载应用程序的能源感知任务调度（EATS）框架

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摘要

Cloud computing is an emerging technology which is rapidly being adopted by industries, government and academia. However, the power consumption of the underlying data center has a critical impact for its known impact on the environment and the Cloud electricity bills. Therefore, there is a need for scheduling framework in the Cloud which takes into account the optimization of the power consumption of the Cloud. In this paper, we propose an Energy-Aware Task Scheduling (EATS) cloud computing framework which is responsible to schedule users' tasks considering the energy consumption when running those tasks. This paper describes our framework, and report on workload classifications of energy consumption. The results reveal that CPU-bound applications are the most consumer of energy, and therefore should be accounted for in any framework of energy-efficient scheduling, and that strategies based on shutdowns and startups should be avoided.
机译：云计算是一种新兴技术，正在迅速被工业界，政府和学术界所采用。但是，基础数据中心的功耗对其已知的对环境和云电费的影响至关重要。因此，需要考虑到云的功耗优化的云中的调度框架。在本文中，我们提出了一种能源感知任务调度（EATS）云计算框架，该框架负责在运行任务时考虑能耗的情况来调度用户的任务。本文介绍了我们的框架，并报告了能耗的工作负载分类。结果表明，受CPU限制的应用程序是最消耗能源的，因此应在任何节能调度框架中加以考虑，并且应避免基于关闭和启动的策略。

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