A systematic mapping study on soft computing techniques to cloud environment

Abstract Cloud computing plays an essential role in storage and transfer of big capacity data due to a rapid increase in size and the number of organizational activities. There exist numerous studies in which diverse soft computing techniques are applied to the cloud environment. The relevant extant literature that were clustered into five main categories with respect to precedence are; task optimization, power optimization, security, service selection and cost optimization. Yet, it was discovered that there is a dearth of systematic review/mapping studies particularly on soft computing techniques in cloud environment so as to obtain exclusive insight, to identify existing gaps and future research directions. Therefore the aim of this paper is to conduct a systematic mapping study of recent literature on soft computing techniques in cloud environment. For this purpose, 163 articles were chosen as primary sources that were published within the last decade, which were classified based on study focus area, type of research, contribution facet and particularly the type of soft computing technique used. Findings revealed that task optimization takes part as the highly preferred research focus area. Secondly, most of the articles found are of validation studies. The contributions of most of the studies are concerned about methods and finally the top three soft computing techniques were detected as particle swarm optimization (PSO), genetic algorithm (GA) and hybrid systems. The results of this study confirm that applying soft computing techniques in cloud computing has gained more and more significant attention recently but there still remain challenges and gaps which calls for further investigation especially in the area of cost optimization and also artificial bee colony.

[1]  Jun Zhang,et al.  Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches , 2015, ACM Comput. Surv..

[2]  Mohammad Kazem Akbari,et al.  Using of Machine Learning into Cloud Environment (A Survey): Managing and Scheduling of Resources in Cloud Systems , 2012, 2012 Seventh International Conference on P2P, Parallel, Grid, Cloud and Internet Computing.

[3]  Mohammad Masdari,et al.  A Survey of PSO-Based Scheduling Algorithms in Cloud Computing , 2016, Journal of Network and Systems Management.

[4]  Christian Esposito,et al.  Smart Cloud Storage Service Selection Based on Fuzzy Logic, Theory of Evidence and Game Theory , 2016, IEEE Transactions on Computers.

[5]  Pascal Bouvry,et al.  A Survey of Evolutionary Computation for Resource Management of Processing in Cloud Computing [Review Article] , 2015, IEEE Computational Intelligence Magazine.

[6]  Pascal Bouvry,et al.  Computational intelligence for cloud management current trends and opportunities , 2013, 2013 IEEE Congress on Evolutionary Computation.

[7]  Mehmet Demirci,et al.  A Survey of Machine Learning Applications for Energy-Efficient Resource Management in Cloud Computing Environments , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).

[8]  Christine Morin,et al.  Energy-Aware Ant Colony Based Workload Placement in Clouds , 2011, 2011 IEEE/ACM 12th International Conference on Grid Computing.

[9]  Farookh Khadeer Hussain,et al.  Task-Based System Load Balancing in Cloud Computing Using Particle Swarm Optimization , 2013, International Journal of Parallel Programming.

[10]  K. Chandra Sekaran,et al.  Survey on meta heuristic optimization techniques in cloud computing , 2016, 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI).