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Improvement QoE-Driven Application Deployment and Energy Module Arrangement in Fog Environments with Offloading

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  • Low Choon Keat

    (Tunku Abdul Rahman University Management and Technologies, Malaysia)

  • Chew Zheng Hing

    (Tunku Abdul Rahman University Management and Technologies, Malaysia)

  • Ng Yen Phing

    (Tunku Abdul Rahman University Management and Technologies, Malaysia)

  • Tew Yiqi

    (Tunku Abdul Rahman University Management and Technologies, Malaysia)

Abstract

The Internet of Things (IoT) and other rapidly evolving technologies have profoundly affected daily life and created an exponential rise in the amount of data generated and processed. By extending cloud capabilities to the network edge, fog computing lowers latency and boosts the effectiveness of data processing. But it also brings with it new difficulties, especially regarding resource management and energy usage. This study starts with a thorough analysis of the current state of fog computing systems, pointing out weaknesses and areas for improvement. We suggest enhancing current QoE-Aware application allocation algorithm, energy-aware module allocation methods, and task-offloading approaches to maximize resource efficiency in light of our research. Experiments in simulated fog computing environments are used to assess these methods, with an emphasis on performance measures including energy-aware module allocation metrics, QoE-aware application allocation enhancement, and offloading applications developing.

Suggested Citation

  • Low Choon Keat & Chew Zheng Hing & Ng Yen Phing & Tew Yiqi, 2025. "Improvement QoE-Driven Application Deployment and Energy Module Arrangement in Fog Environments with Offloading," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(1), pages 3668-3762, January.
    • Handle: RePEc:bcp:journl:v:9:y:2025:i:1:p:3668-3762
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