Open AccessDelay and Energy Consumption Optimization Oriented Multi-service Cloud Edge Collaborative Computing Mechanism in IoT
Author(s) -
Sujie Shao,
Jiajia Tang,
Shuang Wu,
Jiag Li,
Shaoyong Guo,
Feng Qi
Publication year - 2021
Publication title -
journal of web engineering/journal of web engineering on line
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.151
H-Index - 13
eISSN - 1544-5976
pISSN - 1540-9589
DOI - 10.13052/jwe1540-9589.20810
Subject(s) - cloud computing , computer science , simulated annealing , distributed computing , energy consumption , enhanced data rates for gsm evolution , genetic algorithm , task (project management) , edge computing , generalized assignment problem , optimization problem , mathematical optimization , algorithm , artificial intelligence , engineering , machine learning , mathematics , systems engineering , electrical engineering , operating system
The rapid development of the Internet of Things has put forward higher requirements for the processing capacity of the network. The adoption of cloud edge collaboration technology can make full use of computing resources and improve the processing capacity of the network. However, in the cloud edge collaboration technology, how to design a collaborative assignment strategy among different devices to minimize the system cost is still a challenging work. In this paper, a task collaborative assignment algorithm based on genetic algorithm and simulated annealing algorithm is proposed. Firstly, the task collaborative assignment framework of cloud edge collaboration is constructed. Secondly, the problem of task assignment strategy was transformed into a function optimization problem with the objective of minimizing the time delay and energy consumption cost. To solve this problem, a task assignment algorithm combining the improved genetic algorithm and simulated annealing algorithm was proposed, and the optimal task assignment strategy was obtained. Finally, the simulation results show that compared with the traditional cloud computing, the proposed method can improve the system efficiency by more than 25%.