Pavarangkoon, Praphan

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Pavarangkoon, Praphan
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praphan.pa@kmitl.ac.th

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    Item type:Publication,
    Energy-Efficient and Fair Computation Offloading for Multi-user MEC with EH Devices
    (2025-01-01)
    Mustika, I. Wayan
    ;
    Triyanto, Dedi
    ;
    Halimah, Noor Siti
    ;
    Pavarangkoon, Praphan
    The increasing demand for low-latency and energy-efficient mobile applications has propelled the development of Mobile Edge Computing (MEC), enabling the offloading of computational activities from resource-constrained Mobile Devices (MDs) to nearby edge servers. This study examines a joint problem of computation offloading and resource allocation issue in a wireless multi-user, multi-server MEC system with Energy Harvesting (EH) capabilities. Our objective is to reduce long-term energy consumption while adhering to limitations related to latency, energy causality, server capacity, and Signal-To-Interference-Plus-Noise Ratio (SINR). To address the complexities of system dynamics and uncertainty in energy arrivals, we propose a low-complexity online approach utilizing Lyapunov optimization. The proposed method dynamically modifies offloading ratios, transmission power, CPU frequencies, and server allocations without requiring future data. The simulation results show that our method achieves significant energy savings, has low delays, and ensures fairness among users, even in highly congested scenarios. A comparative analysis with benchmark algorithms validates the efficacy and resilience of the proposed framework in real MEC situations.
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    Item type:Publication,
    Fairness-Aware Computation Offloading for Mobile Edge Computing with Energy Harvesting
    (2025-01-01)
    Triyanto, Dedi
    ;
    Wayan Mustika, I.
    ;
    Widyawan
    ;
    Pavarangkoon, Praphan
    Mobile edge computing (MEC) improves network performance by minimizing latency and assigning computing tasks to edge servers. Nonetheless, delegating computations in environments with high device density poses considerable difficulties. Ensuring fairness in resource distribution among users is essential for preserving network stability and user satisfaction in these contexts. This research formulates the Fairness-aware Computation Offloading Optimization (FACOO) algorithm. The Lyapunov approach and sequential least squares quadratic programming (SLSQP) are used to ascertain the best offloading ratio, transmission power, and CPU frequency while complying with signal-to-interference-plus-noise ratio (SINR) limitations. Energy harvesting (EH) is built into FACOO to prolong device battery life and to ensure that MEC systems, which have limited resources, are more sustainable. The results show that FACOO greatly improves throughput and fairness while using significantly less energy, especially in settings with numerous nodes dispersed across large areas. Comprehensive simulations demonstrate that the method effectively balances fairness, throughput, and energy use, making it a workable way to improve resource allocation in MEC systems.