Learning-Based Task Scheduling Using Big Bang Big Crunch for Cloud Computing Environment
| Autor: | Singh Rawat Pradeep, Dimri Priti, Gupta Punit |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
021103 operations research
General Computer Science business.industry Computer science Distributed computing 0211 other engineering and technologies Big bang big crunch Cloud computing 02 engineering and technology 01 natural sciences Scheduling (computing) 010104 statistics & probability Learning based 0101 mathematics business |
| Zdroj: | Recent Advances in Computer Science and Communications. 13:137-146 |
| ISSN: | 2666-2558 |
| DOI: | 10.2174/2213275912666190204125712 |
| Popis: | Cloud Computing is a growing industry for secure and low cost pay per use resources. Efficient resource allocation is the challenging issue in cloud computing environment. Many task scheduling algorithms used to improve the performance of system. It includes ant colony, genetic algorithm and Round Robin improve the performance but these are not cost efficient at the same time. : Scheduling issue and resource cost resolve using improved meta-heuristic approaches. In this work, a cost aware algorithm improved using Big-Bang Big-Crunch based task mapping is proposed which reduces the execution time and cost paid for the resources at the time of execution. The cost aware meta-heuristic technique used. Results show that the proposed algorithm provides better cost efficiency than the existing genetic algorithm. The proposed Big-Bang Big-Crunch based resource allocation technique evaluated against the Genetic approach. Results: Performance is measured using an optimization criteria tasks completion time and resource operational cost in the duration of execution. The population size and user requests measures the performance of the proposed model. : The simulation shows that the proposed cost and time aware technique outperforms using performance measurement parameters (average finish time, resource cost). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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