TM-MOAOA: a two-stage task scheduling approach using TOPSIS and multi-objective Archimedes optimization in fog-cloud environment

The explosive growth of the Internet of Things (IoT) has introduced significant challenges in real-time data processing, placing substantial pressure on fog and cloud computing infrastructures. Efficient and rapid task processing is essential to manage the massive volume of heterogeneous tasks with...

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Veröffentlicht in:	Computing Jg. 107; H. 7; S. 155
Hauptverfasser:	Khaledian, Navid, Razzaghzadeh, Shiva, Moazzami, Setareh, Kivi, Parisa Norouzi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Vienna Springer Vienna 01.07.2025 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Cloud computing Computer Appl. in Administrative Data Processing Computer Communication Networks Computer Science Data processing Deadlines Decision making Deep learning Energy consumption Energy efficiency Fault tolerance Fuzzy logic Genetic algorithms Heuristic Information Systems Applications (incl.Internet) Internet of Things Load balancing Load distribution (forces) Machine learning Methods Multiple criterion Multiple objective analysis Operating costs Optimization algorithms Particle swarm optimization Performance measurement Real time Regular Paper Resource allocation Scheduling Software Engineering Task scheduling Workloads Cloud computing Makespan Multi-objective Archimedes optimization Load balancing Multi-criteria decision making
ISSN:	0010-485X, 1436-5057
Online-Zugang:	Volltext
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