Bio-Inspired Sleep Control for Improving the Energy Efficiency of a MEC System

Park, Jaesung; Lim, Yujin

doi:10.3390/app13042620

Cited by 4 publications

(2 citation statements)

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“…Then, they propose a user connection matrix-based AP sleeping method by using PSO. In the work presented in [12], a bio-inspired method for controlling the sleep states of MECSs is proposed, drawing inspiration from the inter-cell signaling mechanism. At the end of each time slot, each MECS engages in periodic load information exchanges with its neighboring MECSs.…”

Section: Sleep Control Methodsmentioning

confidence: 99%

“…However, the uneven distribution of the load across MECSs at each time step prompts the exploration of comprehensive load distribution considerations to optimize system-wide energy efficiency. Addressing this, cooperative sleep decision methods have been proposed [9,12], where MECSs form clusters, exchange status information, and make sleep decisions, considering the status of other MECSs in the same cluster. Despite the potential enhancement in energy efficiency, such cooperative methods introduce additional signaling overhead, and when MECS decisions differ, an iterative consensus process ensues, potentially leading to delayed decision making.…”

Section: Introductionmentioning

confidence: 99%

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MEC Server Sleep Strategy for Energy Efficient Operation of an MEC System

Koo,

Park

2024

Applied Sciences

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Optimizing the energy consumption of an MEC (Multi-Access Edge Computing) system is a crucial challenge for operation cost reduction and environmental conservation. In this paper, we address an MECS (MEC Server) sleep control problem that aims to reduce the energy consumption of the system while providing users with a reasonable service delay by adjusting the number of active MECSs according to the load imposed on the system. To tackle the problem, we identify two crucial issues that influence the design of an effective sleep control technique and propose methods to address each of these issues. The first issue is accurately predicting the system load. Changes in system load are spatio-temporally correlated among MECSs. By leveraging such correlation information with STGCN (Spatio-Temporal Graph Convolutional Network), we enhance the prediction accuracy of task arrival rates for each MECS. The second issue is rapidly selecting MECSs to sleep when the load distribution over an MEC system is given. The problem of choosing sleep MECS is a combinatorial optimization problem with high time complexity. To address the issue, we employ a genetic algorithm and quickly determine the optimal sleep MECS with the predicted load information for each MECS. Through simulation studies, we verify that compared to the LSTM (Long Short-Term Memory)-based method, our method increases the energy efficiency of an MEC system while providing a compatible service delay.

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Section: Sleep Control Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MEC Server Sleep Strategy for Energy Efficient Operation of an MEC System

Koo,

Park

2024

Applied Sciences

Self Cite

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Reducing Service Response Time Using Genetic Algorithm in Mobile Edge Computing

Yao,

Dai,

Zheng

et al. 2023

2023 2nd International Conference on Artificial Intelligence and Blockchain Technology (AIBT)

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MEC Server Status Optimization Framework for Energy Efficient MEC Systems by Taking a Deep-Learning Approach

Koo,

Park

2024

Future Internet

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Reducing energy consumption in a MEC (Multi-Access Edge Computing) system is a critical goal, both for lowering operational expenses and promoting environmental sustainability. In this paper, we focus on the problem of managing the sleep state of MEC servers (MECSs) to decrease the overall energy consumption of a MEC system while providing users acceptable service delays. The proposed method achieves this objective through dynamic orchestration of MECS activation states based on systematic analysis of workload distribution patterns. To facilitate this optimization, we formulate the MECS sleep control mechanism as a constrained combinatorial optimization problem. To resolve the formulated problem, we take a deep-learning approach. We develop a task arrival rate predictor using a spatio-temporal graph convolution network (STGCN). We then integrate this predicted information with the queue length distribution to form the input state for our deep reinforcement learning (DRL) agent. To verify the effectiveness of our proposed framework, we conduct comprehensive simulation studies incorporating real-world operational datasets, with comparative evaluation against established metaheuristic optimization techniques. The results indicate that our method demonstrates robust performance in MECS state optimization, maintaining operational efficiency despite prediction uncertainties. Accordingly, the proposed approach yields substantial improvements in system performance metrics, including enhanced energy utilization efficiency, decreased service delay violation rate, and reduced computational latency in operational state determination.

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Bio-Inspired Sleep Control for Improving the Energy Efficiency of a MEC System

Cited by 4 publications

References 32 publications

MEC Server Sleep Strategy for Energy Efficient Operation of an MEC System

MEC Server Sleep Strategy for Energy Efficient Operation of an MEC System

Reducing Service Response Time Using Genetic Algorithm in Mobile Edge Computing

MEC Server Status Optimization Framework for Energy Efficient MEC Systems by Taking a Deep-Learning Approach

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