Title :
PACO: A Period ACO Based Scheduling Algorithm in Cloud Computing
Author :
Weifeng Sun ; Ning Zhang ; Haotian Wang ; Wenjuan Yin ; Tie Qiu
Author_Institution :
Sch. of Software Technol., Dalian Univ. of Technol., Dalian, China
Abstract :
Tasks scheduling problem in cloud computing is NP-hard, and it is difficult to attain an optimal solution, so we can use intelligent optimization algorithms to approximate the optimal solution, such as ant colony optimization algorithm. In order to solve the task scheduling problem in cloud computing, a period ACO_based scheduling algorithm (PACO) has been proposed in this paper. PACO uses ant colony optimization algorithm in cloud computing, with the first proposed scheduling period strategy and the improvement of pheromone intensity update strategy. The experiments results show that, PACO has a good performance both in makespan and load balance of the whole cloud cluster.
Keywords :
ant colony optimisation; cloud computing; computational complexity; scheduling; NP-hard problem; PACO; ant colony optimization algorithm; cloud cluster; cloud computing; intelligent optimization algorithms; period ACO based scheduling algorithm; tasks scheduling problem; Algorithm design and analysis; Cloud computing; Clustering algorithms; Heuristic algorithms; Scheduling; Scheduling algorithms; ant colony algorithm; cloud computing; scheduling period; task scheduling;
Conference_Titel :
Cloud Computing and Big Data (CloudCom-Asia), 2013 International Conference on
Conference_Location :
Fuzhou
Print_ISBN :
978-1-4799-2829-3
DOI :
10.1109/CLOUDCOM-ASIA.2013.85