Performance Guaranteed Computation Offloading for Mobile-Edge Cloud Computing

Tao, Xiaoyi; Ota, Kaoru; Dong, Mianxiong; Qi, Heng; Li, Keqiu

doi:10.1109/lwc.2017.2740927

Cited by 205 publications

(114 citation statements)

References 9 publications

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“…Assigning computing tasks to the base stations (BS) can help mobile users improve computing performance. As pioneering literatures already examined mobile cloud computing (e.g., [10,15,21]) and mobile networking (e.g., [22,23]), and to enable tractable performance analysis and obtain useful insights, we consider the application scenario as quasistatic, where the set of MDs will not be changed during a computation offloading period. A set of N � 1, 2, .…”

Section: System Modelmentioning

confidence: 99%

Energy-Efficient Computation Offloading and Transmit Power Allocation Scheme for Mobile Edge Computing

Zhao

et al. 2019

Mobile Information Systems

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Mobile edge computing (MEC) is considered a promising technique that prolongs battery life and enhances the computation capacity of mobile devices (MDs) by offloading computation-intensive tasks to the resource-rich cloud located at the edges of mobile networks. In this study, the problem of energy-efficient computation offloading with guaranteed performance in multiuser MEC systems was investigated. Given that MDs typically seek lower energy consumption and improve the performance of computing tasks, we provide an energy-efficient computation offloading and transmit power allocation scheme that reduces energy consumption and completion time. We formulate the energy efficiency cost minimization problem, which satisfies the completion time deadline constraint of MDs in an MEC system. In addition, the corresponding Karush–Kuhn–Tucker conditions are applied to solve the optimization problem, and a new algorithm comprising the computation offloading policy and transmission power allocation is presented. Numerical results demonstrate that our proposed scheme, with the optimal computation offloading policy and adapted transmission power for MDs, outperforms local computing and full offloading methods in terms of energy consumption and completion delay. Consequently, our proposed system could help overcome the restrictions on computation resources and battery life of mobile devices to meet the requirements of new applications.

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Section: System Modelmentioning

confidence: 99%

Energy-Efficient Computation Offloading and Transmit Power Allocation Scheme for Mobile Edge Computing

Zhao

et al. 2019

Mobile Information Systems

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“…Li et al in [24] studied the computation offloading issue for deep learning applications. Tao et al in [25] investigated the performance guaranteed computation offloading for mobileedge cloud computing. Chen et al in [26] considered to exploit the D2D communications to assist the mobile edge computing.…”

Section: Related Workmentioning

confidence: 99%

Efficient Resource Allocation for On-Demand Mobile-Edge Cloud Computing

et al. 2018

IEEE Trans. Veh. Technol.

113

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Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. Aiming at provisioning flexible on-demand mobile-edge cloud service, in this paper we propose a comprehensive framework consisting of a resource-efficient computation offloading mechanism for users and a joint communication and computation (JCC) resource allocation mechanism for network operator. Specifically, we first study the resource-efficient computation offloading problem for a user, in order to reduce user's resource occupation by determining its optimal communication and computation resource profile with minimum resource occupation and meanwhile satisfying the QoS constraint. We then tackle the critical problem of user admission control for JCC resource allocation, in order to properly select the set of users for resource demand satisfaction. We show the admission control problem is NP-hard, and hence develop an efficient approximation solution of a low complexity by carefully designing the user ranking criteria and rigourously derive its performance guarantee. To prevent the manipulation that some users may untruthfully report their valuations in acquiring mobile-edge cloud service, we further resort to the powerful tool of critical value approach to design truthful pricing scheme for JCC resource allocation. Extensive performance evaluation demonstrates that the proposed schemes can achieve superior performance for on-demand mobile-edge cloud computing.

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“…A major challenge in realizing these computing-intensive immersive applications is the low computing capability of user devices. To alleviate computing capacity constraints and reduce latency, mobile edge computing (MEC) has emerged as a promising platform for users to offload computing tasks via high-speed wireless links to nearby macro base stations (MBS) equipped with high-capacity computing resources [3], [4]. In [5], the offloading latency of a multi-user MEC system was minimized by jointly optimizing the time slot and computing capacity.…”

Section: Introductionmentioning

confidence: 99%

Offloading Optimization for Low-Latency Secure Mobile Edge Computing Systems

Zhou

Yeoh

Pan

et al. 2020

IEEE Wireless Commun. Lett.

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This paper proposes a low-latency secure mobile edge computing (MEC) system where multiple users offload computing tasks to a base station in the presence of an eavesdropper. We jointly optimize the users' transmit power, computing capacity allocation, and user association to minimize the computing and transmission latencies over all users subject to security and computing resource constraints. Numerical results show that our proposed algorithm outperforms baseline strategies. Furthermore, we highlight a novel trade-off between the latency and security of MEC systems.

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Performance Guaranteed Computation Offloading for Mobile-Edge Cloud Computing

Cited by 205 publications

References 9 publications

Energy-Efficient Computation Offloading and Transmit Power Allocation Scheme for Mobile Edge Computing

Energy-Efficient Computation Offloading and Transmit Power Allocation Scheme for Mobile Edge Computing

Efficient Resource Allocation for On-Demand Mobile-Edge Cloud Computing

Offloading Optimization for Low-Latency Secure Mobile Edge Computing Systems

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