Game Theory for Computation Offloading and Resource Allocation in Edge Computing: A Survey

Zamzam, Marwa; Elshabrawy, Tallal; Ashour, Mohamed

doi:10.1109/niles50944.2020.9257921

Cited by 15 publications

(8 citation statements)

References 35 publications

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“…In the context of multi-users and resource competition, many articles have adopted the algorithmic ideas of game theory, which is a favorable tool for finding mutual satisfaction strategies among multi-users. 20 Moreover, game theory fits well with distributed solutions, which is conducive to the design of low-complexity offloading algorithms. Reference 21 studied the problem of computational offloading and scheduling, and strived to minimize the cost of each mobile device, where the cost was defined as a linear combination of task completion time and energy consumption.…”

Section: Related Workmentioning

confidence: 99%

“…where (20) represents A(t), B(t) are the decision sets of all MIEs within the period t. Equations ( 21) and ( 22) indicate that the selection of service nodes cannot exceed its service scope. Equations ( 23) and (24) indicate that the offloading task can only be offloaded to one service node, and cannot be offloaded to multiple service nodes at the same time.…”

Section: Problem Formulationmentioning

confidence: 99%

“…In the context of multi‐users and resource competition, many articles have adopted the algorithmic ideas of game theory, which is a favorable tool for finding mutual satisfaction strategies among multi‐users 20 . Moreover, game theory fits well with distributed solutions, which is conducive to the design of low‐complexity offloading algorithms.…”

Section: Introductionmentioning

confidence: 99%

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The offloading algorithm of mobile edge computing considering mobility in the intelligent inspection scenario

Xie

Sun

et al. 2022

Trans Emerging Tel Tech

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In industrial scenarios, it is very common to replace manual detection with new intelligent applications such as image recognition based on deep learning. However, these applications often have the characteristics of high computational energy consumption and high computational density. It is impossible to deal with these tasks only by relying on intelligent detection equipment. Mobile edge computing (MEC) is an effective method to solve the problems of high energy consumption and insufficient computing power of detection equipment.A reasonable offloading strategy can reduce equipment energy consumption and reduce system maintenance costs. This article studies the intelligent inspection scenarios of multi-user and multi-MEC servers, and considers the resource competition and mobility among users. We divided the entire inspection process into several computing cycles, and designed a mobile migration model based on the traditional computing offloading model. Finally, a distributed algorithm based on game theory is proposed to solve the problem of minimizing energy consumption. The proposed intelligent inspection and offloading migration algorithm is evaluated through simulation experiments. Theoretical and experimental results show that our method can better adapt to the scenario proposed in this article, with low algorithm complexity and can effectively reduce the energy consumption of the whole inspection system. Compared with random and greedy algorithms, the energy consumption is reduced by 60% and 50% respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The offloading algorithm of mobile edge computing considering mobility in the intelligent inspection scenario

Xie

Sun

et al. 2022

Trans Emerging Tel Tech

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“…However, for larger workloads, the cloud server yields higher efficiency. The work in [47] studies the state-ofthe-art of applying game theoretic framework, which is the same technique that is used in this paper for computation offloading to overcome the edge computing challenges. Some recent studies have integrated MEC in indoor localization platforms.…”

Section: Related Workmentioning

confidence: 99%

A Minimized Latency Collaborative Computation Offloading Game Under Mobile Edge Computing for Indoor Localization

2021

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Indoor localization has become one of the fundamental services that is required in a diverse set of applications these days, such as patient monitoring and smart parking. Highly accurate localization techniques impose high latency and high energy consumption on the underlying application system. Thus, for such indoor location-based application, offloading the computation of the localization process to a remote server with high resource capability has been recently introduced as an avenue to address such a challenge. In this paper, a computation offloading problem is formulated to find the optimal decision with regard to the operation of the localization process. This decision includes: a) Where to compute the localization task, either locally on the end device or on the edge server or on the cloud server, b) Which localization technique should be used, and finally, c) Which transmission technology is recommended to be chosen in combination with the localization technique. All these decisions are constrained by the device, and the servers resource capabilities load. They are also constrained by the fact that the localization algorithm has to satisfy a certain application QoS requirement. Within such context, three algorithms are proposed for task offload decision making. First, the Indoor Localization Latency Optimal Offloading algorithm, which finds the optimal offloading decision that minimizes the total latency of the system and is considered a benchmark for the other algorithms. Second, Indoor Localization Latency Centralized Offloading algorithm that finds a sub optimal solution with lower complexity. Third, Indoor Localization Latency Game-Theoretic Offloading decentralized algorithm that converges after finite improvement steps and achieves Nash equilibrium. Altogether, the paper finds the optimum localization strategy for all users with the minimum latency under mobile edge computing environment.

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“…To satisfy the diverse QoS requirements in the 5G vehicular network, computation power needs to be effectively utilized at cloud, fog, and edge servers and spectrum resource should be fairly allocated to VUEs at RSUs and BSs [15]. Thus, an optimization problem in a joint of computation power and radio resource utilization needs to be addressed to support diverse QoS requirements.…”

Section: Introductionmentioning

confidence: 99%

DRL‐Based Intelligent Resource Allocation for Diverse QoS in 5G and toward 6G Vehicular Networks: A Comprehensive Survey

Nguyen

et al. 2021

Wireless Communications and Mobile Computing

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The vehicular network is taking great attention from both academia and industry to enable the intelligent transportation system (ITS), autonomous driving, and smart cities. The system provides extremely dynamic features due to the fast mobile characteristics. While the number of different applications in the vehicular network is growing fast, the quality of service (QoS) in the 5G vehicular network becomes diverse. One of the most stringent requirements in the vehicular network is a safety-critical real-time system. To guarantee low-latency and other diverse QoS requirements, wireless network resources should be effectively utilized and allocated among vehicles, such as computation power in cloud, fog, and edge servers; spectrum at roadside units (RSUs); and base stations (BSs). Historically, optimization problems have mostly been investigated to formulate resource allocation and are solved by mathematical computation methods. However, the optimization problems are usually nonconvex and hard to be solved. Recently, machine learning (ML) is a powerful technique to cope with the complexity in computation and has capability to cope with big data and data analysis in the heterogeneous vehicular network. In this paper, an overview of resource allocation in the 5G vehicular network is represented with the support of traditional optimization and advanced ML approaches, especially a deep reinforcement learning (DRL) method. In addition, a federated deep reinforcement learning- (FDRL-) based vehicular communication is proposed. The challenges, open issues, and future research directions for 5G and toward 6G vehicular networks, are discussed. A multiaccess edge computing assisted by network slicing and a distributed federated learning (FL) technique is analyzed. A FDRL-based UAV-assisted vehicular communication is discussed to point out the future research directions for the networks.

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Game Theory for Computation Offloading and Resource Allocation in Edge Computing: A Survey

Cited by 15 publications

References 35 publications

The offloading algorithm of mobile edge computing considering mobility in the intelligent inspection scenario

The offloading algorithm of mobile edge computing considering mobility in the intelligent inspection scenario

A Minimized Latency Collaborative Computation Offloading Game Under Mobile Edge Computing for Indoor Localization

DRL‐Based Intelligent Resource Allocation for Diverse QoS in 5G and toward 6G Vehicular Networks: A Comprehensive Survey

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