Open AccessRESEARCH ON FUZZY SCHEDULING OF CLOUD COMPUTING TASKS BASED ON HYBRID SEARCH ALGORITHMS AND DIFFERENTIAL EVOLUTION
Author(s) -
Maozhu Jin,
Yifeng Liu,
Hunida Malaikah,
Chao Chen,
Peng Chen
Publication year - 2022
Publication title -
fractals
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.654
H-Index - 44
eISSN - 1793-6543
pISSN - 0218-348X
DOI - 10.1142/s0218348x22400837
Subject(s) - computer science , differential evolution , cloud computing , scheduling (production processes) , fuzzy logic , algorithm , dynamic priority scheduling , distributed computing , quality of service , mathematical optimization , artificial intelligence , mathematics , computer network , operating system
In the process of task scheduling, due to the large search space of resources, it takes a long time to allocate appropriate resources for tasks, which results in the increase of execution time and execution cost of algorithms. For this reason, this paper proposes a cloud computing task fuzzy scheduling strategy based on hybrid search algorithm and differential evolution, which incorporates the classification based on normal distribution and a variety of mutation strategies on the basis of standard differential evolution algorithm. In mutation strategy, the individual difference vector assigns priority to each task and arranges tasks according to resource allocation rules. It improves the slow convergence speed and easy to fall into local optimum of standard difference algorithm, and can effectively solve the task scheduling problem of cloud computing. A cloud computing task scheduling algorithm with time and cost constraints is designed and tested in a simulation environment. The experimental results show that the algorithm can not only shorten the task processing time and reduce the execution cost, but also fully satisfy the users’ actual quality of service requirements by adjusting the weights of time and cost factors.