An energy-efficient and deadline-aware workflow scheduling algorithm in the fog and cloud environment

The Internet of Things (IoT) is constantly evolving. The variety of IoT applications has caused new demands to emerge on users’ part and competition between computing service providers. On the one hand, an IoT application may exhibit several important criteria, such as deadline and runtime simultane...

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Vydáno v:Computing Ročník 106; číslo 1; s. 109 - 137
Hlavní autoři: Khaledian, Navid, Khamforoosh, Keyhan, Akraminejad, Reza, Abualigah, Laith, Javaheri, Danial
Médium: Journal Article
Jazyk:angličtina
Vydáno: Vienna Springer Vienna 01.01.2024
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Cloud computing
Computer Appl. in Administrative Data Processing
Computer Communication Networks
Computer Science
Deadlines
Edge computing
Energy consumption
Information Systems Applications (incl.Internet)
Internet of Things
Particle swarm optimization
Regular Paper
Scheduling
Simulated annealing
Software Engineering
Workflow
Fog computing
Deadline
Energy efficiency
68M20
Metaheuristic algorithm
Workflow scheduling
Particle swarm optimization
ISSN:0010-485X, 1436-5057
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Shrnutí:The Internet of Things (IoT) is constantly evolving. The variety of IoT applications has caused new demands to emerge on users’ part and competition between computing service providers. On the one hand, an IoT application may exhibit several important criteria, such as deadline and runtime simultaneously, and it is confronted with resource limitations and high energy consumption on the other hand. This has turned to adopting a computing environment and scheduling as a fundamental challenge. To resolve the issue, IoT applications are considered in this paper as a workflow composed of a series of interdependent tasks. The tasks in the same workflow (at the same level) are subject to priorities and deadlines for execution, making the problem far more complex and closer to the real world. In this paper, a hybrid Particle Swarm Optimization and Simulated Annealing algorithm (PSO–SA) is used for prioritizing tasks and improving fitness function. Our proposed method managed the task allocation and optimized energy consumption and makespan at the fog-cloud environment nodes. The simulation results indicated that the PSO–SA enhanced energy and makespan by 5% and 9% respectively on average compared with the baseline algorithm (IKH-EFT).
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ISSN:0010-485X
1436-5057
DOI:10.1007/s00607-023-01215-4