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Hybrid ant genetic algorithm for efficient task scheduling in cloud data centers
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AI
DOI：10.1016/j.compeleceng.2021.107419.png)
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Abstract
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Cloud computing is a computing paradigm which meets the computational and storage demands of end users. Cloud-based data centers need to continually improve their performance due to exponential increase in service demands. Efficient task scheduling is essential part of cloud computing to achieve maximum throughput, minimum response time, reduced energy consumption and optimal utilization of resources. Bio-inspired algorithms can solve task scheduling difficulties effectively, but they need a lot of computational power and time due to high workload and complexity of the cloud environment. In this research work, Hybrid ant genetic algorithm for task scheduling is proposed. The proposed algorithm adopts features of genetic algorithm and ant colony algorithm and divides tasks and virtual machines into smaller groups. After allocation of tasks, pheromone is added to virtual machines. The proposed algorithm effectively reduces solution space by dividing tasks into groups and by detecting loaded virtual machines. Due to the minimum solution space of proposed algorithm, convergence and response time is significantly decreased. It finds a feasible scheduling solution to minimize the running time of workflows and tasks. The proposed algorithm achieved 64% decrease in execution time and 11% decrease in overall data center costs.
Keywords:
Ant colony algorithm
Cloud computing
Data center cost
Evolutionary algorithm
Genetic algorithm
Multi-objective optimization
Quality of service (QoS)
Resource allocation
Service level agreement (SLA)
Task scheduling
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