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Effective scheduling algorithm for load balancing in fog environment using CNN and MPSO

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Abstract

Fog computing (FC) designates a decentralized computing structure placed among the devices that produce data and cloud. Such flexible structure empowers users to place resources to increase performance. However, limited resources and low delay services obstruct the application of new virtualization technologies in the task scheduling and resource management of fog computing. Scheduling and load balancing (LB) in the cloud computing have been widely studied. However, countless efforts in LB have been proposed in the fog architectures. This presents some enticing challenges to solve the problem of how tasks are routed between different physical devices between fog nodes and cloud. Within fog, due to its mass and heterogeneity of devices, the scheduling is very difficult. There are still few studies that have been conducted. LB is a very interesting and important study area in FC as it aims to achieve high resource utilization. There are various challenges in LB such as security and fault tolerance. The main objective of this paper is to introduce an effective dynamic load balancing technique (EDLB) using convolutional neural network and modified particle swarm optimization, which is composed of three main modules, namely: (i) fog resource monitor (FRM), (ii) CNN-based classifier (CBC), and (iii) optimized dynamic scheduler (ODS). The main purpose of EDLB is to achieve LB in FC environment via dynamic real-time scheduling algorithm. This paper studies the FC architecture for Healthcare system applications. The FRM is responsible for monitoring each server resource and save the server's data into table called fog resources table. The CNN-based classifier (CBC) is responsible for classifying each fog server to suitable or not suitable. The optimized dynamic scheduler (ODS) is responsible for assigning the incoming process to the most appropriate server. Comparing EDLB with other previous LB algorithms, it reduces the response time and achieves high resource utilization. Hence, it is an efficient way to ensure the continuous service. Accordingly, EDLB is simple and efficient in real-time systems in fog computing such as in the case of healthcare system. Although several methods in LB for FC have been introduced, they have many limitations. EDLB overcomes these limitations and achieves high performance in various scenarios. It achieved better makespan, average resource utilization and load balancing level as compared to previously mentioned LB algorithms.

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Authors and Affiliations

  1. Machine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, Egypt

    Fatma M. Talaat

  2. Department of Computer Engineering and Systems, Faculty of Engineering, Mansoura University, Mansoura, Egypt

    Hesham A. Ali, Mohamed S. Saraya & Ahmed I. Saleh

  3. Faculty of Artificial Intelligence, Delta University, Belqas, Egypt

    Hesham A. Ali

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  1. Fatma M. Talaat
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  2. Hesham A. Ali
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Correspondence to Fatma M. Talaat.

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Talaat, F.M., Ali, H.A., Saraya, M.S. et al. Effective scheduling algorithm for load balancing in fog environment using CNN and MPSO. Knowl Inf Syst 64, 773–797 (2022). https://doi.org/10.1007/s10115-021-01649-2

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