Christobel M, Tamil Selvi S, Benedict Shajulin
Ponjesly College of Engineering, Nagercoil, Tamil Nadu 629003, India.
National Engineering College, Kovilpatti, Tamil Nadu 628503, India.
ScientificWorldJournal. 2015;2015:791058. doi: 10.1155/2015/791058. Epub 2015 May 14.
One of the most significant and the topmost parameters in the real world computing environment is energy. Minimizing energy imposes benefits like reduction in power consumption, decrease in cooling rates of the computing processors, provision of a green environment, and so forth. In fact, computation time and energy are directly proportional to each other and the minimization of computation time may yield a cost effective energy consumption. Proficient scheduling of Bag-of-Tasks in the grid environment ravages in minimum computation time. In this paper, a novel discrete particle swarm optimization (DPSO) algorithm based on the particle's best position (pbDPSO) and global best position (gbDPSO) is adopted to find the global optimal solution for higher dimensions. This novel DPSO yields better schedule with minimum computation time compared to Earliest Deadline First (EDF) and First Come First Serve (FCFS) algorithms which comparably reduces energy. Other scheduling parameters, such as job completion ratio and lateness, are also calculated and compared with EDF and FCFS. An energy improvement of up to 28% was obtained when Makespan Conservative Energy Reduction (MCER) and Dynamic Voltage Scaling (DVS) were used in the proposed DPSO algorithm.
在实际的计算环境中，最重要且最关键的参数之一就是能量。将能量最小化能带来诸多益处，比如降低功耗、降低计算处理器的冷却速率、营造绿色环境等等。事实上，计算时间和能量是直接成正比的，而计算时间的最小化可能会带来具有成本效益的能量消耗。在网格环境中对任务包进行高效调度能在最短的计算时间内完成。本文采用了一种基于粒子最佳位置（pbDPSO）和全局最佳位置（gbDPSO）的新型离散粒子群优化（DPSO）算法来寻找高维的全局最优解。与最早截止时间优先（EDF）和先来先服务（FCFS）算法相比，这种新型DPSO能以最短的计算时间产生更好的调度，从而相对地降低能量。还计算了其他调度参数，如作业完成率和延迟，并与EDF和FCFS进行比较。在所提出的DPSO算法中使用完工时间保守能量降低（MCER）和动态电压缩放（DVS）时，能量提升高达28%。
