MOHBA:multi-objective workflow scheduling in cloud computing using hybrid BAT algorithm

• Published in: Computing Vol. 105; no. 10; pp. 2119 - 2142
• Main Author: Sobhanayak, Srichandan
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01.10.2023; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Cloud computing; Computer Appl. in Administrative Data Processing; Computer Communication Networks; Computer Science; Cost analysis; Energy consumption; Heuristic methods; Information Systems Applications (incl.Internet); Multiple objective analysis; Optimization; Regular Paper; Resource utilization; Scheduling; Scientists; Software Engineering; Variance analysis; Workflow software; Task scheduling; Cloud computing; Directed acyclic graph; 68M20; Infrastructure-as-a-service; Virtualization
• DOI: 10.1007/s00607-023-01175-9
• ISSN: 0010-485X

Workflow applications are a popular tool used by scientists to model and run applications on cloud computing. With the rise of multi-core parallel systems and cloud computing high-performance computing is becoming more accessible to scientists, but it also presents new challenges. One pressing issue is the energy consumption of these systems, both for environmental and financial reasons. To minimize energy usage in cloud computing, methods such as energy-efficient scheduling are gaining attention. However, current scheduling solutions have limitations, or failing to address the problem as a multi-objective optimization balancing performance and energy. This problem is considered complex and NP-complete. Many researchers have attempted to resolve it using heuristic and meta-heuristic methods. Although these methods may not always provide optimal results, they are still a subject of active research. This paper proposes a new multi-objective optimization algorithm that combines the Heterogeneous Earliest Finish Time and BAT algorithm to optimize multiple conflicting objectives for energy, cost, makespan, and resource utilization. The results are verified using the Analysis of Variance statistical tool, and the proposed algorithm is shown to be superior to existing contemporary algorithms.