Energy-Aware Grid Scheduling of Independent Tasks and Highly Distributed Data
Energy-Aware Grid Scheduling of Independent Tasks and Highly Distributed Data
复制标题
独立任务和高度分布式数据的能源感知网格调度
DOI：
10.1109/fit.2013.46
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
S. Khan
中科院分区：
文献类型：
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作者：
J. Kolodziej;M. Szmajduch;Tahir Maqsood;S. Madani;N. Min;S. Khan
数据感知调度在当今的大规模计算系统中已经成为一个主要的复杂的研究课题。这个问题变得更具挑战性的数据存储和访问时，从许多高度分布式的服务器和能源效率被视为一个主要的调度目标。本文将网格环境下的独立批调度问题转化为一个以生产周期和能耗为调度准则的双目标最小化问题。我们使用动态电压和频率缩放（DVFS）模型来减少系统资源执行任务所使用的累积功率能量。我们开发了一个通用的逻辑网络拓扑结构和策略的基础上的睡眠链路的自适应链路速率（ALR）的开/关技术的数据传输。两个发达国家的能源意识的网格搜索是基于遗传算法（GAs）的框架与精英和斗争的替代机制，并在静态和动态模式下的四个网格大小的情况下进行了经验评估。仿真结果表明，所提出的并行执行的水平，这是足以保持所需的质量水平。
Data-aware scheduling in today's large-scale computing systems has become a major complex research issue. This problem becomes even more challenging when data is stored and accessed from many highly distributed servers and energy-efficiency is treated as a main scheduling objective. In this paper we approach the independent batch scheduling in grid environment as a bi-objective minimization problem with make span and energy consumption as the scheduling criteria. We used the Dynamic Voltage and Frequency Scaling (DVFS) model for reducing the cumulative power energy utilized by the system resources for tasks executions. We developed for data transmission a general logical network topology and policy based on the sleep link-based Adaptive Link Rate (ALR) on/off technique. Two developed energy-aware grid schedulers are based on genetic algorithms (GAs) frameworks with elitist and struggle replacement mechanisms and were empirically evaluated for four grid size scenarios in static and dynamic modes. The simulation results show that the proposed schedulers perform to a level that is sufficient to maintain the desired quality levels.