Load Balancing Aware Task Offloading in Mobile Edge Computing

Gao, Yongqiang; Li, Zemin

doi:10.1109/cscwd54268.2022.9776263

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Li et al [19] proposed a distributed task offloading strategy for low-load base stations in the mobile edge computing environment, transforming the energy consumption optimization objective function into a game equation, this strategy is aimed at achieving effective savings in transmission energy by selecting base station offloading based on the game results. Gao et al [20] extended this concept by considering the load of each edge server, they integrated the particle swarm optimization algorithm with deep reinforcement learning (DRL) to identify the optimal node for offloading, with the primary goal of minimizing latency. Lu et al [21] introduced LSTM to improve the proposed DRL-based task offloading strategy, addressing the issue of offloading multiple service nodes and their task dependencies.…”

Section: Related Workmentioning

confidence: 99%

Offloading Strategy based on Graph NeuralReinforcement Learning in Mobile EdgeComputing

Wang,

Sun,

2024

Preprint

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In Mobile Edge Computing (MEC), the service coverage limitation of base stations leads to dynamic changes in the connections between mobile devices and them, directly impacting the decision-making process of agents. The relationship between mobile devices and base stations is abstracted into an MEC structure graph in current research, however, traditional deep reinforcement learning faces challenges in effectively capturing the complex relationships between nodes within dynamic graph structures. To address this issue, this paper proposes a hierarchical mechanism known as Graph Neural Reinforcement Learning (M-GNRL) under multiple constraints to facilitate optimal result. Specifically, when tasks necessitate offloading for execution, M-GNRL algorithm recommends potential offloading nodes based on node representations. Selecting nodes with higher recommended probabilities to reconstruct into a deep reinforcement learning environment, aiming to extract crucial nodes to reduce the state space size of the policy. This strategy incorporates edge features from graph neural networks into the deep reinforcement learning architecture, after training, which can assist the agent in making optimal decisions in dynamic environments. To reduce the time required for mapping MEC scenarios into graph structures, this paper proposes an updating algorithm to acquire graph information that varies with anchor node. Comparative experiments demonstrate that the M-GNRL algorithm outperforms other baseline algorithms in terms of system cost and convergence performance.

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Section: Related Workmentioning

confidence: 99%

Offloading Strategy based on Graph NeuralReinforcement Learning in Mobile EdgeComputing

Wang,

Sun,

2024

Preprint

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“…This strategy is aimed at achieving effective savings in transmission energy by selecting base station offloading based on the game results. Gao et al [20] extended this concept by considering the load of each edge server. They integrated the particle swarm optimization algorithm with deep reinforcement learning (DRL) to identify the optimal node for offloading, with the primary goal of minimizing latency.…”

Section: Related Workmentioning

confidence: 99%

Offloading Strategy Based on Graph Neural Reinforcement Learning in Mobile Edge Computing

Wang,

Ouyang,

Sun

et al. 2024

Electronics

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In the mobile edge computing (MEC) architecture, base stations with computational capabilities are subject to service coverage limitations, and the mobility of devices leads to dynamic changes in their connections, directly impacting the offloading decisions of agents. The connections between base stations and mobile devices, as well as the connections between base stations themselves, are abstracted into an MEC structural diagram due to the difficulty of deep reinforcement learning (DRL) in capturing the complex relationships between nodes and their multi-order neighboring nodes in the graph; decisions solely generated by DRL have limitations. To address this issue, this study proposes a hierarchical mechanism strategy based on Graph Neural Reinforcement Learning (M-GNRL) under multiple constraints. Specifically, the MEC structural graph constructed with the current device as an observation point aggregates to learn node features, thus comprehensively considering the contextual information of nodes, and the learned graph information serves as the environment for deep reinforcement learning, effectively integrating a graph neural network (GNN) with DRL. In the M-GNRL strategy, edge features from GNN are introduced into the architecture of the DRL network to enhance the accuracy of agents’ decision-making. Additionally, this study proposes an updated algorithm to obtain graph data that change with observation points. Comparative experiments demonstrate that the M-GNRL algorithm outperforms other baseline algorithms in terms of system cost and convergence performance.

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“…The latter is solved using a value-based DRL algorithm. In a similar vein, Gao and Li (2022) studied the joint optimization of task offloading and load balancing. In this work, the task offloading problem is solved using the DDPG algorithm and particle swarm optimization is adopted to balance the load between the edge servers.…”

Section: Load Balancing For Mobile Edge Computingmentioning

confidence: 99%

Reinforcement learning for communication load balancing: approaches and challenges

Wu,

Li,

Ferini

et al. 2023

Front. Comput. Sci.

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The amount of cellular communication network traffic has increased dramatically in recent years, and this increase has led to a demand for enhanced network performance. Communication load balancing aims to balance the load across available network resources and thus improve the quality of service for network users. Most existing load balancing algorithms are manually designed and tuned rule-based methods where near-optimality is almost impossible to achieve. Furthermore, rule-based methods are difficult to adapt to quickly changing traffic patterns in real-world environments. Reinforcement learning (RL) algorithms, especially deep reinforcement learning algorithms, have achieved impressive successes in many application domains and offer the potential of good adaptabiity to dynamic changes in network load patterns. This survey presents a systematic overview of RL-based communication load-balancing methods and discusses related challenges and opportunities. We first provide an introduction to the load balancing problem and to RL from fundamental concepts to advanced models. Then, we review RL approaches that address emerging communication load balancing issues important to next generation networks, including 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions for applying RL for communication load balancing.

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Load Balancing Aware Task Offloading in Mobile Edge Computing

Cited by 4 publications

References 11 publications

Offloading Strategy based on Graph NeuralReinforcement Learning in Mobile EdgeComputing

Offloading Strategy based on Graph NeuralReinforcement Learning in Mobile EdgeComputing

Offloading Strategy Based on Graph Neural Reinforcement Learning in Mobile Edge Computing

Reinforcement learning for communication load balancing: approaches and challenges

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