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

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...

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Veröffentlicht in:Computing Jg. 105; H. 10; S. 2119 - 2142
1. Verfasser: Sobhanayak, Srichandan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Vienna Springer Vienna 01.10.2023
Springer Nature B.V
Schlagworte:
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
ISSN:0010-485X, 1436-5057
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:0010-485X
1436-5057
DOI:10.1007/s00607-023-01175-9