The Meta Distribution of Task Offloading in Stochastic Mobile Edge Computing Networks

Gu, Yixiao; Xia, Bin; Yang, Chenchen; Chen, Zhiyong

doi:10.1109/tvt.2022.3190712

Cited by 4 publications

(2 citation statements)

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“…Following [5], the authors in [6] employed the technology of stochastic geometry to examine the probability of service interruptions in mobile cloud systems, where tasks are offloaded to the radio access network for execution. Similar to [6], the authors in [7] also exploited the stochastic geometry modelling approach to analyse the success task offloading probabilities and approximated MEC computing load. The authors in [8] looked at another aspect of VEC performance modelling related to cooperation strategies among edge servers.…”

Section: Related Workmentioning

confidence: 99%

“…To capture the limited-service capability of practical VEC systems, the buffer sizes of the ODM, VP, and ES modules are set to be limited, denoted as 𝐾 𝑑,𝑛 , 𝐾 𝑣 𝑝,𝑛 , and 𝐾 𝑒𝑠 , respectively. To facilitate the designed analytical model capable of investigating VEC performance with different offloading algorithms, we harness a parameterbased approach [23] [24] to employ the variables 𝜂, 𝜗 𝑖 , and 𝜉 to represent the probabilities of tasks being executed at the local server, the 𝑖th neighbour vehicle and the edge server. Herein, if the 𝑖th neighbour vehicle is not selected by the offloading algorithm, potentially because of hardware issues or unstable V2V link, the value of 𝜗 𝑖 would be set to be zero in the analytical model and no tasks will be scheduled to the 𝑖th vehicle for processing.…”

Section: Mec In Base Stationmentioning

confidence: 99%

See 1 more Smart Citation

Performance Analytical Modeling of Mobile Edge Computing for Mobile Vehicular Applications: A Worst-Case Perspective

Miao,

Min,

et al. 2024

IEEE Trans. on Mobile Comput.

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Quantitative performance analysis plays a pivotal role in theoretically investigating the performance of Vehicular Edge Computing (VEC) systems. Although considerable research efforts have been devoted to VEC performance analysis, all of the existing analytical models were designed to derive the average system performance, paying insufficient attention to the worst-case performance analysis, which hinders the practical deployment of VEC systems to support mission-critical vehicular applications, such as collision avoidance. To bridge this gap, we develop an original performance analytical model by virtue of Stochastic Network Calculus (SNC) to investigate the worst-case end-to-end performance of VEC systems. Specifically, to capture the bursty feature of task generation, an innovative bivariate Markov Chain is firstly established and rigorously analysed to derive the stochastic task envelope. Then, an effective service curve is created to investigate the severe resource competition among vehicular applications. Driven by the stochastic task envelope and effective service curve, a closed-form end-to-end analytical model is derived to obtain the latency bound for VEC systems. Extensive simulation experiments are conducted to validate the accuracy of the proposed analytical model under different system configurations. Furthermore, we exploit the proposed analytical model as a cost-effective tool to investigate the resource allocation strategies in VEC systems.

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