A review of metaheuristic scheduling techniques in cloud computing

By Mala Kalra, Sarbjeet Singh. 2015

410 citations recorded by the source corpus.

Abstract Cloud computing has become a buzzword in the area of high performance distributed computing as it provides on-demand access to shared pool of resources over Internet in a self-service, dynamically scalable and metered manner. Cloud computing is still in its infancy, so to reap its full benefits, much research is required across a broad array of topics. One of the important research issues which need to be focused for its efficient performance is scheduling. The goal of scheduling is to map tasks to appropriate resources that optimize one or more objectives. Scheduling in cloud computing belongs to a category of problems known as NP-hard problem due to large solution space and thus it takes a long time to find an optimal solution. There are no algorithms which may produce optimal solution within polynomial time to solve these problems. In cloud environment, it is preferable to find suboptimal solution, but in short period of time. Metaheuristic based techniques have been proved to achieve near optimal solutions within reasonable time for such problems. In this paper, we provide an extensive survey and comparative analysis of various scheduling algorithms for cloud and grid environments based on three popular metaheuristic techniques: Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), and two novel techniques: League Championship Algorithm (LCA) and BAT algorithm.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/88dccfc427c57a395c51d9fd304332d07af6ed64

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In Information Systems

1. Afeez Abidemi
2. Emmanuel Gbenga Dada
3. Ban Mohammed Khammas
4. Stephen Bassi Joseph
5. David Opeoluwa Oyewola

Citations in the corpus

• Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems (2022, methodology): “Further readings on metaheuristic algorithms reviews and taxonomies on other subject matters like: scheduling techniques in cloud computing (Kalra and Singh, 2015), vehicle routing problem and its variants (Elshaer and Awad, 2020), optimizing 3D well-path designs (Biswas et al.”