An Energy-Efficient Sleep Management Algorithm for UDN with Edge Caching

Wu, Tianhao; Li, Xi; Ji, Hong; Zhang, Heli

doi:10.1109/glocomw.2017.8269189

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…The authors in [33] use the particle swarm optimization (PSO) algorithm to control the operation mode of MECSs. They formulate the MECS sleep control problem as a twodimensional optimization problem.…”

Section: Sleep Control Methodsmentioning

confidence: 99%

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%

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

Koo,

Park

2024

Applied Sciences

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“…More recently, there has been a renewed focus on caching policy combined with sleeping strategy for further conservation of energy. In [31], a user connection matrix-based access point (AP) sleeping algorithm is proposed to minimise the energy consumption. Each AP will randomly store a part of each file, and the connection state matrix for users and the active APs in the local area is established.…”

Section: Related Workmentioning

confidence: 99%

“…Since the cached files with the more popularity have larger probabilities to be requested, the average delay decreases with the increase of σ. However, the average delay exhibits stable trend for the strategy caching most popular content with the same file size due to the same popularity distribution parameter for each file, in which BSs or UEs cache the file content based on the popularity of each file as in [31][32][33]. As a benchmark, the exhaustive-search results for sleeping problem are presented.…”

Section: Mean Delaymentioning

confidence: 99%

“…Combining the sleeping strategy with the caching scheme further reduced the energy consumption by a considerable amount. However, since the optimal sleeping strategy is obtained by particle swarm optimisation algorithm in [31] with the aim of minimising system energy consumption, it can save more energy compared with our proposed algorithm, and the weighting factor has no impact on it due to the neglect of delay performance. Fig.…”

Section: Mean Delaymentioning

confidence: 99%

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