Article Dans Une Revue IEEE Transactions on Network and Service Management Année : 2018

Mobile Edge Computing Resources Optimization: a Geo-clustering Approach

Résumé

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 formulate this problem as a mixed integer linear program. We then 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 extensively evaluate them 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 between 55% and 64% of the traffic stay at the edge), and that are well balanced through time.

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hal-02065474 , version 1 (12-03-2019)

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Mathieu Bouet, Vania Conan. Mobile Edge Computing Resources Optimization: a Geo-clustering Approach. IEEE Transactions on Network and Service Management, 2018, 15 (2), pp.787-796. ⟨10.1109/TNSM.2018.2816263⟩. ⟨hal-02065474⟩
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