Open AccessEdge cloud task scheduling model based on layered excellent gene replication strategy
Author(s) -
Lizhong Zhu,
Kangfei Zhao,
Huaze Lin,
Dan Liu,
Li Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2132/1/012002
Subject(s) - cloud computing , computer science , distributed computing , scheduling (production processes) , population , particle swarm optimization , mathematical optimization , algorithm , mathematics , demography , sociology , operating system
With the development of the Internet of Things and 5G. Edge cloud technology has gradually become a research hotspot. When facing the massive and concurrent tasks of terminal users, reasonable resource scheduling strategy is a key technology. Because edge cloud needs to respond quickly to real-time tasks and ensure the stability of nodes at the same time, the optimal task scheduling strategy needs to be selected to meet the low latency requirements of edge users. In view of the above problems in resource allocation of edge cloud, this paper established a layered excellent gene replication strategy (HEGPSO model), in which the optimal replicator is added, and an evolutionary particle swarm optimization algorithm is proposed. In each iteration, the population is divided into three layers based on individual fitness. After that, the optimal replication factor is added to each layer of individuals to enhance the global search ability of the algorithm and ensure the good convergence of the algorithm. Finally, a balanced resource allocation model is established. Experiments show that the HEGPSO model proposed in this paper has high fitness and fast convergence speed, and is suitable for large-scale task access scenarios.