EdgeCloudSim-based Modelling on Power Distribution Internet of Things and Task Offloading Strategy

Zhao, Junwei; Zhou, Lide; Zhang, Rui; He, Yipeng; Deng, Jingzhu

doi:10.1109/powercon53785.2021.9697841

Cited by 3 publications

(3 citation statements)

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“…In [3], a kind of MEO based on fuzzy logic is proposed, which comprehensively considers parameters such as WAN delay, MAN delay, delay sensitivity, and length of task, and designs a two-level fuzzy logic system to determine offloading target. In [18], EdgeCloudSim is used to study the task offloading effect of single-layer strategy, two-layer strategy, and balanced two-layer strategy in power distribution Internet of Things. However, the above methods do not take into account the computing function of mobile devices themselves, nor do they study the influence of WLAN delay on offloading decisions.…”

Section: Related Workmentioning

confidence: 99%

“…C j,i is the number of CPU cycles required by the best MEC server to calculate one module of tasks [18]. • m j,i MEC is the number of task modules offloaded from MD i to MEC j .…”

mentioning

confidence: 99%

“…𝑀𝐸𝐶 is the computing resource assigned by the best 𝑀𝐸𝐶 𝑗 to task modul from 𝑀𝐷 𝑖 (modules/s).•𝐶 𝑗,𝑖 is the number of CPU cycles required by the best MEC server to calcula one module of tasks[18].• 𝑚 𝑗,𝑖𝑀𝐸𝐶 is the number of task modules offloaded from 𝑀𝐷 𝑖 to 𝑀𝐸𝐶 𝑗 .…”

mentioning

confidence: 99%

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A Fuzzy-Based Mobile Edge Architecture for Latency-Sensitive and Heavy-Task Applications

Shi

Chu

Ji³

et al. 2022

Symmetry

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Appropriate task offloading management strategy is a challenging problem for high delay-sensitive and heavy-task applications. This paper proposes a fuzzy-based mobile edge manager with task partitioning, which can handle the multi-criteria decision-making process by considering multiple parameters in the MEC network framework and make appropriate offloading decisions for incoming tasks of IoT applications. Considering that the mobile devices are becoming more and more powerful, this paper also takes WLAN delay and the computing power of mobile devices into account, forming a three-level fuzzy logic system. In addition, since many tasks of Internet of Things applications are composed of several independent modules, this paper also sets two optimal task partitioning ratios, which have symmetry, so that each module can be independently executed in each layer of the MEC network. In addition, results will return to the mobile devices after execution, so as to minimize the service time and improve QoS. Finally, several indexes such as task failure rate and service time are simulated, and the results show that the proposed scheme has better performance compared with the other four comparison schemes, especially for high-latency sensitivity and heavy-task applications.

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Section: Related Workmentioning

confidence: 99%

“…C j,i is the number of CPU cycles required by the best MEC server to calculate one module of tasks [18]. • m j,i MEC is the number of task modules offloaded from MD i to MEC j .…”

mentioning

confidence: 99%