Computation Offloading Based on Cooperations of Mobile Edge Computing-Enabled Base Stations

Fan, Wenhao; Liu, Yuanan; Tang, Bihua; Wu, Fan; Wang, Zhongbao

doi:10.1109/access.2017.2787737

Cited by 68 publications

(39 citation statements)

References 17 publications

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“…Although the MEC servers endow computational resources, the computing capabilities are limited due to the installation and operation maintenance cost. In order to prevent the quality of service (QoS) degradation when the traffic load is huge, the MEC servers can further relay the excessive workloads to the remote cloud [27], [32], or other around servers [28], [29]. The tradeoffs between the computation delay and communication delay are considered.…”

Section: Related Workmentioning

confidence: 99%

Efficient Mobility-Aware Task Offloading for Vehicular Edge Computing Networks

et al. 2019

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Vehicular networks are facing the challenges to support ubiquitous connections and high quality of service for numerous vehicles. To address these issues, mobile edge computing (MEC) is explored as a promising technology in vehicular networks by employing computing resources at the edge of vehicular wireless access networks. In this paper, we study the efficient task offloading schemes in vehicular edge computing networks. The vehicles perform the offloading time selection, communication, and computing resource allocations optimally, the mobility of vehicles and the maximum latency of tasks are considered. To minimize the system costs, including the costs of the required communication and computing resources, we first analyze the offloading schemes in the independent MEC servers scenario. The offloading tasks are processed by the MEC servers deployed at the access point (AP) independently. A mobility-aware task offloading scheme is proposed. Then, in the cooperative MEC servers scenario, the MEC servers can further offload the collected overloading tasks to the adjacent servers at the next AP on the vehicles' moving direction. A location-based offloading scheme is proposed. In both scenarios, the tradeoffs between the task completed latency and the required communication and computation resources are mainly considered. Numerical results show that our proposed schemes can reduce the system costs efficiently, while the latency constraints are satisfied.

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Section: Related Workmentioning

confidence: 99%

Efficient Mobility-Aware Task Offloading for Vehicular Edge Computing Networks

et al. 2019

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“…When specific APs are involved, we take AP m as an example, the service rate is v m = 12 GHz, and storage space is C m = 240 GB. Also, the CPU clock is G m = 240 GBz, and the unit energy consumption is e m = 1.2 kW H. As for non-task processing energy consumption, it is E t n = [0, 2] kW H. At last, the smoothing factor and the unit task volume transmission delay of tasks offloaded to the remote cloud server are η = 10 −2 and a t = [2,5] sec, respectively.…”

Section: A Parameters Setting In Simulationmentioning

confidence: 99%

“…However, remote cloud computing resources are usually deployed in large data centers far from most users. Therefore, this will cause SMD to have a longer delay and higher energy consumption during the offloading process [5].…”

Section: Introductionmentioning

confidence: 99%

Delay Optimization Strategy for Service Cache and Task Offloading in Three-Tier Architecture Mobile Edge Computing System

Zhang

2020

IEEE Access

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As more and more compute-intensive and delay-sensitive applications are deployed on smart mobile devices, mobile edge computing is considered an effective way to solve the limited computing ability of smart mobile devices (SMDs). At present, latency has become the most critical indicator of the quality of service (QoS), and more and more studies focus on this aspect. Unlike previous work, our work fully takes into account the limited storage and computing ability of edge servers. To effectively reduce the delay of SMDs and improve QoS, we propose a Delay Control Strategy Joint Service Caching and Task Offloading (DCS-OCTO) in a three-tier mobile edge computing (MEC) system consist of multiuser , multiedge server and remote cloud servers. Some of the key challenges include service heterogeneity, unknown system dynamics, spatial demand coupling, and decentralized coordination. In particular, a very compelling but rarely studied issue is the dynamic service caching in the three-tier MEC system. The DCS-OCTO strategy is proposed based on Lyapunov optimization and Gibbs sampling. It works online without requiring prior information and achieves provable near-optimal performance. Finally, simulation results show that the strategy effectively reduces the overall system delay while ensuring low energy consumption.

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“…Finally it designs a low complexity of fairness cooperation algorithm (FCA) to solve the optimization problem. [18] proposes Mobile Edge Computing-Base Station (MEC-BS) cooperation strategy, which offloads queued tasks on MEC to other MEC-BS directly connected to enhance service satisfaction, and finally converts it into the problem of maximizing the total time and energy consumption. [19] proposes a lightweight computing offloading method, which can improve the performance of wearable devices and reduce energy consumption by distributing computing tasks of wearable devices to multiple nearby mobile devices.…”

Section: Related Workmentioning

confidence: 99%

Task Allocation Mechanism of Power Internet of Things Based on Cooperative Edge Computing

et al. 2020

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Edge computing can be widely used in unmanned aerial vehicle (UAV) inspection, field operation control, power consumption information collection and other businesses in the power Internet of Things scene. Edge computing offloads functions such as data processing and applications to network edge nodes near the terminals to provide low-latency services and ensure service quality. However, with the explosive growth of business terminals, the capacity of single edge node is limited and it is difficult to meet all business requirements at the same time. Therefore, this paper proposes a task allocation mechanism based on cooperative edge computing. Firstly, a task allocation model based on cooperation of two edge nodes is established to minimize the average task completion delay while meeting business requirements. Secondly, the Two-edge-node Cooperative-task Allocation based on Improved Particle Swarm Optimization (TCA-IPSO) algorithm is proposed, which applies the crossover and mutation strategy in genetic algorithm to improve the particle swarm optimization algorithm, and solves the problem that the task allocation scheme in cooperation is prone to fall into a local optimum. Finally the simulation results show that the proposed TCA-IPSO algorithm reduces the average task completion delay by 53.8% and 36.0% compared to the benchmark and QoS-based Task Distribution (QBTD) algorithm. INDEX TERMS Cooperative edge computing, Power Internet of Things, Task completion delay, Task allocation.

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Computation Offloading Based on Cooperations of Mobile Edge Computing-Enabled Base Stations

Cited by 68 publications

References 17 publications

Efficient Mobility-Aware Task Offloading for Vehicular Edge Computing Networks

Efficient Mobility-Aware Task Offloading for Vehicular Edge Computing Networks

Delay Optimization Strategy for Service Cache and Task Offloading in Three-Tier Architecture Mobile Edge Computing System

Task Allocation Mechanism of Power Internet of Things Based on Cooperative Edge Computing

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