Multiobjective Artificial Bee Colony based Job Scheduling for Cloud Computing Environment

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Neha Sethi 1,* Surjit Singh 1 Gurvinder Singh 2

1. IKG Punjab Technical University, Jalandhar,Punjab,India

2. Guru Nanak Dev University, Amritsar, Punjab, India

* Corresponding author.


Received: 1 Aug. 2017 / Revised: 2 Sep. 2017 / Accepted: 22 Sep. 2017 / Published: 8 Jan. 2018

Index Terms

Ant Colony Optimization, Job Scheduling, Honey Bee Colony, Particle Swarm Optimization


Cloud computing has become the hottest issue due to its wide range of services. Due to a large number of users, it becomes more significant to provide high availability of services to cloud users. The majority of existing scheduling techniques in the cloud environment is NP-Complete in nature. Many researchers have utilized meta-heuristic techniques to schedule the jobs in cloud data centers. The majority of existing techniques such as Genetic Algorithm, Ant colony optimization, Non-dominated Sorting Genetic Algorithm (NSGA-III), etc. suffer from poor convergence speed. Also, most of these techniques are either based upon scheduling or load balancing. Therefore, to overcome these issues, a new Variance Honey Bee Behavior with multi-objective optimization method (VHBBMO) is proposed in this paper. Extensive experiments have been conducted by considering the various set of jobs. The experimental results have shown that the proposed method provides more significant results than available methods.

Cite This Paper

Neha Sethi, Surjit Singh, Gurvinder Singh,"Multiobjective Artificial Bee Colony based Job Scheduling for Cloud Computing Environment", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.4, No.1, pp.41-55, 2018. DOI: 10.5815/ijmsc.2018.01.03


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