Task Scheduling Optimization in Cloud Computing Applying Multi-Objective Particle Swarm Optimization

Optimizing the scheduling of tasks in a distributed heterogeneous computing environment is a nonlinear multi-objective NP-hard problem which is playing an important role in optimizing cloud utilization and Quality of Service QoS. In this paper, we develop a comprehensive multi-objective model for optimizing task scheduling to minimize task execution time, task transferring time, and task execution cost. However, the objective functions in this model are in conflict with one another. Considering this fact and the supremacy of Particle Swarm Optimization PSO algorithm in speed and accuracy, we design a multi-objective algorithm based on multi-objective PSO MOPSO method to provide an optimal solution for the proposed model. To implement and evaluate the proposed model, we extend Jswarm package to multi-objective Jswarm MO-Jswarm package. We also extend Cloudsim toolkit applying MO-Jswarm as its task scheduling algorithm. MO-Jswarm in Cloudsim determines the optimal task arrangement among VMs according to MOPSO algorithm. The simulation results show that the proposed method has the ability to find optimal trade-off solutions for multi-objective task scheduling problems that represent the best possible compromises among the conflicting objectives, and significantly increases the QoS.

[1]  Václav Snásel,et al.  Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments , 2012, Inf. Sci..

[2]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[3]  Xiao Zhi.  An Optimization Method of Workflow Dynamic Scheduling Based on Heuristic GA , 2007 .

[4]  Albert Y. Zomaya,et al.  Observations on Using Genetic Algorithms for Dynamic Load-Balancing , 2001, IEEE Trans. Parallel Distributed Syst..

[5]  Hui-Ming Wee,et al.  Particle swarm optimization for bi-level pricing problems in supply chains , 2011, J. Glob. Optim..

[6]  Imtiaz Ahmad,et al.  Particle swarm optimization for task assignment problem , 2002, Microprocess. Microsystems.

[7]  A. Jamali,et al.  A new optimization algorithm based on a combination of particle swarm optimization, convergence and divergence operators for single-objective and multi-objective problems , 2012 .

[8]  Meikang Qiu,et al.  Online optimization for scheduling preemptable tasks on IaaS cloud systems , 2012, J. Parallel Distributed Comput..

[9]  Bernd Freisleben,et al.  Multi-objective Scheduling of BPEL Workflows in Geographically Distributed Clouds , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[10]  Maria João Alves.  Using MOPSO to Solve Multiobjective Bilevel Linear Problems , 2012, ANTS.

[11]  Antonio Puliafito,et al.  Virtual machine provisioning through satellite communications in federated Cloud environments , 2012, Future Gener. Comput. Syst..

[12]  Biao Song,et al.  A Novel Heuristic-Based Task Selection and Allocation Framework in Dynamic Collaborative Cloud Service Platform , 2010, 2010 IEEE Second International Conference on Cloud Computing Technology and Science.

[13]  Yee Ming Chen,et al.  Optimal Provisioning of Resource in a Cloud Service , 2010 .

[14]  Rajkumar Buyya,et al.  CloudSim: A Novel Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services , 2009, ArXiv.

[15]  Yuehui Chen,et al.  A Task Scheduling Algorithm Based on PSO for Grid Computing , 2008 .

[16]  Da Ruan,et al.  Multi-Objective Group Decision Making - Methods, Software and Applications with Fuzzy Set Techniques(With CD-ROM) , 2007, Series in Electrical and Computer Engineering.

[17]  Jian Peng,et al.  A Task Scheduling Algorithm Based on Improved Ant Colony Optimization in Cloud Computing Environment , 2011 .

[18]  Shigen Shen,et al.  Task Scheduling Optimization in Cloud Computing Based on Heuristic Algorithm , 2012, J. Networks.

[19]  Jie Lu,et al.  Fuzzy bridged Refinement Domain Adaptation: Long-Term Bank Failure Prediction , 2013, Int. J. Comput. Intell. Appl..