research-article

Service Placement and Request Routing in MEC Networks With Storage, Computation, and Communication Constraints

Authors:

Konstantinos Poularakis,

Antonia M. Tulino,

Leandros TassiulasAuthors Info & Claims

IEEE/ACM Transactions on Networking, Volume 28, Issue 3

Pages 1047 - 1060

https://doi.org/10.1109/TNET.2020.2980175

Published: 16 June 2020 Publication History

Abstract

The proliferation of innovative mobile services such as augmented reality, networked gaming, and autonomous driving has spurred a growing need for low-latency access to computing resources that cannot be met solely by existing centralized cloud systems. Mobile Edge Computing (MEC) is expected to be an effective solution to meet the demand for low-latency services by enabling the execution of computing tasks at the network edge, in proximity to the end-users. While a number of recent studies have addressed the problem of determining the execution of service tasks and the routing of user requests to corresponding edge servers, the focus has primarily been on the efficient utilization of computing resources, neglecting the fact that non-trivial amounts of data need to be pre-stored to enable service execution, and that many emerging services exhibit asymmetric bandwidth requirements. To fill this gap, we study the joint optimization of service placement and request routing in dense MEC networks with multidimensional constraints. We show that this problem generalizes several well-known placement and routing problems and propose an algorithm that achieves close-to-optimal performance using a randomized rounding technique. Evaluation results demonstrate that our approach can effectively utilize available storage, computation, and communication resources to maximize the number of requests served by low-latency edge cloud servers.

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Service Placement and Request Routing in MEC Networks With Storage, Computation, and Communication Constraints

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cover image IEEE/ACM Transactions on Networking

IEEE/ACM Transactions on Networking Volume 28, Issue 3

June 2020

476 pages

ISSN:1063-6692

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1063-6692 © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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IEEE Press

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Published: 16 June 2020

Published in TON Volume 28, Issue 3

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https://dl.acm.org/doi/10.1016/j.engappai.2024.109662
Mudvari AVainio AOfeidis ITarkoma STassiulas L(2024)Adaptive Compression-Aware Split Learning and Inference for Enhanced Network EfficiencyACM Transactions on Internet Technology10.1145/368747124:4(1-26)Online publication date: 15-Nov-2024
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