Workflow Scheduling Using Hybrid GA-PSO Algorithm in Cloud Computing

Cloud computing environment provides several on-demand services and resource sharing for clients. Business processes are managed using the workflow technology over the cloud, which represents one of the challenges in using the resources in an efficient manner due to the dependencies between the tasks. In this paper, a Hybrid GA-PSO algorithm is proposed to allocate tasks to the resources efficiently. The Hybrid GA-PSO algorithm aims to reduce the makespan and the cost and balance the load of the dependent tasks over the heterogonous resources in cloud computing environments. The experiment results show that the GA-PSO algorithm decreases the total execution time of the workflow tasks, in comparison with GA, PSO, HSGA, WSGA, and MTCT algorithms. Furthermore, it reduces the execution cost. In addition, it improves the load balancing of the workflow application over the available resources. Finally, the obtained results also proved that the proposed algorithm converges to optimal solutions faster and with higher quality compared to other algorithms.

[1]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[2]  Miron Livny,et al.  Pegasus, a workflow management system for science automation , 2015, Future Gener. Comput. Syst..

[3]  Ewa Deelman,et al.  WorkflowSim: A toolkit for simulating scientific workflows in distributed environments , 2012, 2012 IEEE 8th International Conference on E-Science.

[4]  D. Katz,et al.  The Montage architecture for grid-enabled science processing of large, distributed datasets , 2004 .

[5]  Sakshi Kaushal,et al.  Cost-Time Efficient Scheduling Plan for Executing Workflows in the Cloud , 2015, Journal of Grid Computing.

[6]  Radu Prodan,et al.  A Multi-objective Approach for Workflow Scheduling in Heterogeneous Environments , 2012, 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012).

[7]  Amandeep Verma,et al.  Scheduling using improved genetic algorithm in cloud computing for independent tasks , 2012, ICACCI '12.

[8]  Emmanuel Ahene,et al.  A Multi-objective Optimization Approach to Workflow Scheduling in Clouds Considering Fault Recovery , 2016, KSII Trans. Internet Inf. Syst..

[9]  Yingchi Mao,et al.  Max–Min Task Scheduling Algorithm for Load Balance in Cloud Computing , 2014 .

[10]  S. Chitra,et al.  Local Minima Jump PSO for Workflow Scheduling in Cloud Computing Environments , 2014 .

[11]  Rajkumar Buyya,et al.  Multiobjective differential evolution for scheduling workflow applications on global Grids , 2009 .

[12]  Ahmad M. Manasrah.  Dynamic weighted VM load balancing for cloud-analyst , 2017, Int. J. Inf. Comput. Secur..

[13]  Ashraf Hamdan Aljammal,et al.  A new architecture of cloud computing to enhance the load balancing , 2017, Int. J. Bus. Inf. Syst..

[14]  Radu Prodan,et al.  Towards a general model of the multi-criteria workflow scheduling on the grid , 2009, Future Gener. Comput. Syst..

[15]  Ladislau Bölöni,et al.  A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems , 2001, J. Parallel Distributed Comput..

[16]  Jin Li,et al.  Privacy-preserving outsourced classification in cloud computing , 2017, Cluster Computing.

[17]  Rajkumar Buyya,et al.  Workflow scheduling algorithms for grid computing , 2008 .

[18]  Robert W. Graves,et al.  The SCEC Southern California Reference Three-Dimensional Seismic Velocity Model Version 2 , 2000 .

[19]  B. B. Gupta,et al.  A survey on smart power grid: frameworks, tools, security issues, and solutions , 2017, Annals of Telecommunications.

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

[21]  Xiaohui Liu,et al.  Evolutionary Multi-Objective Workflow Scheduling in Cloud , 2016, IEEE Transactions on Parallel and Distributed Systems.

[22]  D. Amalarethinam,et al.  Workflow Scheduling for Public Cloud Using Genetic Algorithm (WSGA) , 2016 .

[23]  Arash Ghorbannia Delavar,et al.  HSGA: a hybrid heuristic algorithm for workflow scheduling in cloud systems , 2013, Cluster Computing.

[24]  Jun Zhang,et al.  An Ant Colony Optimization Approach to a Grid Workflow Scheduling Problem With Various QoS Requirements , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[25]  Fatos Xhafa,et al.  L-EncDB: A lightweight framework for privacy-preserving data queries in cloud computing , 2015, Knowl. Based Syst..

[26]  Xiaomin Zhu,et al.  Adaptive workflow scheduling for diverse objectives in cloud environments , 2017, Trans. Emerg. Telecommun. Technol..

[27]  Kousik Dasgupta,et al.  A Genetic Algorithm (GA) based Load Balancing Strategy for Cloud Computing , 2013 .

[28]  Junwei Cao,et al.  A Case Study on the Use of Workflow Technologies for Scientific Analysis: Gravitational Wave Data Analysis , 2007, Workflows for e-Science, Scientific Workflows for Grids.

[29]  Ali F. Alajmi,et al.  Selecting the most efficient genetic algorithm sets in solving unconstrained building optimization problem , 2014 .

[30]  M. Livny,et al.  High-Throughput, Kingdom-Wide Prediction and Annotation of Bacterial Non-Coding RNAs , 2008, PloS one.

[31]  Saurabh Bilgaiyan,et al.  A study on load balancing in cloud computing environment using evolutionary and swarm based algorithms , 2014, 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT).

[32]  Xiao Liu,et al.  A market-oriented hierarchical scheduling strategy in cloud workflow systems , 2011, The Journal of Supercomputing.

[33]  Rizos Sakellariou,et al.  Budget-Deadline Constrained Workflow Planning for Admission Control , 2013, Journal of Grid Computing.

[34]  Kostas E. Psannis,et al.  Secure integration of IoT and Cloud Computing , 2018, Future Gener. Comput. Syst..

[35]  Hamid Arabnejad,et al.  A Budget Constrained Scheduling Algorithm for Workflow Applications , 2014, Journal of Grid Computing.

[36]  Miron Livny,et al.  Correction: High-Throughput, Kingdom-Wide Prediction and Annotation of Bacterial Non-Coding RNAs , 2008, PLoS ONE.

[37]  Xuejie Zhang,et al.  A load balancing mechanism based on ant colony and complex network theory in open cloud computing federation , 2010, 2010 The 2nd International Conference on Industrial Mechatronics and Automation.

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

[39]  Guiyi Wei,et al.  GA-Based Task Scheduler for the Cloud Computing Systems , 2010, 2010 International Conference on Web Information Systems and Mining.

[40]  Rajkumar Buyya,et al.  A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments , 2010, 2010 24th IEEE International Conference on Advanced Information Networking and Applications.

[41]  Ammar Almomani,et al.  A Variable Service Broker Routing Policy for data center selection in cloud analyst , 2017, J. King Saud Univ. Comput. Inf. Sci..