A Modified Manta Ray Foraging Algorithm for Edge Server Placement in Mobile Edge Computing

Walaa H. El-Ashmawi, Ahmed F. Ali and Ahmed Ali
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Walaa H. El-Ashmawi: Department of Computer Science, Faculty of Computers and Informatics, Suez Canal University, 4.5 Km the Ring Road, Ismailia 41522, Ismailia, Egypt†Department of Computer Science, Faculty of Computer Science, Misr International University, 28 KM Cairo - Ismailia Road, Cairo 44971, Egypt
Ahmed F. Ali: Department of Computer Science, Faculty of Computers and Informatics, Suez Canal University, 4.5 Km the Ring Road, Ismailia 41522, Ismailia, Egypt‡Faculty of Information Technology, Misr University for Science and Technology, 6th of October, Egypt
Ahmed Ali: �Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam B in Abdulaziz University, Al-Kharj 11942, Saudi Arabia¶Higher Future Institute for Specialized Technological Studies, Cairo 3044, Egypt

International Journal of Information Technology & Decision Making (IJITDM), 2024, vol. 23, issue 04, 1703-1739

Abstract: Owing to the rapid growth of the Internet of Things (IoT) and mobile devices, big data has been generated, and a cloud computing system is required to manipulate it. The main issue in this system is the access delay (AD) when the IoT and mobile devices offload their workload on the servers and the workload balance among them. Mobile edge computing (MEC), which has a sufficient distribution of edge servers (ESs) and resources, has been developed to overcome this problem. The placement of ESs in MEC is an NP-hard problem and a multi-objective optimization (MOO) problem that aims to reduce the AD between mobile users and ES and balance the workload among these servers. In this paper, we propose a new hybrid natural-inspired algorithm by combining the manta ray foraging algorithm and the uniform crossover operator to find the optimal edge server placement (ESP) in MEC and minimize the AD between mobile users and the ES. Although the standard manta ray foraging optimization (MRFO) algorithm can balance diversification and intensification, it suffers from premature convergence. We invoke a uniform crossover operator in the standard MRFO algorithm to overcome this problem. In addition, the standard MRFO algorithm is designed to solve continuous optimization problems and is unsuitable for solving discrete problems. However, ESP is a discrete optimization problem. We modified the operators in the standard MRFO algorithm to solve the ESP problem, achieving an optimal solution in terms of minimizing the AD between BSs and ESs and balancing the workload among ESs. The proposed algorithm is then tested on the Shanghai Telecom dataset and compared with five other algorithms. The results show that the proposed algorithm achieves the least AD, and the workload balance reaches about 52% compared with the other algorithms.

Keywords: Edge server placement (ESP); manta ray foraging algorithm; access delay (AD); workload balancing; uniform crossover (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219622023500542

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