SG-MOACO: a semi-greedy multi-objective ACO method for edge server placement in mobile edge computing

The importance of Mobile Edge Computing (MEC) systems has risen due to increasing data volumes from Internet of Things (IoT) devices and connected technologies. These systems place computation power and data storage near the network edge to improve response times and minimize bandwidth usage. One im...

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Bibliographic Details
Published in:Computing Vol. 107; no. 1; p. 51
Main Authors: Havas, Shahla, Azizi, Sadoon, Abdollahpouri, Alireza
Format: Journal Article
Language:English
Published: Wien Springer Nature B.V 01.01.2025
Subjects:
Ant colony optimization
Bandwidths
Communications traffic
Data storage
Edge computing
Greedy algorithms
Integer programming
Internet of Things
Linear programming
Load balancing
Mixed integer
Mobile computing
Multiple objective analysis
Performance evaluation
Placement
Response time (computers)
Servers
User experience
ISSN:0010-485X, 1436-5057
Online Access:Get full text
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Summary:The importance of Mobile Edge Computing (MEC) systems has risen due to increasing data volumes from Internet of Things (IoT) devices and connected technologies. These systems place computation power and data storage near the network edge to improve response times and minimize bandwidth usage. One important thing in MEC is the edge server placement (ESP) problem for which placing servers strategically enhances system performance and user experience. Given the complexity of ESP, finding the optimal solution is challenging as it is an NP-hard problem. Reducing network traffic and achieving load balancing among edge servers are crucial objectives in ESP to alleviate bandwidth consumption and enhance the user experience. This paper introduces a mixed integer programming (MIP) model that addresses these issues and proposes a multi-objective ant colony optimization algorithm for solving this model. A semi-greedy approach is utilized to enhance the proposed algorithm's efficiency, accelerating convergence and reducing the search space when generating the initial population. To evaluate the performance of the proposed method, extensive simulation experiments were conducted using a real-world dataset from Shanghai Telecom's base stations. The results demonstrate the effectiveness of our method, showing a load balancing improvement of over 24% and a network traffic reduction exceeding 19% compared to other algorithms.
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ISSN:0010-485X
1436-5057
DOI:10.1007/s00607-024-01400-z