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Dynamic resource allocation with optimized task scheduling and improved power management in cloud computing

机译:具有优化任务调度的动态资源分配和云计算中的电源管理

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摘要

Cloud computing is one among the emerging platforms in business, IT enterprise and mobile computing applications. Resources like Software, CPU, Memory and I/O devices etc. are utilized and charged as per the usage, instead of buying it. A Proper and efficient resource allocation in this dynamic cloud environment becomes the challenging task due to drastic increment in cloud usage. Various promising technologies have been developed to improve the efficiency of resource allocation process. But still there is some incompetency in terms of task scheduling and power consumption, when the system gets overloaded. So an energy efficient task scheduling algorithm is required to improve the efficiency of resource allocation process. In this paper an improved task scheduling and an optimal power minimization approach is proposed for efficient dynamic resource allocation process. Using prediction mechanism and dynamic resource table updating algorithm, efficiency of resource allocation in terms of task completion and response time is achieved. This framework brings an efficient result in terms of power reduction since it reduces the power consumption in data centers. The proposed approach gives accurate values for updating resource table. An efficient resource allocation is achieved by an improved task scheduling technique and reduced power consumption approach. The Simulation result gives 8% better results when comparing to other existing methods.
机译:云计算是业务,IT企业和移动计算应用程序中的新兴平台中的一个。使用软件,CPU,内存和I / O设备等资源,并根据用法充电,而不是购买它。由于云使用中的急剧增量,这种动态云环境中的适当和高效的资源分配成为挑战性的任务。已经制定了各种有希望的技术来提高资源分配过程的效率。但是当系统超载时,仍然存在任务调度和功耗方面存在一些不称职。因此,需要节能任务调度算法来提高资源分配过程的效率。本文提出了一种改进的任务调度和最佳功率最小化方法,以实现有效的动态资源分配过程。使用预测机制和动态资源表更新算法,实现了任务完成和响应时间的资源分配效率。该框架在减少电力降低的情况下,为减少数据中心的功耗来带来有效的结果。该方法提供了更新资源表的准确值。通过改进的任务调度技术和降低的功耗方法来实现高效的资源分配。与其他现有方法相比,仿真结果为8%的结果提供了8%。

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