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Dynamic network slicing in fog computing for mobile users in MobFogSim

Goncalves, Diogo, Puliafito, Carlo, Mingozzi, Enzo, Rana, Omer, Bittencourt, Luiz and Madeira, Edmundo 2020. Dynamic network slicing in fog computing for mobile users in MobFogSim. Presented at: IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC 2020), Leicester, England, 7-10 December 2020. 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC). IEEE, pp. 237-246. 10.1109/UCC48980.2020.00042

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Abstract

Fog computing provides resources and services in proximity to users. To achieve latency and throughput requirements of mobile users, it may be useful to migrate fog services in accordance with user movement – a scenario referred to as follow me cloud. The frequency of migration can be adapted based on the mobility pattern of a user. In such a scenario, the fog computing infrastructure should simultaneously accommodate users with different characteristics, both in terms of mobility (e.g., route and speed) and Quality of Service requirements (e.g., latency, throughput, and reliability). Migration performance may be improved by leveraging "network slicing", a capability available in Software Defined Networks with Network Function Virtualisation. In this work, we describe how we extended our simulator, called MobFogSim, to support dynamic network slicing and describe how MobFogSim can be used for capacity planning and service management for such mobile fog services. Moreover, we report an experimental evaluation of how dynamic network slicing impacts on container migration to support mobile users in a fog environment. Results show that dynamic network slicing can improve resource utilisation and migration performance in the fog.

Item Type: Conference or Workshop Item (Paper)
Date Type: Published Online
Status: Published
Schools: Computer Science & Informatics
Additional Information: "© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works."
Publisher: IEEE
ISBN: 9780738123943
Date of First Compliant Deposit: 20 January 2021
Date of Acceptance: 12 October 2020
Last Modified: 20 Jan 2021 09:26
URI: http://orca.cf.ac.uk/id/eprint/137737

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