Cloud Task Scheduling Using Nature Inspired Meta-Heuristic Algorithm

被引：0

作者：

Adil, Syed Hasan ^{[1
]}

Raza, Kamran ^{[1
]}

Ahmed, Usman ^{[2
]}

Ali, Syed Saad Azhar ^{[3
]}

Hashmani, Manzoor ^{[1
]}

机构：

[1] Iqra Univ, Dept Comp Sci, Karachi, Pakistan

[2] King Khalid Univ, Coll Comp Sci, Abha, Saudi Arabia

[3] Univ Teknol Petronas, Dept Elect Engn, Bandar Seri Iskandar, Malaysia

来源：

2015 INTERNATIONAL CONFERENCE ON OPEN SOURCE SYSTEMS & TECHNOLOGIES (ICOSST) | 2015年

关键词：

Task Scheduling; Cloud Computing; Meta-Heuristic; Particle Swarm Optimization; Brute Force Approach; Hadoop; MapReduce;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper we investigate the application of Meta-Heuristic for cloud task scheduling on Hadoop. Hadoop is an open source implementation of MapReduce framework which extensively used for processing computational intensive jobs on huge amount of data over multi-node cluster. In order to achieve an efficient execution schedule, the scheduling algorithm requires to determining the order and the node on which tasks will be executed. A scheduling algorithm uses execution time, order of task arrival and location of data (i.e., assign task to the node which contains the required data) to determine the best execution schedule. We use Particle Swarm Optimization (PSO) to determine the tasks execution schedule and compare with tasks schedules obtained from other techniques like Genetic Algorithm (GA), Brute Force (BF) algorithm, First In First Out (FIFO) algorithm and Delay Scheduling Policy (DSP) algorithm. The results of this study prove the significance of PSO algorithm for cloud task scheduling over other algorithms.

引用

页码：158 / 164

页数：7

共 19 条

[1]

Adil S. H., 2013, LIFE SCI J, V10

[2]

Borthakur D, 2007, The hadoop distributed file system: Architecture and design

[3] MapReduce: A Flexible Data Processing Tool [J].