Journal ArticleDOI
Energy-Aware Task Offloading and Resource Allocation for Time-Sensitive Services in Mobile Edge Computing Systems
Mingxiong Zhao,Jun-Jie Yu,Wen-Tao Li,Di Liu,Shaowen Yao,Wei Feng,Changyang She,Tony Q. S. Quek +7 more
TLDR
In this article, the authors jointly optimize task offloading and resource allocation to minimize the energy consumption subject to the latency requirement, and propose an iterative algorithm to deal with them in a sequence.Abstract:
Mobile Edge Computing (MEC) is a promising architecture to reduce the energy consumption of mobile devices and provide satisfactory quality-of-service to time-sensitive services. How to jointly optimize task offloading and resource allocation to minimize the energy consumption subject to the latency requirement remains an open problem, which motivates this paper. When the latency constraint is taken into account, the optimization variables, including offloading ratio, transmission power, and subcarrier and computing resource allocation, are strongly coupled. To address this issue, we first decompose the original problem into three subproblems named as offloading ratio selection, transmission power optimization, and subcarrier and computing resource allocation. Then, we propose an iterative algorithm to deal with them in a sequence. To be specific, we derive the closed-form solution of offloading ratios, employ the equivalent parametric convex programming to obtain the optimal power allocation policy, and deal with subcarrier and computing resource allocation by the primal-dual method. Simulation results demonstrate that the proposed algorithm can save 20%–40% energy compared with the reference schemes, and can converge to local optimal solutions.read more
Citations
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Journal ArticleDOI
Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network
Xing Chen,Guizhong Liu +1 more
TL;DR: This article forms the joint optimization problem of task offloading and resource allocation to minimize the energy consumption of all Internet of Things (IoT) devices subject to delay threshold and limited resources and proposes a two-timescale federated deep reinforcement learning algorithm based on Deep Deterministic Policy Gradient framework (FL-DDPG).
Journal ArticleDOI
Performance Optimization of Serverless Computing for Latency-Guaranteed and Energy-Efficient Task Offloading in Energy-Harvesting Industrial IoT
TL;DR: In this paper , the authors proposed a latency-guaranteed and energy-efficient task offloading (LETO) system where an Internet of Things (IoT) device decides the number of stateless functions requested to the cloud by considering the deadline on the task completion time and its energy level.
Journal ArticleDOI
A Smart Road Side Unit in a Microeolic Box to Provide Edge Computing for Vehicular Applications
TL;DR: In this paper , the authors proposed VMEC-in-a-box, a smart RSU combined with a MEC station, aimed at providing edge computing for vehicular applications by enabling job offloading from vehicles.
Journal ArticleDOI
A Smart Road Side Unit in a Microeolic Box to Provide Edge Computing for Vehicular Applications
TL;DR: In this paper , the authors proposed VMEC-in-a-box, a smart RSU combined with a MEC station, aimed at providing edge computing for vehicular applications by enabling job offloading from vehicles.
Journal ArticleDOI
Performance Optimization of Serverless Computing for Latency-Guaranteed and Energy-Efficient Task Offloading in Energy-Harvesting Industrial IoT
TL;DR: In this article , the authors proposed a latency-guaranteed and energy-efficient task offloading (LETO) system where an Internet of Things (IoT) device decides the number of stateless functions requested to the cloud by considering the deadline on the task completion time and its energy level.
References
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Journal ArticleDOI
Mobile-Edge Computing: Partial Computation Offloading Using Dynamic Voltage Scaling
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