Cost Effective Ant Colony Optimization in Cloud Computing
Chandrakanta Yadav1, Yogesh Kumar Gupta2

1Dr. Yogesh Kumar Gupta, Department of Computer Science, Banasthali Vidyapith, Rajasthan, India.
2Chandrakanta Yadav, Department of Computer Science, Banasthali Vidyapith, Rajasthan, India.

Manuscript received on 15 August 2019 | Revised Manuscript received on 22 August 2019 | Manuscript published on 30 August 2019 | PP: 3016-3020 | Volume-8 Issue-10, August 2019 | Retrieval Number: J94600881019/19©BEIESP | DOI: 10.35940/ijitee.J9460.0881019
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Abstract: Cloud computing is a term for a wide range of developments possibilities. It is rapidly growing paradigm in software technology that offers different services. Cloud computing has come of age, since Amazon’s rollouted the first of its kind of cloud services in 2006. It stores the tremendous amount of data that are being processed every day. Cloud computing is a reliable computing base for data-intensive jobs. Cloud computing provide computing resources as a service. It is on-demand availability of computing resources without direct interaction of user. A major focus area of cloud computing is task scheduling. Task scheduling is one among the many important issues to be dealt with. It means to optimize overall system capabilities and to allocate the right resources. Task scheduling referred to NP-hard problem. The proposed algorithm is Cost Effective ACO for task scheduling, which calculates execution cost of CPU, bandwidth, memory etc. The suggested algorithm is compared with CloudSim with the presented Basic Cost ACO algorithm-based task scheduling method and outcomes clearly shows that the CEACO based task scheduling method clearly outperforms the others techniques which are in use into considerations. The task is allotted to the number of VMs based on the priorities (highest to lowest) given by user. The simulation consequences demonstrate that the suggested scheduling algorithm performs faster than previous Ant Colony Optimization algorithm in reference to the cost. It reduces the overall cost as compare to existing algorithm.
Keywords: Cloud Computing, Cost Optimization, Task Scheduling, ACO, CEACO.

Scope of the Article: Cloud Computing