Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks

By Ke Zhang, Yuming Mao, Supeng Leng, Quanxin Zhao, Longjiang Li, Xin Peng, Li Pan, Sabita Maharjan, Yan Zhang. IEEE Access, 2016

751 citations recorded by the source corpus.

Mobile edge computing (MEC) is a promising paradigm to provide cloud-computing capabilities in close proximity to mobile devices in fifth-generation (5G) networks. In this paper, we study energy-efficient computation offloading (EECO) mechanisms for MEC in 5G heterogeneous networks. We formulate an optimization problem to minimize the energy consumption of the offloading system, where the energy cost of both task computing and file transmission are taken into consideration. Incorporating the multi-access characteristics of the 5G heterogeneous network, we then design an EECO scheme, which jointly optimizes offloading and radio resource allocation to obtain the minimal energy consumption under the latency constraints. Numerical results demonstrate energy efficiency improvement of our proposed EECO scheme.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/3149e65ebd8f3c0bbab410c8c0733a00a842cde4

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Citations in the corpus

Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization (2020, background): “The works [15], [16] consider energy-efficient computing in MEC.”
Collaborative Computation Offloading for Multiaccess Edge Computing Over Fiber–Wireless Networks (2018, background): “Compared to traditional MCO schemes, MECO can achieve lower latency and higher reliability, and a number of research work on MECO has been proposed over the past several years [16], [17].”
Space-Air-Ground Integrated Network: A Survey (2018, neutral): “networks [75].”
A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications (2017, methodology): “Moreover, in next generation heterogeneous networks, the computation tasks can not only be offloaded to servers but also to devices by utilizing D2D communication [51], [62].”
Joint Task Offloading and Resource Allocation in UAV-Enabled Mobile Edge Computing (2020, background): “[16] proposed an energy-efficient offloading scheme for MEC in 5G heterogeneous networks by formulating the optimization problem with the objective of minimizing the total system energy consumption.”
A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms (2019, background): “In Reference [169], the authors consider 5G heterogeneous networks, including a set of MDs and a macro base station built-in with an edge server.”
Deep reinforcement learning based computation offloading and resource allocation for MEC (2018, background): “But according to [14], [15], the download data rate is very high in general, and the data size of the result is much smaller than that of input data, so the delay and energy consumptions of this step are neglected in the rest of this paper.”
Computation Offloading and Resource Allocation For Cloud Assisted Mobile Edge Computing in Vehicular Networks (2019, background): “Moreover, (15) is a classical maximum cardinality bin packing problem, which is NP-hard [36].”
Energy-Latency Tradeoff for Energy-Aware Offloading in Mobile Edge Computing Networks (2018, background): “The energy consumption [6], [14]–[18] and latency [19]–[21] are usually considered as the criterions for performance evaluation.”
A Survey on the Edge Computing for the Internet of Things (2018, methodology): “in [75] proposed an energyefficient computation offloading scheme, aiming to address the optimization problem.”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, neutral): “Moreover, the joint optimization of offloading decisions and resource allocation has been extensively investigated to improve the network performance [210], [211].”
Edge Computing in 5G: A Review (2019, methodology): “In [68], an architecture is presented to perform energy-aware offloading, whereby each mobile UE decides whether to perform or offload computational tasks to MEC server, in order to reduce energy consumption of MEC.”
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].”
A Survey on Mobile Augmented Reality With 5G Mobile Edge Computing: Architectures, Applications, and Technical Aspects (2021, background): “2) How 5G Helps: Edge offloading is generally considered as a suitable approach to achieve energy efficiency of the user devices [222], [223].”
Joint Load Balancing and Offloading in Vehicular Edge Computing and Networks (2019, background): “Recent works on MEC include those that reduce energy consumption [7], [8], [10], [12], [14], [15], and those that reduce latency [16], [17] by offloading computation-heavy and latency-sensitive tasks to nearby RSUs and/or base stations for remote execution.”