A Study on Nature Inspired Task Scheduling Algorithms in Cloud EnvironmentAuthor : N. Deepika and O. S. Abdul Qadir
Volume 8 No.2 Special Issue:March 2019 pp 79-82
Cloud computing is an encouraging paradigm which offers resources to customers on their demand with least cost. Task scheduling is the key difficult in cloud computing which decreases the performance of the system. To develop performance of the system, there is necessity of an effective task-scheduling algorithm. Nature inspired computing is a technique that is inspired by practices detected from nature. These computing techniques led to the growth of algorithms called Nature Inspired Algorithms (NIA). These algorithms are theme of computational intelligence. The persistence of raising such algorithms is to enhance engineering problems. Nature inspired algorithms have enlarged huge popularity in recent years to challenge hard real world (NP hard and NP complete) problems and resolve complex optimization functions whose actual solution doesn’t occur. The paper presents a complete review of 12 nature inspired algorithms. This study offers the researchers with a single platform to analyze the conventional and contemporary nature inspired algorithms in terms of essential input parameters, their key evolutionary strategies and application areas. This study would support the research community to recognize what all algorithms could be observed for big scale global optimization to overwhelm the problem of ‘curse of dimensionality’.
Cloud Computing, Task Scheduling, Nature Inspired Algorithm
 K.R. Ramesh Babu and Philip Samuel, “Enhanced Bee Colony Algorithm for Efficient Load Balancing and Scheduling in Cloud”, Innovations in Bio-Inspired Computing and Applications, 2016.
 ParulAgarwal and Shikha Mehta, “Nature-Inspired Algorithms: State-of-Art, Problems and Prospects”, International Journal of Computer Applications, Vol. 100, August, 2014.
 G.D Shridharand G.R.M Reddy, “Optimal load balancing in cloud computing by efficient utilization of virtual machines”, IEEE Sixth
International Conference on Communication Systems and Networks (COMSNETS), 2014.
 A. Sharma and S.K. Peddoju, “Response time based load balancing in cloud computing”, International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2014.
 G. Soni and M. Kalra, “A novel approach for load balancing in cloud data centre”, IEEE International Conference on Advance Computing Conference (IACC), 2014.
 S. Dam, G. Mandal, K.D asgupta, and P. Dutta, “An ant colony based load balancing strategy in cloud computing. Springer Advanced Computing”, Networking and Informatics, Vol. 28, 2014.
 M. Mohammadreza, M.R. Amir, and T.C. Anthony “Cloud light weight: a new solution for load balancing in cloud computing”, International Conference on Data Science and Engineering (ICDSE), 2014.
 Shiva Razzaghzadeh, Ahmad Habibizad Navin, Amir Masoud Rahmani, and Mehdi Hosseinzadeh, “Probabilistic Modeling to Achieve Load balancing in Expert Clouds”, Ad Hoc Networks, 2017.
 Hui Wang, Zhihua Cui, Hui Sun, Shahryar Rahnamayan, and Xin-She Yang, “Randomly attracted firefly algorithm with neighbourhood search and dynamic parameter adjustment mechanism”, Springer-Verlag Berlin Heidelberg, 2016.
 Mohit Jain Vijander Singh and Asha Rani, “A novel nature- inspired algorithm for optimization: Squirrel search algorithm”, Swarm and Evolutionary Computation, 2018.
 M. Durairaj and P. Kannan, “Improvised Genetic Approach for an Effective Resource Allocation in Cloud Infrastructure”, International Journal of Computer Science and Information Technologies, Vol. 6, No. 4, 2015.
 V.A. Leena, A.S. Ajeena Beegom, and M.S. Rajasree, “Genetic Algorithm Based Bi-Objective Task Scheduling in Hybrid Cloud Platform”, International Journal of Computer Theory and Engineering, Vol. 8, No. 1, Feb 2016.