Efficient resource assignment in mobile edge computing: A dynamic congestion-aware offloading approach

Guo, Kai; Yang, Mingcong; Zhang, Yongbing; Jia, Xiaohua

doi:10.1016/j.jnca.2019.02.017

Cited by 17 publications

(9 citation statements)

References 15 publications

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“…Common methods are primal-dual optimization [10,11], nonlinear optimization [7,12,13] and mixedinteger linear programming problem [14,15]. In [10], an online truthful mechanism based on the primal-dual optimization framework integrating computation and communication resource allocation is proposed .…”

Section: Related Workmentioning

confidence: 99%

Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm

Liao

Peng

et al. 2021

J Cloud Comp

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With the combination of Mobile Edge Computing (MEC) and the next generation cellular networks, computation requests from end devices can be offloaded promptly and accurately by edge servers equipped on Base Stations (BSs). However, due to the densified heterogeneous deployment of BSs, the end device may be covered by more than one BS, which brings new challenges for offloading decision, that is whether and where to offload computing tasks for low latency and energy cost. This paper formulates a multi-user-to-multi-servers (MUMS) edge computing problem in ultra-dense cellular networks. The MUMS problem is divided and conquered by two phases, which are server selection and offloading decision. For the server selection phases, mobile users are grouped to one BS considering both physical distance and workload. After the grouping, the original problem is divided into parallel multi-user-to-one-server offloading decision subproblems. To get fast and near-optimal solutions for these subproblems, a distributed offloading strategy based on a binary-coded genetic algorithm is designed to get an adaptive offloading decision. Convergence analysis of the genetic algorithm is given and extensive simulations show that the proposed strategy significantly reduces the average latency and energy consumption of mobile devices. Compared with the state-of-the-art offloading researches, our strategy reduces the average delay by 56% and total energy consumption by 14% in the ultra-dense cellular networks.

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Section: Related Workmentioning

confidence: 99%

Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm

Liao

Peng

et al. 2021

J Cloud Comp

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“…Ping and traceroute are of well-known commands to measure response time in the field of networking. According to the definition, the summation of the Equations (9) and (10) gives the response time as follows 35 :…”

Section: Response Timementioning

confidence: 99%

“…mobile edge computing (MEC) is an emerging internet‐based computing paradigm that is rapidly developing and adopting by different businesses aiming to gain the highest rank among related under progress technologies . MEC is developed to locate near the mobile devices, usually in one hop, with small‐scale servers located at the edge of the network to meet computation and delay requirements . Since MEC still has some resource constraints such as storage, bandwidth, and CPU, efficient resource management is essential to make EC a reality …”

Section: Introductionmentioning

confidence: 99%

“…7 MEC is developed to locate near the mobile devices, usually in one hop, with small-scale servers located at the edge of the network to meet computation and delay requirements. [8][9][10] Since MEC still has some resource constraints such as storage, bandwidth, and CPU, efficient resource management is essential to make EC a reality. 11 To meet different quality of services (QoS) of computation-intensive user's applications in presence of resource-hungry user equipments, computation offloading has been presented as a promising approach.…”

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confidence: 99%

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A review on the computation offloading approaches in mobile edge computing: A game‐theoreticperspective

2020

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In recent years, novel mobile applications such as augmented reality, virtual reality, and three-dimensional gaming, running on handy mobile devices have been pervasively popular. With rapid developments of such mobile applications, decentralized mobile edge computing (MEC) as an emerging distributed computing paradigm is developed for serving them near the smart devices, usually in one hop, to meet their computation, and delay requirements. In the literature, offloading mechanisms are designed to execute such mobile applications in the MEC environments through transferring resource-intensive tasks to the MEC servers. On the other hand, due to the resource limitations, resource heterogeneity, dynamic nature, and unpredictable behavior of MEC environments, it is necessary to consider the computation offloading issues as the challenging problem in the MEC environment. However, to the best of our knowledge, despite its importance, there is not any systematic, comprehensive, and detailed survey in game theory (GT)-based computation offloading mechanisms in the MEC environment. In this article, we provide a systematic literature review on the GT-based computation offloading approaches in the MEC environment in the form of a classical taxonomy to recognize the state-of-the-art mechanisms on this important topic and to provide open issues as well. The proposed taxonomy is classified into four main fields: classical game mechanisms, auction theory, evolutionary game mechanisms, and hybrid-base game mechanisms. Next, these classes are compared with each other according to the important factors such as performance metrics, case studies, utilized techniques, and evaluation tools, and their advantages and disadvantages are discussed, as well. Finally, open issues and future uncovered or weakly covered research challenges are discussed and the survey is concluded. K E Y W O R D S game theory, mobile edge computing, offloading, systematic review 1 INTRODUCTION Thanks to the recent improvements in hardware and software capabilities and accordingly complex mobile applications of mobile devices, they are becoming more popular in recent years. In the presence of emerging technologies,

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“…By offloading computation‐intensive tasks from mobile devices to the proximate edge servers, the quality of computation experience, e.g. the user energy consumption and the computing delay, could be greatly improved [3, 5, 6].…”

Section: Introductionmentioning

confidence: 99%

Multi‐tier MEC offloading strategy based on dynamic channel characteristics

Yang

Wang

et al. 2020

IET Communications

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Comparing with the cloud computing, mobile edge computing (MEC) can further decrease the latency and improve the stability of the networks. However, it is challenging for the edge servers to deal with the large computation task due to the limited computing capacity. In this study, we design a novel three-layer network architecture consisting of mobile devices, edge cloudlets, and helper cloudlets, where the computing data can be partially processed at the edge cloudlet and helper cloudlet. Based on this, a joint communication, offloading, and computation resource allocation problem is formulated to minimise the computation cost and energy consumption. Due to its difficulty to directly solve the formulated problem, we first propose an offloading scheme to obtain the closed-form solutions for the optimal offloading data size. Next, we decompose the optimisation problem into two subproblems: (i) for the cloud execution, we dynamically adjust the data transmission rate according to the stochastic channel condition, (ii) for the mobile execution, the energy consumption can be further reduced by applying the dynamic voltage and frequency scaling technique. Finally, the numerical results demonstrate the efficiency of the proposed scheme, and show the performance gains in terms of delay, computation cost and energy consumption.

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Efficient resource assignment in mobile edge computing: A dynamic congestion-aware offloading approach

Cited by 17 publications

References 15 publications

Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm

Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm

A review on the computation offloading approaches in mobile edge computing: A game‐theoreticperspective

Multi‐tier MEC offloading strategy based on dynamic channel characteristics

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