A Hyper Heuristics Technique for Data Partitioning and Scheduling to Heterogeneous Systems using Genetic Algorithm and Improved Particle Swarm Optimization
Sundar Ganesh1, R. Sivakumar2, N. Rajkumar3
1Sundar Ganesh C S, Assistant Professor, Department of EEE, Karpagam College of Engineering, Coimbatore (Tamil Nadu), India.
2Dr. R. Sivakumar, Professor, Department of Mechatronics Engineering, Akshaya College of Engineering, Coimbatore (Tamil Nadu), India.
3Dr. N. Rajkumar, Associate Professor, Department of Computer Science and Engineering, Akshaya College of Engineering, Coimbatore (Tamil Nadu), India.
Manuscript received on 23 August 2019 | Revised Manuscript received on 11 September 2019 | Manuscript Published on 17 September 2019 | PP: 1696-1701 | Volume-8 Issue-2S8 August 2019 | Retrieval Number: B11360882S819/2019©BEIESP | DOI: 10.35940/ijrte.B1136.0882S819
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Development of the load partitioning for multiple round load distribution and effective scheduling of partitioned load to heterogeneous processor is primary goal of distributed and parallel system. In this paper, we propose hyper heuristics scheduling algorithm for load partitioning using genetic and improved particle swarm optimization techniques.A communication model is used to predict the optimal activation order, optimal number of processor and optimal number of rounds of the load. Heuristics Based Scheduling Algorithm is proposed using Hyper Heuristic Scheduling which is used to find the candidate solution (low level heuristic) to form Scheduling Solutions (heuristics algorithms) for large scale system with diversity operator as sequence dependent and sequence independent scheduling. For this solution, processing time of the entire processing load will be reduced. Hybrid Real Code genetic algorithm(HRGA) computes optimal activation order with cross over and mutation operator without considering the processor latency and different types of variation in the perturbation parameters. In order to optimize this issue, we utilizeImproved Particle swarm optimization (IPSO) determine the load fraction for generating activation order in terms of dynamically predicting fitness value of the processor with certain number. The Simulation analysis demonstrates the proposed model performance in terms of mean, standard deviation, computational complexity and Average Execution Time comparing against hybrid real coded genetic algorithm.
Keywords: Data Partitioning, Processor Scheduling, Improved Particle Swarm Optimization, Real Coded Genetic Algorithm, Hyper Heuristic.
Scope of the Article: Data Analytics