An Optimal Time-Based Resource Allocation for Biomedical Workflow Applications in Cloud. Issue 4 (4th July 2022)

• Record Type: Journal Article
• Authors: Mohanapriya, N.; Kousalya, G.; Balakrishnan, P.
• Is Part Of: IETE journal of research. Volume 68:Issue 4(2022)
• Page Start: 2595
• Page End: 2608
• Publication Date: 2022-07-04
• Subjects: Biomedical workflow scheduling -- Biomedical workflows -- Cloud scheduling algorithms -- Workflow planning -- Workflow scheduling; Electronics -- Periodicals; Telecommunication -- Periodicals; Electronics; Telecommunication; Periodicals; 621.38
• DOI: 10.1080/03772063.2020.1724521
• Languages: English
• ISSNs: 0377-2063

Abstract

ABSTRACT: Biomedical workflow applications' necessities have increased with the progress of the biotechnology industry and it involves large volumes of data which require large-scale computation. Due to the upcoming data deluge of biomedical data, processing and scheduling of the same in computing resources have presented many challenges. A biomedical workflow application requires heterogeneous resources for execution. Thus, it utilizes the advantage of cloud computing, as a cloud provides scalable high-performance computing resources. Scheduling of these workflows in the cloud is a crucial problem for which the proposed solution contributes greatly towards execution improvement. The proposed algorithm provides optimal and effective utilization of computational resources by novel scheduling of algorithms. Least Execution Time Cloud Workflow Scheduling (LETCWS) and Revised Heterogeneous Earliest Finish Time (RHEFT) algorithms are proposed and implemented in two distinct levels to schedule the biomedical workflow applications in such a way that it reduces the execution time while reducing the cost. The proposed algorithms are evaluated using the Workflowsim tool with real-world biomedical workflow applications. Experimental results illustrate the performance of scheduling workflow applications over the other existing approaches. The result shows that the proposed algorithm achieves better performance in terms of execution time and cost.