Cloud services are widely used around the world since the cloud services are playing a key role in many industries such as Supply Chain, Networking, Storages, etc. Different task scheduling algorithms have been used to handle cloud service applications, but none of the algorithms contain all the constraints such as load balancing, makespan time, cost and the time of execution. The scheduling technique considers well when it efficiently performs utilizing resources of the cloud. The heuristic scheduling algorithm provides the optimal solution, thereby increasing the efficiency of the overall system. Heuristic methods such as Genetic Algorithm (GA) are deals with the natural selection of solutions from the all possible solutions. Genetic algorithms schedule the cloud tasks according to the computational power of the system, memory resources and requirements of the tasks. The aim of this survey is to propose a technique to minimize the completion time and cost of tasks and maximize resource utilization using Genetic Algorithm (GA). This work also presents the comparative analysis of different task scheduled applications proposed by the researchers during the last five years.

Cloud Computing, Scheduling, Genetic Algorithm, Optimization, Scheduling Algorithms

[1] Sourabh Budhiraja, Dheerendra Sing, “Task Scheduling using Genetic Algorithm in Cloud Computing Environment: A Review”, International Journal of Info. Tech. and Knowledge Management, Vol.8, No.1, pp.46-49, 2014.
[2] Tarun Goyal and Aakanksha Agrawal, “Host Scheduling Algorithm Using Genetic Algorithm in Cloud Computing Environment”, International Journal of Research in Engineering & Technology, Vol.1, Issue.1, pp.7-12, 2013.
[3] Simply, Jagandeep Sidhu, “Different Scheduling Algorithms in Different Cloud Environment”, International Journal of Advanced Research in Computer and Communication Engineering, Vol.3, Issue.9, pp.8003-8006, 2014.
[4] Hongyan Cui, Xiaofei Liu, Tao Yu, Honggang Zhang, Yajun Fang, and Zhongguo Xia, “Cloud Service Scheduling Algorithm Research and Optimization”, Security and Communication Networks, Hindawi, Vol.2017, Article ID 2503153, pp.1-7, 2017.
[5] Durairaj, M and Kannan, P, “Improvised Genetic Approach for an Effective Resource Allocation in Cloud Infrastructure”, International Journal of Computer Science and Information Technologies, Vol.6, Issue.4, pp.4037-4046, 2015.
[6] R. Durga, Lakshmi, N. Srinivasu " A Review and Analysis of Task Scheduling Algorithms in Different Cloud Computing Environments " International Journal of Computer Science and Mobile Computing, Vol.4, Issue.12, pp.235–241, 2015.
[7] S. Ravichandran, E.R. Naganathan, “Dynamic Scheduling of Data Using Genetic Algorithm in Cloud Computing”, International Journal of Computing Algorithm, Vol.2, Issue.1, pp.127-133, 2013.
[8] M. Padmavathi, Shaik. Mahboob Basha, Srinivas Pothapragada, “A Survey on Scheduling Algorithms in Cloud Computing”, IOSR Journal of Computer Engineering, Vol.16, Issue.4, pp.27-32, 2014.
[9] Safwat A. Hamad, Fatma A. Asmara “Genetic-Based Task Scheduling Algorithm in Cloud Computing Environment " International Journal of Advanced Computer Science and Applications, Vol.7, No.4, pp. 550-556, 2016.
[10] A. Kaleeswaran, V. Ramasamy, P. Vivekanandan, “Dynamic Scheduling of Data using Genetic Algorithm in Cloud Computing”, International Journal of Advances in Engineering & Technology, Vol.5, Issue.2, pp.327-334, 2013.
[11] R. Durga Lakshmi, N. Srinivasu, “A Dynamic Approach to Task Scheduling in Cloud Computing using Genetic Algorithm”, Journal of Theoretical and Applied Information Technology, Vol.85, No.2, pp.124-135, 2016.
[12] M. Durairaj, A. Menaka, “Load Balancing in Cloud Computing”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 5, Issue.8, pp. 862-870, 2015.
[13] Athokpam Bikramjit Singh, Sathyendra Bhat J, Rajesh Raju, Rio D`Souza, “A Comparative 8dy of Various Scheduling Algorithms in Cloud Computing”, American Journal of Intelligent Systems, Vol.7, Issue.3, pp.68-72, 2017.
[14] Pardeep Kumar, Amandeep Verma, “Scheduling Using Improved Genetic Algorithm in Cloud Computing for Independent Tasks”, International Conference on Advances in Computing, Communications, and Informatics, India, pp.137-142, 2012.
[15] Teena Mathew, K. Chandra Sekaran, John Jose, “Study and Analysis of Various Task Scheduling Algorithms in the Cloud Computing Environment”, International Conference on Advances in Computing, Communications, and Informatics, India, pp.658-664, 2014.
[16] Sonali Jain, “Task Scheduling in Cloud Computing using Genetic Algorithm”, International Journal of Computer Science, Engineering, and Information Technology Research, Vol.6, Issue.4, pp. 9-22, 2016.
[17] Pinal Salot, “A Survey of Various Scheduling Algorithm in Cloud Computing Environment”, International Journal of Research in Engineering and Technology, Vol.2, Issue.2, pp. 131-135, 2013.
[18] Jeni Patel, Jignesh Prajapati, “A Survey of Various Scheduling Algorithms and types of Resources Provisioning in Cloud Environment”, International Journal of Engineering and Computer Science, Vol.4 Issue.1, pp.10132-10134, 2015.
[19] Shekhar Singh, Mala Kalra, “Task Scheduling Optimization of Independent Tasks in Cloud Computing using Enhanced Genetic Algorithm”, International Journal of Application or Innovation in Engineering & Management, Vol.3, Issue.7, pp.286-291, 2014.
[20] M. Durairaj, T. Sathyavathi, “Applying Rough Set Theory for Medical Informatics Data Analysis,” International Journal of Scientific Research in Computer Sciences and Engineering, Vol. 1, Issue. 5, pp. 1–8, Oct. 2013.