Computation Offloading and Resource Allocation for Backhaul Limited Cooperative MEC Systems

Nguyen, Phuong-Duy; Ha, Vu Nguyen; Le, Long Bao

doi:10.1109/vtcfall.2019.8891244

Cited by 12 publications

(14 citation statements)

References 16 publications

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“…Consequently, various task-offloading decision methods are proposed to minimize the energy consumption of a user device. These methods take into account factors such as computing power, transmission power, and task completion delay [21][22][23].…”

Section: Energy Saving In An Mec Systemmentioning

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: Energy Saving In An Mec Systemmentioning

confidence: 99%

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

Koo,

Park

2024

Applied Sciences

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“…For example, UE can connect to the MEC in two approaches, one is via 5G access to the gNB, and the other is via WiFi access to an AP [34]. In addition, depending on the number of antennas, the categories of BS can contain single-antenna BS [35] or multi-antenna BS [36].…”

Section: Components Of Network Architecturementioning

confidence: 99%

A Survey of Energy Optimization Approaches for Computational Task Offloading and Resource Allocation in MEC Networks

Yang,

Shah,

Pezaros

2023

Electronics

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With the increased penetration of cloud computing and virtualization, a plethora of internet of things devices have been deployed globally. As a result, computationally intensive tasks are transmitted from the edge towards the centralized cloud for processing that leads to increased energy utilization in the cloud data centers while at the same increasing significant latency for critical applications. Recent years have witnessed a paradigm shift from centralized cloud computing towards mobile edge computing (MEC), where computational tasks are offloaded at the edge servers near user equipment (UE). This paradigm leads to lowering the energy utilization in the cloud data centers, along with low latency for UE and efficient resource utilization at the edge. In this context, the scale and complexity of the MEC networks is drastically increasing and, consequently, finding effective energy-efficient solutions for computational task offloading and resource allocation in MEC networks has become an ambitious task. To address the aforementioned challenges, this work surveys the state of the art in different categorizations of algorithm-based computational task offloading and resource allocation strategies focusing on energy utilization. It also provides a detailed cross-comparison of existing strategies in terms of their implementation specifications. Additionally, this paper also highlights open challenges and potential future research directions to facilitate efficient task offloading and resource allocation at the edge with reduced energy consumption at the centralized data centers. Our work also paves the way for the deployment of critical applications at the edge that require low latency and high service quality guarantees.

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“…This algorithm can greatly improve the task completion efficiency of users with insufficient power. Cost and latency tradeoff algorithm (CLT) [52] token the cost of task uploading, task execution, and result transmission as offloading cost. Each user will solve his own optimization problem when controlling resource allocation to maximize his role.…”

Section: Benchmark Algorithmsmentioning

confidence: 99%

Deep reinforcement learning-based resource allocation and seamless handover in multi-access edge computing based on SDN

Zhang

Luo

2021

Knowl Inf Syst

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With the access devices that are densely deployed in multi-access edge computing environments, users frequently switch access devices when moving, which causes the imbalance of network load and the decline of service quality. To solve the problems above, a seamless handover scheme for wireless access points based on perception is proposed. First, a seamless handover model based on load perception is proposed to solve the unbalanced network load, in which a seamless handover algorithm for wireless access points is used to calculate the access point with the highest weight, and a software-defined network controller controls the switching process. A joint allocation method of communication and computing resources based on deep reinforcement learning is proposed to minimize the terminal energy consumption and the system delay. A resource allocation model is based on minimizing terminal energy consumption, and system delay is built. The optimal value of task offloading decision and resource allocation vector are calculated with deep reinforcement learning. Experimental results show that the proposed method can reduce the network load and the task execution cost.

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Computation Offloading and Resource Allocation for Backhaul Limited Cooperative MEC Systems

Cited by 12 publications

References 16 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

A Survey of Energy Optimization Approaches for Computational Task Offloading and Resource Allocation in MEC Networks

Deep reinforcement learning-based resource allocation and seamless handover in multi-access edge computing based on SDN

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