Title
Deadline Constrained Cloud Computing Resources Scheduling through an Ant Colony System Approach.
Abstract
Cloud computing resources scheduling is essential for executing workflows in the cloud platform because it relates to both execution time and execution cost. In this paper, we adopt a model that optimizes the execution cost while meeting deadline constraints. In solving this problem, we propose an Improved Ant Colony System (IACS) approach featuring two novel strategies. Firstly, a dynamic heuristic strategy is used to calculate a heuristic value during an evolutionary process by taking the workflow topological structure into consideration. Secondly, a double search strategy is used to initialize the pheromone and calculate the heuristic value according to the execution time at the beginning and to initialize the pheromone and calculate heuristic value according to the execution cost after a feasible solution is found. Therefore, the proposed IACS is adaptive to the search environment and to different objectives. We have conducted extensive experiments based on workflows with different scales and different cloud resources. We compare the result with a particle swarm optimization (PSO) approach and a dynamic objective genetic algorithm (DOGA) approach. Experimental results show that IACS is able to find better solutions with a lower cost than both PSO and DOGA do on various scheduling scales and deadline conditions.
Year
DOI
Venue
2015
10.1109/ICCCRI.2015.14
ICCCRI
Keywords
Field
DocType
cloud computing, resource scheduling, deadline constrained, task scheduling, ant colony system
Particle swarm optimization,Mathematical optimization,Heuristic,Computer science,Scheduling (computing),Real-time computing,Earliest deadline first scheduling,Ant colony,Dynamic priority scheduling,Genetic algorithm,Cloud computing,Distributed computing
Conference
Citations 
PageRank 
References 
4
0.44
19
Authors
8
Name
Order
Citations
PageRank
Zong-Gan Chen1754.37
Zhi-hui Zhan2178986.72
Hai-Hao Li3100.92
Ke-jing Du4353.10
Jing-hui Zhong538033.00
Yong Wee Foo6152.19
Yun Li7104057.24
Jun Zhang82491127.27