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Issue title: Special Section: Intelligent, Smart and Scalable Cyber-Physical Systems
Guest editors: V. Vijayakumar, V. Subramaniyaswamy, Jemal Abawajy and Longzhi Yang
Article type: Research Article
Authors: Banerjee, Anuradhaa; * | Akbar Hussain, D.M.b
Affiliations: [a] Department of Computer Applications, Kalyani Govt. Engg. College, Kalyani, Nadia, West Bengal, India | [b] Department of Energy Technology, Aalborg University, Esbjerg, Denmark
Correspondence: [*] Corresponding author. Anuradha Banerjee, Department of Computer Applications, Kalyani Govt. Engg. College, Kalyani, Nadia, West Bengal, India. E-mail: anuradha79bn@gmail.com.
Abstract: Efficient servicing of requests in cloud environment has become need of the hour. Cloud services work based on zones in various locations and multiple service requests may be simultaneously considered as a batch and allocated to various zones. Experience-based Efficient Scheduling or EXES focuses on achieving minimum possible waiting time for a batch of requests, under the constraint that overall allocation cost should be less than or equal to a budget limit. Migration of tasks is also possible to balance loads if budget permits and we gain in energy. For each task in a batch and all available zones, a priority value is computed based on previous interaction experience of the zone and the site that generated this task. The zone that produces highest priority for a task, is allocated the task. An SDN controller is in charge of the entire process of priority computation and assigning tasks to zones. Priority is given to requests generating from sites that consumed lesser execution time compared to other sites that have generated requests in request queue of the zone. To the best of authors’ knowledge, no existing scheduling scheme in cloud has considered batch processing based on service process experience of zones.
Keywords: Allocation, budget, cloud, load, minimum waiting time, priority, software-defined-networks
DOI: 10.3233/JIFS-169987
Journal: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4305-4317, 2019
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