Dynamic Load Balancing Ant Colony Optimization (DLBACO) Algorithm for Task Scheduling in Cloud Environment
Arvinda Kushwaha1, Mohd Amjad2, Arvind Kumar3

1Arvinda Kushwaha*, Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India.
2Mohd Amjad, Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India.
3Arvind Kumar, Department of CSE, KEC, Ghaziabad, India.

Manuscript received on September 17, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 939-946 | Volume-8 Issue-12, October 2019. | Retrieval Number: J94040881019/2019©BEIESP | DOI: 10.35940/ijitee.J9404.1081219
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Abstract: Cloud computing is a framework which provides on-demand services to the user for scalability, security, and reliability based on pay as used service anytime & anywhere. For load balancing, task scheduling is the most critical issues in the cloud environment. There are so many meta-heuristic algorithms used to solve the load balancing problem. A good task scheduling algorithm should be used for optimum load balancing in cloud environment. Such scheduling algorithm must have some vital characteristic like minimum makespan, maximum throughput, and maximum resource utilization, etc. In this paper, a dynamic load balancing and task scheduling algorithm based on ant colony optimization (DLBACO) has been proposed. This algorithm assigns the task the VM which has highest probability of availability in minimum time. The proposed algorithm balances the whole system by minimizing the makespan of the task and maximizing the throughput. CloudSim simulator is used to simulate the proposed scheduling algorithm and results show that the proposed (DLBACO) algorithm is better than the existing algorithms such as FCFS, LBACO (Load balancing ACO), and primary ACO.
Keywords: CloudSim, Task Scheduling, Cloud Environment, ACO, load Balancing.
Scope of the Article: Environmental Engineering