:
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.
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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.
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The simulation shows that the proposed cost and time aware technique outperforms using performance
measurement parameters (average finish time, resource cost).
Cloud computing task scheduling strategy based on improved differential evolution algorithm