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Cost-Effective Edge Server Placement in Wireless Metropolitan Area Networks.
Feng Zeng1, Yongzheng Ren2, Xiaoheng Deng3
1School of Software, Central South University, Changsha 410083, China. fengzeng@csu.edu.cn.
Sensors (Basel, Switzerland)
|December 23, 2018
Summary
Mobile edge computing offers low latency solutions for mobile users. This study proposes effective and economical edge server deployment strategies, minimizing servers while meeting quality of service requirements.
Area of Science:
- Computer Science
- Network Engineering
Background:
- Remote cloud computing faces latency and congestion issues for mobile users.
- Mobile edge computing (MEC) is a proposed solution to address these limitations.
- Existing MEC research often overlooks practical edge server deployment challenges.
Purpose of the Study:
- To investigate effective and economical edge server deployment in wireless metropolitan area networks.
- To minimize the number of edge servers while ensuring Quality of Service (QoS) requirements.
- To address the practical challenges of edge server placement in real-world scenarios.
Main Methods:
- The problem is modeled as a minimum dominating set problem in graph theory by extending the definition of a dominating set.
- A greedy algorithm is proposed for on-demand edge server capacities, prioritizing nodes that connect the most others under constraints.
- A simulated annealing approach is used for global optimization.
- A separate greedy algorithm addresses scenarios with uniform edge server capacities, minimizing server count while meeting capacity constraints.
Main Results:
- The proposed algorithms demonstrate feasibility for effective and economical edge server deployment.
- The methods successfully minimize the number of deployed edge servers.
- QoS requirements are met through the strategic placement of edge servers.
Conclusions:
- The study provides practical solutions for edge server deployment in wireless metropolitan networks.
- The graph theory-based approach offers a robust framework for optimizing edge server placement.
- The developed algorithms are efficient and effective in minimizing resources while ensuring network performance.
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