Research Area: Cloud Computing
Cloud computing has attracted scientists to deploy scientific applications by offering services such as Infrastructure-as-a-service (IaaS), Software-as-a-service (SaaS), and Platform-as-a-Service (PaaS). The research community is able to get access to resources on-demand within a short period of time. But, as the demand for cloud resources is dynamic in nature, this affects resource availability during scheduling. Hence, there is a need for efficient management of resources so that tasks can be scheduled based on their execution requirements. To provide a solution, a resource prediction based scheduling approach has been introduced in this paper which automates the resource allocation for scientific applications in a virtualized cloud environment. This research work focuses on the design of an optimized prediction based scheduling approach which maps the tasks of scientific application with the optimal VM by combining the features of swarm intelligence and TOPSIS. The proposed approach minimizes the execution time, cost, and SLA violation rate in comparison to existing scheduling heuristics.
Keywords:
Author(s) Name: Gurleen Kaur & Anju Bala
Journal name: Cluster Computing
Conferrence name:
Publisher name: Springer
DOI: 10.1007/s10586-021-03232-4
Volume Information: volume 24, pages 1955–1974 (2021)
