Multi-parametric and priority driven particle swarm (MPPPSO) optimized task scheduling approach for improving performance of fog computing system

Fog computing architecture provides real-time support for service distribution in a smart and IoT-based network. Various industries, hospitals, hostels, and smart environments use the same architecture over cloud computing to optimize the performance and reliability of service distribution. Fog comp...

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Veröffentlicht in:	Progress in artificial intelligence Jg. 14; H. 3; S. 301 - 318
Hauptverfasser:	Monika, Sehrawat, Harkesh
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2025 Springer Nature B.V
Schlagworte:	Algorithms Architecture Artificial Intelligence Cloud computing Computational Intelligence Computer architecture Computer Imaging Computer Science Control Data Mining and Knowledge Discovery Deadlines Edge computing Failure Failure rates Geographical distribution Hostels Mechatronics Methods Natural Language Processing (NLP) Parameter sensitivity Particle swarm optimization Pattern Recognition and Graphics Real time Regular Paper Reliability Resource allocation Robotics Scheduling Smart houses Task scheduling Vision Fog computing Scheduling Particle swarm optimization Resource allocation
ISSN:	2192-6352, 2192-6360
Online-Zugang:	Volltext
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