Minimizing Task Completion Time in the Cloud based on Random Neural Network

With the development of IoT and 5G, the number of devices accessing the Internet is increasing every day. While mobile edge computing effectively reduces the pressure on cloud centers, cloud centers still face the challenge of task scheduling and resource allocation for a large amount of SaaS applic...

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Bibliographic Details
Published in:	2021 International Conference on Computer, Blockchain and Financial Development (CBFD) pp. 60 - 65
Main Authors:	Yu, Wang, Tongtong, Wu
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.04.2021
Subjects:	Cloud computing Heuristic algorithms Multi-access edge computing Neural networks Processor scheduling queue theory random neural network Simulation Stochastic processes task completion time task dispatching
Online Access:	Get full text
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Summary:	With the development of IoT and 5G, the number of devices accessing the Internet is increasing every day. While mobile edge computing effectively reduces the pressure on cloud centers, cloud centers still face the challenge of task scheduling and resource allocation for a large amount of SaaS applications. In this paper, the conditions for minimizing the average task completion time are derived by a simplified queuing model and an adaptive dynamic scheduling algorithm for minimizing the average task completion time is proposed in combination with stochastic neural networks, which is based on online measurements and takes up very little resources and computation. A diverse range of algorithms are tested in many different environments as a way to analyze algorithm performance. The simulation results show that our proposed algorithm is effective in reducing the average task completion time in a variety of environments.
DOI:	10.1109/CBFD52659.2021.00019