Task Scheduling in Distributed Systems Using Heap Intelligent Discrete Particle Swarm Optimization

The optimal mapping of tasks to the processors is one of the challenging issues in heterogeneous computing systems. This article presents a task scheduling problem in distributed systems using discrete particle swarm optimization (DPSO) algorithm with various neighborhood topologies. The DPSO is a recent metaheuristic population‐based algorithm. In DPSO, the set of particles in a swarm flies through the N‐dimensional search space by learning from both the personal best position and a neighborhood best position. Each particle inside the swarm belongs to a specific topology for communicating with neighboring particles in the swarm. The neighborhood topology affects the performance of DPSO significantly, because it determines the rate at which information transmits through the swarm. The proposed DPSO algorithm works on dynamic topology that is binary heap tree for communication between the particles in the swarm. The performance of the proposed topology is compared with other topologies such as star, ring, fully connected, binary tree, and Von Neumann. The three well‐known performance measures such as Makespan, mean flow time, and reliability cost are used for the comparison of the proposed topology with other neighborhood topologies. Computational simulation results indicate that the performance of DPSO algorithm has shown significant improvement with binary heap tree topology used for communication among the particles in the swarm.

[1]  Dan Simon,et al.  Oppositional biogeography-based optimization for combinatorial problems , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[2]  Hongbo Liu,et al.  Nature inspired meta-heuristics for grid scheduling: single and multi-objective optimization approaches , 2008 .

[3]  D. Manimegalai,et al.  Multiobjective Variable Neighborhood Search algorithm for scheduling independent jobs on computational grid , 2015 .

[4]  José Neves,et al.  What Makes a Successful Society? Experiments with Population Topologies in Particle Swarms , 2004, SBIA.

[5]  Angel Eduardo Muñoz Zavala,et al.  A Comparison Study of PSO Neighborhoods , 2012, EVOLVE.

[6]  A. Kai Qin,et al.  A review of population initialization techniques for evolutionary algorithms , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[7]  Fahd Alharbi,et al.  Simple Scheduling Algorithm with Load Balancing for Grid Computing , 2012 .

[8]  Jun Sun,et al.  Permutation-based Particle Swarm Algorithm for Tasks Scheduling in Heterogeneous systems with Communication Delays , 2008 .

[9]  Howard Jay Siegel,et al.  Representing Task and Machine Heterogeneities for Heterogeneous Computing Systems , 2000 .

[10]  Jing Hu,et al.  An Ant Colony Optimization for Grid Task Scheduling with Multiple QoS Dimensions , 2009, 2009 Eighth International Conference on Grid and Cooperative Computing.

[11]  Hong He,et al.  A novel discrete particle swarm optimization algorithm for meta-task assignment in heterogeneous computing systems , 2011, Microprocess. Microsystems.

[12]  Xiao Qin,et al.  Dynamic, reliability-driven scheduling of parallel real-time jobs in heterogeneous systems , 2001, International Conference on Parallel Processing, 2001..

[13]  José Gabriel Ramírez-Torres,et al.  A Statistical Study of the Effects of Neighborhood Topologies in Particle Swarm Optimization , 2011 .

[14]  James Kennedy,et al.  Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[15]  Jan Platos,et al.  Differential Evolution for Scheduling Independent Tasks on Heterogeneous Distributed Environments , 2010 .

[16]  S. A. Hamdan,et al.  Hybrid Particle Swarm Optimiser using multi-neighborhood topologies , 2008 .

[17]  Fatma A. Omara,et al.  Genetic algorithms for task scheduling problem , 2010, J. Parallel Distributed Comput..

[18]  Ajith Abraham,et al.  Inertia Weight strategies in Particle Swarm Optimization , 2011, 2011 Third World Congress on Nature and Biologically Inspired Computing.

[19]  Sakti Prasad Ghoshal,et al.  Radiation pattern optimization for concentric circular antenna array with central element feeding using craziness-based particle swarm optimization , 2010 .

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

[21]  Yunlong Zhu,et al.  Artificial Bee Colony Algorithm Based On Von Neumann Topology Structure , 2010 .

[22]  K. Umamaheswari,et al.  Task Scheduling in Distributed Systems using Discrete Particle Swarm Optimization , 2014 .

[23]  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.

[24]  Enrique Alba,et al.  A parallel micro evolutionary algorithm for heterogeneous computing and grid scheduling , 2012, Appl. Soft Comput..

[25]  J. Kennedy,et al.  Population structure and particle swarm performance , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[26]  R. Pimpale,et al.  GUARANTEED COVERAGE PARTICLE SWARM OPTIMIZATION USING NEIGHBORHOOD TOPOLOGIES , 2012 .

[27]  K. Umamaheswari,et al.  Task Scheduling Using Multi-objective Particle Swarm Optimization with Hamming Inertia Weight , 2016 .

[28]  Salwani Abdullah,et al.  A multi-population harmony search algorithm with external archive for dynamic optimization problems , 2014, Inf. Sci..

[29]  Ajith Abraham,et al.  A DISCRETE PARTICLE SWARM OPTIMIZATION APPROACH FOR GRID JOB SCHEDULING , 2009 .

[30]  Jianming Deng,et al.  A New Logistic Dynamic Particle Swarm Optimization Algorithm Based on Random Topology , 2013, TheScientificWorldJournal.

[31]  Tieli Sun,et al.  Comparison of Particle Swarm Optimization and Genetic Algorithm for HMM training , 2008, 2008 19th International Conference on Pattern Recognition.

[32]  Naglaa M. Reda,et al.  Sort-Mid tasks scheduling algorithm in grid computing , 2014, Journal of advanced research.

[33]  K. Umamaheswari,et al.  Comparison among four Modified Discrete Particle Swarm Optimization for Task Scheduling in Heterogeneous Computing Systems , 2013 .

[34]  O. Weck,et al.  A COMPARISON OF PARTICLE SWARM OPTIMIZATION AND THE GENETIC ALGORITHM , 2005 .

[35]  Sudhanshu Prakash Tiwari,et al.  Grid Scheduling Using PSO with SPV Rule , 2012 .

[36]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[37]  Qiu Guan,et al.  Adaptive Parameters for a Modified Comprehensive Learning Particle Swarm Optimizer , 2012 .

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

[39]  M. C. Bhuvaneswari,et al.  Non Dominated Particle Swarm Optimization For Scheduling Independent Tasks On Heterogeneous Distributed Environments , 2011 .