A Novel Architecture with Dynamic Queues Based on Fuzzy Logic and Particle Swarm Optimization Algorithm for Task Scheduling in Cloud Computing

Cloud computing is an emerging high performance computing paradigm for managing and delivering services using a large collection of heterogeneous autonomous systems with flexible computational architecture. Task scheduling is one of the most challenging aspects to improve the overall performance of the cloud computing such as response time, cost, makespan, throughput etc. Task scheduling is also essential to reduce power consumption, processing time and improve the profit of service providers by decreasing operating costs and improving the system reliability. This paper focuses on Task Scheduling using a novel architecture with Dynamic Queues based on hybrid algorithm using Fuzzy Logic and Particle Swarm Optimization algorithm (TSDQ-FLPSO) to optimize makespan and waiting time. The experimental result based on an open source simulator (CloudSim) show that the proposed TSDQ-FLPSO provides an optimal balance results, minimizing the waiting time, reducing the makespan and improving the resource utilization compared to existing scheduling algorithms.

[1]  Guimin Chen,et al.  A Particle Swarm Optimizer with Multi-stage Linearly-Decreasing Inertia Weight , 2009, 2009 International Joint Conference on Computational Sciences and Optimization.

[2]  Bing Zeng,et al.  A Task Scheduling Algorithm based on QoS-Driven in Cloud Computing , 2013, ITQM.

[3]  Tian Fu,et al.  A Novel Dynamic Task Scheduling Algorithm Based on Improved Genetic Algorithm in Cloud Computing , 2016 .

[4]  Yong Feng,et al.  Chaotic Inertia Weight in Particle Swarm Optimization , 2007, Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007).

[5]  Rashedur M. Rahman,et al.  Fuzzy logic based dynamic load balancing in virtualized data centers , 2013, 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[6]  Himani,et al.  Cost-Deadline Based Task Scheduling in Cloud Computing , 2015, 2015 Second International Conference on Advances in Computing and Communication Engineering.

[7]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[8]  A. S. Ajeena Beegom,et al.  A Particle Swarm Optimization Based Pareto Optimal Task Scheduling in Cloud Computing , 2014, ICSI.

[9]  Yuansheng Lou,et al.  A Task Scheduling Algorithm Based on Genetic Algorithm and Ant Colony Optimization Algorithm with Multi-QoS Constraints in Cloud Computing , 2015, 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics.

[10]  Jesús Alcalá-Fdez,et al.  jFuzzyLogic: a robust and flexible Fuzzy-Logic inference system language implementation , 2012, 2012 IEEE International Conference on Fuzzy Systems.

[11]  R. K. Jena,et al.  Multi Objective Task Scheduling in Cloud Environment Using Nested PSO Framework , 2015 .

[12]  Sanjeev Kumar,et al.  Tuning of Particle Swarm Optimization Parameter Using Fuzzy Logic , 2011, 2011 International Conference on Communication Systems and Network Technologies.

[13]  Sarbjeet Singh,et al.  A review of metaheuristic scheduling techniques in cloud computing , 2015 .

[14]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .

[15]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[16]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[17]  Jesús Alcalá-Fdez,et al.  jFuzzyLogic: a Java Library to Design Fuzzy Logic Controllers According to the Standard for Fuzzy Control Programming , 2013, Int. J. Comput. Intell. Syst..

[18]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[19]  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..

[20]  Gao Yue-lin,et al.  A New Particle Swarm Optimization Algorithm with Random Inertia Weight and Evolution Strategy , 2007, 2007 International Conference on Computational Intelligence and Security Workshops (CISW 2007).

[21]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.