Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices

By Yuyi Mao, Jun Zhang, Khaled Ben Letaief. IEEE Journal on Selected Areas in Communications, 2016

1,394 citations recorded by the source corpus.

Mobile-edge computing (MEC) is an emerging paradigm to meet the ever-increasing computation demands from mobile applications. By offloading the computationally intensive workloads to the MEC server, the quality of computation experience, e.g., the execution latency, could be greatly improved. Nevertheless, as the on-device battery capacities are limited, computation would be interrupted when the battery energy runs out. To provide satisfactory computation performance as well as achieving green computing, it is of significant importance to seek renewable energy sources to power mobile devices via energy harvesting (EH) technologies. In this paper, we will investigate a green MEC system with EH devices and develop an effective computation offloading strategy. The execution cost, which addresses both the execution latency and task failure, is adopted as the performance metric. A low-complexity online algorithm is proposed, namely, the Lyapunov optimization-based dynamic computation offloading algorithm, which jointly decides the offloading decision, the CPU-cycle frequencies for mobile execution, and the transmit power for computation offloading. A unique advantage of this algorithm is that the decisions depend only on the current system state without requiring distribution information of the computation task request, wireless channel, and EH processes. The implementation of the algorithm only requires to solve a deterministic problem in each time slot, for which the optimal solution can be obtained either in closed form or by bisection search. Moreover, the proposed algorithm is shown to be asymptotically optimal via rigorous analysis. Sample simulation results shall be presented to corroborate the theoretical analysis as well as validate the effectiveness of the proposed algorithm.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/ba2c3ee8422f7cb28d184d0abd7ca6c27669ce8a

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Citations in the corpus

Energy Efficient Federated Learning Over Wireless Communication Networks (2019, background): “The effective switched capacitance in local computation is κ = 10−28 [35].”
Stochastic Joint Radio and Computational Resource Management for Multi-User Mobile-Edge Computing Systems (2017, background): “Moreover, dynamic computation offloading policies have been developed for MEC systems powered by energy harvesting [19] and wireless power transfer [20], for applications where replacing/recharging the device batteries is costly and difficult.”
A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications (2017, methodology): “In [59], a low complexity online algorithm is proposed for a MEC system with energy harvesting devices.”
Energy-Latency Tradeoff for Energy-Aware Offloading in Mobile Edge Computing Networks (2018, background): “[19] proposed a low-complexity Lyapunov optimization-based dynamic computation offloading algorithm to reduce execution time.”
A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms (2019, background): “…illustrate that the proposed approach enables substantial improvements via using a computing access point between the remote cloud and the MD. References [97, 165] consider the cases where the MDs can be powered by energy harvested from the ambient energy sources to further prolong their lifetime.”
UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimization (2018, background): “MEC systems [5–14].”
Data Security and Privacy-Preserving in Edge Computing Paradigm: Survey and Open Issues (2018, background): “Numbers of researchers have addressed the improvement of user experience by offloading the computationally intensive workloads to the edge server in mobile edge computing environment [30]–[33].”
Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks (2017, background): “In the context of MEC, the problem of joint task offloading and resource allocation was studied in a single-user system with energy harvesting devices [29], and in a multi-cell multi-user systems [13]; however the congestion of computing resources at the MEC server was omitted.”
Reducing Offloading Latency for Digital Twin Edge Networks in 6G (2020, neutral): “[18] explored a low-complexity online”
Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing (2017, methodology): “For energy harvesting mobile devices, a dynamic computation offloading policy was proposed in [20] using Lyapunov optimization tech-”
Coded Federated Computing in Wireless Networks with Straggling Devices and Imperfect CSI (2019, neutral): “, [15], [16]).”
Edge of Things: The Big Picture on the Integration of Edge, IoT and the Cloud in a Distributed Computing Environment (2018, background): “The simulation results show that the proposed system outperforms the benchmarked policies [26].”
Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization (2020, background): “The works [15], [16] consider energy-efficient computing in MEC.”
Mobile Edge Intelligence and Computing for the Internet of Vehicles (2019, background): “[35] Y.”