Dynamic Multi-user Computation Offloading for Mobile Edge Computing using Game Theory and Deep Reinforcement Learning

Teymoori, Peyvand; Boukerche, Azzedine

doi:10.1109/icc45855.2022.9838691

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…The new algorithms have performed better than existing methods through simulations, reducing system overhead, profit maximization for MEC servers, and secure caching services in MSNs. These studies' efforts demonstrate the value of game theory in refining MARL methods and providing reliable, practical solutions for decision-making and coordination in multi-agent systems [28]- [30], [46].…”

Section: Mobile Edge Cachingmentioning

confidence: 90%

“…They employ game theory to characterize the compute offloading choice process as a stochastic game to limit user interference on wireless channels. Next, they use a payoff-based MARL to verify the suggested game model's NE exists [46]. Farabaksh discusses the conflict between profit-seeking people's individual advantage and the resource's persistence-based collective well-being.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Each arm is treated as an independent agent and coordination is achieved using a game-theory based distributed coordination strategy. Computation Offloading is also explored through game theory [46]. Formulating the offloading process as a Stochastic game and proposing a payoff-based MARL approach has proven effective.…”

Section: Identifying Potential Areas a Multi-agent Reinforcement Lear...mentioning

confidence: 99%

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Recent Developments of Game Theory and Reinforcement Learning Approaches: A Systematic Review

Jain,

Kumar,

Bhat

2024

IEEE Access

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In the ever-changing world of decision-making, when game theory and reinforcement learning(RL) come together, they create a fascinating combination that shows a new way to solve complex problems in many different fields. The combination of game theory and RL is a powerful convergence that opens up a hopeful new frontier for dealing with difficult decision-making problems in many different fields. Research on the convergence of game theory and RL has shown to be beneficial, providing essential insights into challenging decision-making issues in various disciplines. This study investigates the recent developments of game theory and RL approaches through a systematic review and highlights the significance of game theory in boosting reinforcement algorithms and increasing the interaction of autonomous vehicles, safeguarding edge caching, and more. It offers a thorough account of the developments at the confluence of game theory and RL. The reviewed papers mainly focus on broad themes and address three important research questions: the impact of game theory on multi-agent reinforcement learning (MARL), the significant contributions of game theory to RL, and the significant impact areas. Following the methodology, search outcomes, and study areas is a discussion on game theory-related terminology, followed by study findings. The review's conclusions are offered with ideas for further study and open research questions. The importance of game theory in advancing MARL, the potential of game theory in promoting RL strategies, and the opportunities for combining game theory and RL in cutting-edge fields like mobile edge caching and cyber-physical systems(CPS) are all emphasized in the conclusion. This review article advances our knowledge of the theoretical underpinnings and real-world applications of game theory and RL, laying the groundwork for future improvements in decision-making techniques and algorithms.

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Section: Mobile Edge Cachingmentioning

confidence: 90%

Section: Literature Reviewmentioning

confidence: 99%

Section: Identifying Potential Areas a Multi-agent Reinforcement Lear...mentioning

confidence: 99%

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Recent Developments of Game Theory and Reinforcement Learning Approaches: A Systematic Review

Jain,

Kumar,

Bhat

2024

IEEE Access

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“…Mobility Sensing and Prediction: Research could focus on how to sense and predict user mobility. By collecting data, such as location information and historical movement trajectories of mobile devices [19], models could be constructed using machine learning and data mining techniques to forecast users' future mobility behaviors. Such predictive capabilities could assist in optimizing resource allocation and task scheduling strategies in cloud-edge collaborative networks.…”

Section: Discussionmentioning

confidence: 99%

Optimization of edge computing task offloading based on multi-agent game

Yang,

Liu,

et al. 2024

Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023)

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“…Huang et al [36] addressed the latency optimization offloading problem using non-cooperative game theory and provided a solution. Teymoori et al [37] described the offloading decision process as a stochastic game model to minimize mutual interference during channel access, solving for Nash equilibrium based on multiagent reinforcement learning. Mensah et al [38] combined device-to-device (D2D) communication with vehicular networks, formulating the task offloading and resource allocation problem as a mixed-strategy game and solving for Nash equilibrium.…”

Section: Related Workmentioning

confidence: 99%

A cloud-edge collaborative computing framework using potential games for space-air-ground integrated IoT

Peng,

Guang,

Zhang

et al. 2024

EURASIP J. Adv. Signal Process.

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As a critical component of space-air-ground integrated IoT, the aerial network provides highly reliable, low-latency and ubiquitous information services to ground users by virtue of their high mobility, easy deployment and low cost. However, the current computation and resource management model of air-ground integrated networks are insufficient to meet the latency demanding of emerging intelligent services such as autonomous systems, extended reality and haptic feedback. To tackle these challenges, we propose a computation offloading and optimization method based on potential game. First, we construct an cloud-edge collaborative computing model. Secondly, we construct Offloading Decision Objective Functions (ODOF) with the objective of minimum task processing latency and energy consumption. ODOF is proved to be a Mixed Inferior Nonlinear Programming (MINLP) problem, which is hard to solve. ODOF is converted to be a full potential game, and the Nash equilibrium solution exists. Then, a computational resource allocation algorithm based on Karush–Kuhn–Tucker (KKT) conditions is proposed to solve resource allocation problem. On this basis, a distributed game-based computational offloading algorithm is proposed to minimize the offloading cost. Extensive simulation results demonstrate that the convergence performance of the proposed algorithm is reduced by 50%, the convergence time is reduced by 13.3% and the average task processing delay is reduced by 10%.

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Dynamic Multi-user Computation Offloading for Mobile Edge Computing using Game Theory and Deep Reinforcement Learning

Cited by 8 publications

References 14 publications

Recent Developments of Game Theory and Reinforcement Learning Approaches: A Systematic Review

Recent Developments of Game Theory and Reinforcement Learning Approaches: A Systematic Review

Optimization of edge computing task offloading based on multi-agent game

A cloud-edge collaborative computing framework using potential games for space-air-ground integrated IoT

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