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  1. Proceedings of the Workshop on Mobile Edge Communications (MECOMM '17)
  2. Geo-partitioning of MEC Resources
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Virtualization and Programmability in Mobile Wireless Networks: Architecture and Resource Management
Mobility-based Proactive Multicast for Seamless Mobility Support in Cellular Network Environments
Geo-partitioning of MEC Resources
Information-Centric Mobile Edge Computing for Connected Vehicle Environments: Challenges and Research Directions
Optimization of Webpage Downloading Performance with Content-aware Mobile Edge Computing
RAERA: A Robust Auctioning Approach for Edge Resource Allocation
Wi-Stitch: Content Delivery in Converged Edge Networks
Realization of CDMA-based IoT Services with Shared Band Operation of LTE in 5G
SDLB: A Scalable and Dynamic Software Load Balancer for Fog and Mobile Edge Computing
Managing Data in Computational Edge Clouds

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Geo-partitioning of MEC Resources

Content Provider ACM Digital Library
Author Bouet, Mathieu Conan, Vania
Abstract Mobile Edge Computing (MEC) is an emerging technology that aims at pushing applications and content close to the users (e.g. at base stations, access points, aggregation networks) to reduce latency, improve quality of experience, and ensure highly efficient network operation and service delivery It principally relies on virtualization-enabled MEC servers with limited capacity at the edge of the network. One key issue is to dimension such systems in terms of server size, server number and server operation area to meet MEC goals. In this paper, we propose a graph-based algorithm that, taking into account a maximum MEC server capacity, provides a partition of MEC clusters, which consolidates as many communications as possible at the edge. We use a dataset of mobile communications to evaluate it with real world spatio-temporal human dynamics. In addition to quantifying macroscopic MEC benefits, the evaluation shows that our algorithm provides MEC area partitions that largely offload the core, thus pushing the load at the edge (e.g., with 10 small MEC servers around 55% of the traffic stay at the edge), and that are well balanced through time.
Starting Page 43
Ending Page 48
Page Count 6
File Format PDF
ISBN 9781450350525
DOI 10.1145/3098208.3098216
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2017-08-09
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Server clustering Mec Load-balancing
Content Type Text
Resource Type Article
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