cloud computing delivers a service over the network by the use of hardware as well as of software that is the internet. Cloud computing is technology that are rapidly increase in terms of both academia and industry. Cloud computing allows everyone to use software and computing services on-demand at anytime, anywhere and anyplace using the internet. With the help of the cloud computing, users can access the files as well as can use the applications from any other device which can access the internet device. In Scheduling, cloud computing infrastructures contain several challenging issues like time estimation and load balancing etc. But main challenge for cloud computing environment is load balancing. Basically load balancing distributes the load to get lesser makespan (MS) and higher resource utilization. Load balancing algorithms ensure that neither a Virtual Machine is overloaded nor it is under loaded. This paper presents comparison of the metaheuristic approach which is inspired by Ant Behaviors (AB) and Swarm Intelligence (SI): The Ant Colony Optimization (ACO) and The Genetic algorithm (GA).

Cloud Computing, Load Balancer, The Ant Colony Optimization (ACO), The Genetic Algorithm (GA), Make-span, Resource-utilizations

[1] Mell, Peter and Tim Grance,”The NIST of cloud
Definition of cloud computing.” (2011).
[2] R.W Lucky, “Cloud Computing” (IEEE journal of spectrum, vol.46, no. 5, May 2009.
[3] Rimal, Bhaskar Prasad, Eunmi Choi, and Ian Lumb. "A taxonomy and survey of cloud computing systems." INC, IMS and IDC, 2009. NCM`09. Fifth International Joint Conference on. IEEE, 2009.
[4] G. Pallio,”Cloud computing:The new frontier of internet computing.” IEEE journal of internet computing, vol. 14, no. 5, September/October, 2010.
[5] Shimay and Jagandeep Sidhu,”Difference Scheduling Algorithm in Different Cloud Environment,”international journal of Advanced Research in Computer and Communication Engineering(IJARCCE), vol. 3,no. 9, pp. 8003-8006,September 2014.
[6] Dr. R. Joshua Samuel Raj and Dr. S.V. Muruga Prasad,”Survey on variants of Heuristic Algorithms for scheduling Workflow of tasks,”international conference on circuits,power and computing technology (ICCPCT),IEEE,2016.
[7] Gaurang Pate and Rutvik Mehta,”A survey on various task scheduling algorithm in cloud computing,”international journal of advanced research in computer engineering & technology (IJARCET),vol. 3,no. 3,pp. 715-717,March 2014.
[8] J.H Holand,”Adaption in natural and artificial systems.”The university of Michigan press, 1975.
[9]Putha et.al,”Comparing Ant Colony Optimization and Genetic Algorithm approaches for solving traffic signal coordination.”,Computer-aided civil & infrastructure ,14-28,2012.
[10] L.J Fogel, A.J Fogel and M.J Walsh,”Artificial intelligence through simulated evolution.”,John Wiley & Sons,1996
[11] D.B Fogel ,”The evolution of intelligence decision making in gaming,”cybernetics and systems, vol.22, pp.223-236, 1991.
[12] M.Vazuez and L.D Whitley, “A hybrid genetic algorithm for the quadratic assignment problem,”in GECCO, 2000, pp.135-142.
[13] M.Kalra, Sarbjeet Singh,”A review of metaheuristic seheduling techniques in cloud computing.”Egyption ingormatics journal vol 16, pp 275-295,25-july-2015.
[14] Kun Li,Gaochao Xo et.al,”Cloud task scheduling on load balancing Ant Colony Optimuzation.”(2011),ieee