Mobility-Aware and Migration-Enabled Online Edge User Allocation in Mobile Edge Computing

Peng, Qinglan; Xia, Yunni; Zeng, Feng; Lee, Jia; Wu, Chunrong; Luo, Xin; Zheng, Wanbo; Liu, Hui; Qin, Yidan; Chen, Peng

doi:10.1109/icws.2019.00026

Cited by 102 publications

(39 citation statements)

References 21 publications

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“…One can find papers in which one of these two goals are set out, e.g., energy in [84], [85], [102], processing time in [94], [104], [115], [148], [152], [157], [173], and there are also related works in which the goals are targeted jointly, e.g., in [81], [103], [112], [113], [117], [183]. While the former goal aims at preserving the limited battery capacity of terminals, e.g., IoT sensors, mobile phones, [57], [60], [63], [69], [74], [78], [79], [81], [82], [84]- [87], [90], [93], [94], [98], [101]- [104], [112], [113], [115], [117], [130], [143], [144], [148], [151], [152], [154], [157]- [160], [1...…”

Section: A Single Componentmentioning

confidence: 99%

Survey on Placement Methods in the Edge and Beyond

Sonkoly

Czentye

Szalay

et al. 2021

IEEE Commun. Surv. Tutorials

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Edge computing is a (r)evolutionary extension of traditional cloud computing. It expands central cloud infrastructure with execution environments close to the users in terms of latency in order to enable a new generation of cloud applications. This paradigm shift has opened the door for telecommunications operators, mobile and fixed network vendors: they have joined the cloud ecosystem as essential stakeholders considerably influencing the future success of the technology. A key problem in edge computing is the optimal placement of computational units (virtual machines, containers, tasks or functions) of novel distributed applications. These components are deployed to a geographically distributed virtualized infrastructure and heterogeneous networking technologies are invoked to connect them while respecting quality requirements. The optimal hosting environment should be selected based on multiple criteria by novel scheduler algorithms which can cope with the new challenges of distributed cloud architecture where networking aspects cannot be ignored. The research community has dedicated significant efforts to this topic during recent years and a vast number of theoretical results have been published addressing different variants of the related mathematical problems. However, a comprehensive survey focusing on the technical and analytical aspects of the placement problem in various edge architectures is still missing. This survey provides a comprehensive summary and a structured taxonomy of the vast research on placement of computational entities in emerging edge infrastructures. Following the given taxonomy, the research papers are analyzed and categorized according to several dimensions, such as the capabilities of the underlying platforms, the structure of the supported services, the problem formulation, the applied mathematical methods, the objectives and constraints incorporated in the optimization problems, and the complexity of the proposed methods. We summarize the gained insights and important lessons learned, and finally, we reveal some important research gaps in the current literature.

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Section: A Single Componentmentioning

confidence: 99%

Survey on Placement Methods in the Edge and Beyond

Sonkoly

Czentye

Szalay

et al. 2021

IEEE Commun. Surv. Tutorials

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“…The initial location information of the edge server and mobile device is simulated based on the Melbourne CBD area in the EUA data set, and the location of mobile devices changes with time following the Truncated Levy Walk mobility model to ensure that it moves in the area covered by the signal [22]. The signal coverage radius of the HAP is randomly distributed between [100,400], the uploading bandwidth of the mobile device w = 10MHz, the fixed communication power p 0 = 0.4W, the data transmission power p tran = 0.1W, the signal power amplifier coefficient α = 40, the energy conversion efficiency υ = 0.8 , the integrated channel gain G = 20, the initial power is 4000mah, and it is assumed that each mobile device can only send one application request at the same time [23][24][25][26]. In order to consider the computing performance and power consumption of different edge servers and mobile devices, this paper refers to Standard Performance Evaluation Corporation (SPEC) to set the device configuration and average performance power consumption ratio.…”

Section: A Simulation Environmentmentioning

confidence: 99%

Optimization of Task Offloading Strategy for Mobile Edge Computing Based on Multi-Agent Deep Reinforcement Learning

Luo

et al. 2020

IEEE Access

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Combined with wireless power transfer (WPT) technology, mobile edge computing can provide continuous energy supply and computing resources for mobile devices, and improve their battery life and business application scenarios. This paper first designs the mobile edge computing (MEC) model of mobile devices with random mobility and hybrid access point (HAP) with data transmission and energy transmission. On this basis, the selection of target server and the amount of data offloading are taken as the learning objectives, and the task offloading strategy based on multi-agent deep reinforcement learning is constructed. Then combined with MADDPG algorithm and SAC algorithm, the problems of multi-agent environment instability and the difficulty of convergence are solved. The final experimental results show that the improved algorithm based on MADDPG and SAC has good stability and convergence. Compared with other algorithms, it has achieved good results in energy consumption, delay and task failure rate. INDEX TERMS Mobile edge computing, task offloading, wireless power transfer, multi-agent, deep reinforcement learning.

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“…Each user observed the task delay as an experience and automatically learnt the optimal mobility management strategy through trial and error. Peng et al [36] considered the edge user allocation problem as an on-line decision-making and developed a mobility-aware and migration-enabled approach, called MobMig, for allocating users at real-time. Both schemes established the learning-based algorithms to choose the suitable MEC servers for the service to be migrated, but lacked further study on QoS of migrated services, leading to fail to choose the most suitable MEC servers.…”

Section: Related Workmentioning

confidence: 99%

Dynamic service migration in ultra-dense multi-access edge computing network for high-mobility scenarios

Lin

Zhao

et al. 2020

J Wireless Com Network

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The multi-access edge computing (MEC) has higher computing power and lower latency than user equipment and remote cloud computing, enabling the continuing emergence of new types of services and mobile application. However, the movement of users could induce service migration or interruption in the MEC network. Especially for highly mobile users, they accelerate the frequency of services’ migration and handover, impacting on the stability of the total MEC network. In this paper, we propose a hierarchical multi-access edge computing architecture, setting up the infrastructure for dynamic service migration in the ultra-dense MEC networks. Moreover, we propose a new mechanism for users with high mobility in the ultra-dense MEC network, efficiently arranging service migrations for users with high-mobility and ordinary users together. Then, we propose an algorithm for evaluating migrated services to contribute to choose the suitable MEC servers for migrated services. The results show that the proposed mechanism can efficiently arrange service migrations and more quickly restore the services even in the blockage. On the other hand, the proposed algorithm is able to make a supplement to the existing algorithms for selecting MEC servers because it can better reflect the capability of migrated services.

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Mobility-Aware and Migration-Enabled Online Edge User Allocation in Mobile Edge Computing

Cited by 102 publications

References 21 publications

Survey on Placement Methods in the Edge and Beyond

Survey on Placement Methods in the Edge and Beyond

Optimization of Task Offloading Strategy for Mobile Edge Computing Based on Multi-Agent Deep Reinforcement Learning

Dynamic service migration in ultra-dense multi-access edge computing network for high-mobility scenarios

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