Deadline Constrained Cloud Computing Resources Scheduling through an Ant Colony System Approach

Chen, Z.-G., Zhan, Z.-H., Li, H.-H., Du, K.-J., Zhong, J.-H., Foo, Y. W., Li, Y. and Zhang, J. (2015) Deadline Constrained Cloud Computing Resources Scheduling through an Ant Colony System Approach. In: ICCCRI 2015: International Conference on Cloud Computing Research and Innovation, Singapore, 26-27 Oct 2015, pp. 112-119. (doi: 10.1109/ICCCRI.2015.14)

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.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Li, Professor Yun
• Authors: Chen, Z.-G., Zhan, Z.-H., Li, H.-H., Du, K.-J., Zhong, J.-H., Foo, Y. W., Li, Y., and Zhang, J.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Copyright Holders: Copyright © 2016 Institute of Electrical and Electronics Engineers
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher