Chicken Swarm Optimization in Task Scheduling in Cloud Computing

In this paper, VM replacement strategy is developed using the optimization algorithm, namely artificial bee chicken swarm optimization (ABCSO), in cloud computing model. The ABCSO algorithm is the integration of the artificial bee colony (ABC) in chicken swarm optimization (CSO). This method employed VM placement based on the requirement of the VM for the completion of the particular task using the service provider. Initially, the cloud system is designed, and the proposed ABCSO-based VM placement approach is employed for handling the factors, such as load, CPU usage, memory, and power by moving the virtual machines optimally. The best VM migration strategy is determined using the fitness function by considering the factors, like migration cost, load, and power consumption. The proposed ABCSO method achieved a minimal load of 0.1688, minimal power consumption of 0.0419, and minimal migration cost of 0.0567, respectively.

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An efficient task scheduling in a cloud computing environment using hybrid Genetic Algorithm - Particle Swarm Optimization (GA-PSO) algorithm

2019 International Conference on Intelligent Sustainable Systems (ICISS) ◽

10.1109/iss1.2019.8908041 ◽

2019 ◽

Author(s):

A. M. Senthil Kumar ◽

K. Parthiban ◽

S Siva Shankar

Keyword(s):

Genetic Algorithm ◽

Cloud Computing ◽

Particle Swarm Optimization ◽

Task Scheduling ◽

Hybrid Genetic Algorithm ◽

Particle Swarm ◽

Pso Algorithm ◽

Computing Environment ◽

Swarm Optimization ◽

Cloud Computing Environment

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A PSO Algorithm Based Task Scheduling in Cloud Computing

International Journal of Applied Metaheuristic Computing ◽

10.4018/ijamc.2019100101 ◽

2019 ◽

Vol 10 (4) ◽

pp. 1-17 ◽

Cited By ~ 2

Author(s):

Mohit Agarwal ◽

Gur Mauj Saran Srivastava

Keyword(s):

Cloud Computing ◽

Task Scheduling ◽

Virtual Machines ◽

Pso Algorithm ◽

Emerging Technology ◽

The Internet ◽

Hard Problem ◽

Swarm Optimization ◽

Np Hard Problem ◽

Simulation Results

Cloud computing is an emerging technology which involves the allocation and de-allocation of the computing resources using the internet. Task scheduling (TS) is one of the fundamental issues in cloud computing and effort has been made to solve this problem. An efficient task scheduling mechanism is always needed for the allocation to the available processing machines in such a manner that no machine is over or under-utilized. Scheduling tasks belongs to the category of NP-hard problem. Through this article, the authors are proposing a particle swarm optimization (PSO) based task scheduling mechanism for the efficient scheduling of tasks among the virtual machines (VMs). The proposed algorithm is compared using the CloudSim simulator with the existing greedy and genetic algorithm-based task scheduling mechanism. The simulation results clearly show that the PSO-based task scheduling mechanism clearly outperforms the others as it results in almost 30% reduction in makespan and increases the resource utilization by 20%.

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