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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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
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Abstract	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.
AbstractList	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.
ArticleNumber	51
Author	Azizi, Sadoon Havas, Shahla Abdollahpouri, Alireza
Author_xml	– sequence: 1 givenname: Shahla surname: Havas fullname: Havas, Shahla – sequence: 2 givenname: Sadoon surname: Azizi fullname: Azizi, Sadoon – sequence: 3 givenname: Alireza surname: Abdollahpouri fullname: Abdollahpouri, Alireza
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SubjectTerms	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
Title	SG-MOACO: a semi-greedy multi-objective ACO method for edge server placement in mobile edge computing
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