An Optimal Time-Based Resource Allocation for Biomedical Workflow Applications in Cloud

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.

KEYWORDS:

• Biomedical workflow scheduling
• Biomedical workflows
• Cloud scheduling algorithms
• Workflow planning
• Workflow scheduling

Additional information

Notes on contributors

N. Mohanapriya

N Mohanapriya is currently pursuing PhD in the Department of Computer Science and Engineering at Coimbatore Institute of Technology, Coimbatore, India. She obtained her MSc degree in software engineering from Anna University. She has three years of research experience and her research interests include workflow management systems, workflow scheduling, cloud, and distributed systems.

G. Kousalya

G Kousalya is currently working as a professor and head in the Department of Computer Science and Engineering at Coimbatore Institute of Technology, Coimbatore, India. She did her PhD in computer science and engineering at Anna University. She has 27 years of teaching and research experience. Her research area of interests include distributed systems, networks, cloud computing, and machine learning. She has published numerous research articles in refereed international journals and international conferences. Email: [email protected]

P. Balakrishnan

P Balakrishnan is currently working as an associate professor in School of Computer Science and Engineering (SCOPE) at Vellore Institute of Technology, Vellore, India. He did his PhD in computer science and engineering at Anna University. His research area of interests includes cloud computing, multi-cloud scheduling, virtualization, grid computing, and machine learning. He has published several research articles in national/international journals and international conferences. Email: [email protected]