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<p>Mobile Edge Computing (MEC) facilitates rapid data handling by processing real-time data in proximity to users. However, due to constrained resources of MEC servers and strict requirements of end users, the need for optimal server placement, and associated provisioning of composite services are required to enable better resource management of the MEC servers while enabling higher Quality of Services (QoS) to end users. In this paper, we propose a framework to enable proper provisioning of the end users’ composite services considering their mobility, while facilitating placement of MEC servers and enabling load balancing to enhance QoS. We formulated the composite service placement in MEC as a Service Coverage Problem (SCP) and solved it using our Adaptive Composite Service Deployment (ASDMD) using Multilayer Perceptron (MLP)-Based Deep Q-Network (DQN). Following that, we formulated the MEC server placement as a constraint-based P-Median problem and solved it using the Constraint-Based Knapsack Heuristic (CB-KH) algorithm. To ensure higher QoS and reduction of congested servers, we used a load balancing technique to iteratively monitor the loads of the servers and transfer the congested servers’ users to the most appropriate alternatives. Experiments and simulations based on real-world datasets showcase the significance of our model in better management of resources of MEC servers while ensuring proper processing of data and enhanced QoS for end users. The results indicate a minimum of 4.8% improvement in the reduction of composite services deployed, which improves resource usage, enhancing the user coverage by a minimum of 19.8%, to serve a greater number of users,&#xa0;and enabling a minimum of 12.7% reduction in the average latency, enhancing the user satisfaction, compared to the state-of-the-art methods.</p>

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An adaptive composite service deployment in mobile edge computing using MLP-based DQN

  • 期刊论文

  • 发表日期：

    2025年9月15日

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  • Majid Ayoubi,
  • Mingchu Li,
  • Javid Enayat,
  • Abdulgbar A. R. Farea,
  • Lutf Ul Rahman Haqmal

摘要

Mobile Edge Computing (MEC) facilitates rapid data handling by processing real-time data in proximity to users. However, due to constrained resources of MEC servers and strict requirements of end users, the need for optimal server placement, and associated provisioning of composite services are required to enable better resource management of the MEC servers while enabling higher Quality of Services (QoS) to end users. In this paper, we propose a framework to enable proper provisioning of the end users’ composite services considering their mobility, while facilitating placement of MEC servers and enabling load balancing to enhance QoS. We formulated the composite service placement in MEC as a Service Coverage Problem (SCP) and solved it using our Adaptive Composite Service Deployment (ASDMD) using Multilayer Perceptron (MLP)-Based Deep Q-Network (DQN). Following that, we formulated the MEC server placement as a constraint-based P-Median problem and solved it using the Constraint-Based Knapsack Heuristic (CB-KH) algorithm. To ensure higher QoS and reduction of congested servers, we used a load balancing technique to iteratively monitor the loads of the servers and transfer the congested servers’ users to the most appropriate alternatives. Experiments and simulations based on real-world datasets showcase the significance of our model in better management of resources of MEC servers while ensuring proper processing of data and enhanced QoS for end users. The results indicate a minimum of 4.8% improvement in the reduction of composite services deployed, which improves resource usage, enhancing the user coverage by a minimum of 19.8%, to serve a greater number of users, and enabling a minimum of 12.7% reduction in the average latency, enhancing the user satisfaction, compared to the state-of-the-art methods.