Energy‐efficient computation offloading in 5G cellular networks with edge computing and D2D communications

Jia, Qingmin; Xie, Renchao; Tang, Qinqin; Li, Xiaolu; Huang, Tao; Liu, Jiang; Liu, Yunjie

doi:10.1049/iet-com.2018.5934

Cited by 36 publications

(10 citation statements)

References 43 publications

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“…and condition A2 is satisfied. In summary, formula (18) satisfies conditions A1 and A2; therefore, the optimal stopping rule exists.…”

Section: Proof and Solution Of Optimal Stopping Rulesmentioning

confidence: 91%

“…Cai [17] studied an offloading method of parallel communication and computation in a multiuser system to minimize task delay. Jia et al [18] proposed an energy-saving computing offloading scheme with edge computing and device-to-device (D2D) communication in 5G cellular networks. ey defined the computational offloading problem as a stochastic optimization problem and used the Lyapunov optimization technology framework to solve the problem.…”

Section: Related Workmentioning

confidence: 99%

“…According to literature [25], when the mobile terminal obtains the optimal expected reward V * � sup N∈N * E[Y N ], the stop detection number N is the solution of the optimal stopping rule question formula (18). erefore, the optimal number of stop detection N * is…”

Section: Proof and Solution Of Optimal Stopping Rulesmentioning

confidence: 99%

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Resource Caching and Task Migration Strategy of Small Cellular Networks under Mobile Edge Computing

Liang

Wang

2021

Mobile Information Systems

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As computing-intensive mobile applications become increasingly diversified, mobile devices’ computing power is hard to keep up with demand. Mobile devices migrate tasks to the Mobile Edge Computing (MEC) platform and improve the performance of task processing through reasonable allocation and caching of resources on the platform. Small cellular networks (SCN) have excellent short-distance communication capabilities, and the combination of MEC and SCN is a promising research direction. This paper focuses on minimizing energy consumption for task migration in small cellular networks and proposes a task migration energy optimization strategy with resource caching by combining optimal stopping theory with migration decision-making. Firstly, the process of device finding the MEC platform with the required task processing resources is formulated as the optimal stopping problem. Secondly, we prove an optimal stopping rule’s existence, obtain the optimal processing energy consumption threshold, and compare it with the device energy consumption. Finally, the platform with the best energy consumption is selected to process the task. In the simulation experiment, the optimization strategy has lower average migration energy consumption and higher average data execution energy efficiency and average distance execution energy efficiency, which improves task migration performance by 10% ∼ 60%.

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“…and condition A2 is satisfied. In summary, formula (18) satisfies conditions A1 and A2; therefore, the optimal stopping rule exists.…”

Section: Proof and Solution Of Optimal Stopping Rulesmentioning

confidence: 91%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Resource Caching and Task Migration Strategy of Small Cellular Networks under Mobile Edge Computing

Liang

Wang

2021

Mobile Information Systems

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“…Accessing both the MEC and the fog computing devices can improve energy savings. Towards this goal, an energyefficient joint computation offloading via cellular networks to the MEC server and via D2D communications to fog computing devices in a 5G network has been presented in [144]. Some UEs are deployed around one MEC server in the considered system.…”

Section: Energy Consumption Minimizationmentioning

confidence: 99%

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

et al. 2019

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Multi-access edge computing (MEC) has recently been proposed to aid mobile end devices in providing compute-and data-intensive services with low latency. Growing service demands by the end devices may overwhelm MEC installations, while cost constraints limit the increases of the installed MEC computing and data storage capacities. At the same time, the ever increasing computation capabilities and storage capacities of mobile end devices are valuable resources that can be utilized to enhance the MEC. This article comprehensively surveys the topic area of device-enhanced MEC, i.e., mechanisms that jointly utilize the resources of the community of end devices and the installed MEC to provide services to end devices. We classify the device-enhanced MEC mechanisms into mechanisms for computation offloading and mechanisms for caching. We further subclassify the offloading and caching mechanisms according to the targeted performance goals, which include throughput maximization, latency minimization, energy conservation, utility maximization, and enhanced security. We identify the main limitations of the existing device-enhanced MEC mechanisms and outline future research directions. INDEX TERMS Caching, computation offloading, device-to-device (D2D) communication, mobile edge computing (MEC). I. INTRODUCTION A. MOTIVATION

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“…Figure 1 depicts the task offloading scenario in this paper, where vehicle-to-vehicle (V2V) communication is adopted between vehicles, and vehicle-to-infrastructure (V2I) communication is adopted between vehicle and base station (BS) [25,26]. A cellular network can provide sufficiently stable/reliable wireless communication for V2V and V2I communication.…”

Section: System Modelmentioning

confidence: 99%

Task Offloading Based on Lyapunov Optimization for MEC-Assisted Vehicular Platooning Networks

Cui

Shen

et al. 2019

Sensors

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Due to limited computation resources of a vehicle terminal, it is impossible to meet the demands of some applications and services, especially for computation-intensive types, which not only results in computation burden and delay, but also consumes more energy. Mobile edge computing (MEC) is an emerging architecture in which computation and storage services are extended to the edge of a network, which is an advanced technology to support multiple applications and services that requires ultra-low latency. In this paper, a task offloading approach for an MEC-assisted vehicle platooning is proposed, where the Lyapunov optimization algorithm is employed to solve the optimization problem under the condition of stability of task queues. The proposed approach dynamically adjusts the offloading decisions for all tasks according to data parameters of current task, and judge whether it is executed locally, in other platooning member or at an MEC server. The simulation results show that the proposed algorithm can effectively reduce energy consumption of task execution and greatly improve the offloading efficiency compared with the shortest queue waiting time algorithm and the full offloading to an MEC algorithm.

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Energy‐efficient computation offloading in 5G cellular networks with edge computing and D2D communications

Cited by 36 publications

References 43 publications

Resource Caching and Task Migration Strategy of Small Cellular Networks under Mobile Edge Computing

Resource Caching and Task Migration Strategy of Small Cellular Networks under Mobile Edge Computing

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

Task Offloading Based on Lyapunov Optimization for MEC-Assisted Vehicular Platooning Networks

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