Big data-driven scheduling optimization algorithm for Cyber–Physical Systems based on a cloud platform

In this paper, we study big data-driven Cyber–Physical Systems (CPS) through cloud platforms and design scheduling optimization algorithms to improve the efficiency of the system. A task scheduling scheme for large-scale factory access under cloud–edge collaborative computing architecture is propose...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Computer communications Ročník 181; s. 173 - 181
Hlavní autoři: Niu, Chao, Wang, Lizhou
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.01.2022
Témata:
Big data-driven
Cloud platform
Cyber–Physical Systems
Scheduling optimization algorithm
Cyber–Physical Systems
Scheduling optimization algorithm
Cloud platform
Big data-driven
ISSN:0140-3664, 1873-703X
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:In this paper, we study big data-driven Cyber–Physical Systems (CPS) through cloud platforms and design scheduling optimization algorithms to improve the efficiency of the system. A task scheduling scheme for large-scale factory access under cloud–edge collaborative computing architecture is proposed. The method firstly merges the directed acyclic graphs on cloud-side servers and edge-side servers; secondly, divide the tasks using a critical path-based partitioning strategy to effectively improve the allocation accuracy; then achieves load balancing through reasonable processor allocation, and finally compares and analyses the proposed task scheduling algorithm through simulation experiments. The experimental system is thoroughly analysed, hierarchically designed, and modelled, simulated, and the experimental data analysed and compared with related methods. The experimental results prove the effectiveness and correctness of the worst-case execution time analysis method and the idea of big data-driven CPS proposed in this paper and show that big data knowledge can help improve the accuracy of worst-case execution time analysis. This paper implements a big data-driven scheduling optimization algorithm for Cyber–Physical Systems based on a cloud platform, which improves the accuracy and efficiency of the algorithm by about 15% compared to other related studies.
ISSN:0140-3664
1873-703X
DOI:10.1016/j.comcom.2021.10.020