Cloud computing platform has emerged to be a promising computing paradigm of recent time. Various applications from different domains having rigid deadline constraints are deployed in the cloud system for their respective benefits. Energy-efficient execution of these applications, meeting their deadline constraints is a challenge. Most of the existing research on the energy-efficient scheduling of these applications in the cloud domain consider a linear relationship between the energy consumption and the resource utilization of the system, and they focus on maximizing the utilization of resources to reduce the active number of computing nodes to minimize energy consumption. In this paper, we first devise a power consumption model for the cloud system which considers both the static and dynamic components of it and assumes a nonlinear relationship with utilization. Then we introduce the concept of urgent points in case of tasks having deadline in the context of a heterogeneous cloud computing environment. Then we propose two energy-efficient scheduling approaches, named UPS and UPS-ES designed based on the urgent points of the tasks and two threshold values of the host utilization. Extensive simulation experiments are conducted both for synthetic tasksets and Google cloud tracelogs. The results are compared with a state of the art scheduling policy and found that our policies perform significantly better than them, with an energy reduction of up to 42% while the deadline constraints of all the tasks are met.

云计算平台已成为最近有希望的计算范例。具有严格截止期限约束的来自不同域的各种应用程序被部署在云系统中，以实现各自的优势。这些应用程序的节能执行，满足其截止日期的限制是一个挑战。在云领域，有关这些应用程序的节能调度的大多数现有研究都考虑了能耗与系统资源利用率之间的线性关系，并且他们专注于最大限度地利用资源以减少活动计算量。节点以最小化能耗。在本文中，我们首先为云系统设计一个功耗模型，该模型同时考虑了其静态和动态组成部分，并假设其与利用率之间存在非线性关系。然后我们介绍在异构云计算环境中，任务具有截止日期的情况下的紧急点。然后我们提出了两种节能调度方法，分别根据任务的紧急点和主机利用率的两个阈值设计了UPS和UPS-ES。针对合成任务集和Google云跟踪日志都进行了广泛的仿真实验。将结果与最先进的调度策略进行比较，发现我们的策略性能明显优于它们，在满足所有任务的最后期限约束的同时，节能高达42％。