Cloud Comptuing is the act of utilizing a system of remote servers facilitated on the Internet to store, oversee, and prepare information, as opposed to a nearby server or an individual computer . The organization piece based procedures that are mindful from the server determination from the cloud can progress to the cost and adequacy of distributed computing. In this we concentrated on the distinctive swarm savvy based vitality proficient procedures called Ant settlement enhancement and Particle swarm streamlining based methods. There are different planning systems like the utilization of Ant settlement improvement has demonstrated a low convergence rate to the genuine worldwide least even at high quantities of measurements Artificial bee colony optimization algorithm has been widely accepted as a global optimization algorithm of current interest for distributed optimization and control. Particle swarm advancement is restricted to introductory arrangement of particles, wrongly chose particles tends to poor results. In order to overcome these constrains a new hybrid Artificial bee colony and ant colony optimization algorithm for cloud computing environment will be proposed to enhance the energy consumption rate further.

Cloud Computing, Artificial bees colony , Ants colony optimization ,load balancing , scheduling

[1] Alkhanak, Ehab Nabiel, Sai Peck Lee, and Saif Ur Rehman Khan. "Cost-aware challenges for workflow scheduling approaches in cloud computing environments: Taxonomy and opportunities." Future Generation Computer Systems 50 (2015): 3-21.
[2] Srikantaiah, Shekhar, Aman Kansal, and Feng Zhao. "Energy aware consolidation for cloud computing." Proceedings of the 2008 conference on Power aware computing and systems. Vol. 10. 2008.
[3] Poli, Riccardo. "Analysis of the publications on the applications of particle swarm optimisation." Journal of Artificial Evolution and Applications 2008 (2008): 3.
[4] Bansal, Nidhi, et al. "Cost performance of QoS Driven task scheduling in cloud computing." Procedia Computer Science 57 (2015): 126-130.
[5] Berl, Andreas, et al. "Energy-efficient cloud computing." The computer journal 53.7 (2010): 1045-1051.
[6] Chen, Da-Ren, and Kai-Feng Chiang. "Cloud-based power estimation and power-aware scheduling for embedded systems." Computers & Electrical Engineering 47 (2015): 204-221.
[7] Kalra, Mala, and Sarbjeet Singh. "A review of metaheuristic scheduling techniques in cloud computing." Egyptian Informatics Journal 16.3 (2015): 275-295.
[8] Lakra, Atul Vikas, and Dharmendra Kumar Yadav. "Multi-Objective Tasks Scheduling Algorithm for Cloud Computing Throughput Optimization." Procedia Computer Science 48 (2015): 107-113.
[9] Li, Zhongjin, et al. "A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds." Future Generation Computer Systems (2016).
[10] Cho, Keng-Mao, et al. "A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing." Neural Computing and Applications 26.6 (2015): 1297-1309.
[11] Wang, Xiaoli, Yuping Wang, and Yue Cui. "A new multi-objective bi-level programming model for energy and locality aware multi-job scheduling in cloud computing." Future Generation Computer Systems 36 (2014): 91-101.
[12] Zhan, Zhi-Hui, et al. "Cloud computing resource scheduling and a survey of its evolutionary approaches." ACM Computing Surveys (CSUR) 47.4 (2015): 63.
[13] Mishra, Abhinav, and Anil Kumar. "Context Switching In Clouds: Analysis And Enhancements." International Journal of Engineering Research and Technology. Vol. 2. No. 7 (July-2013). ESRSA Publications, 2013.
[14] Bai, Qinghai. "Analysis of particle swarm optimization algorithm." Computer and information science 3.1 (2010): 180.
[15] Thomas, Antony, G. Krishnalal, and VP Jagathy Raj. "Credit Based Scheduling Algorithm in Cloud Computing Environment." Procedia Computer Science 46 (2015): 913-920.