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Network Intelligence for Enhanced Multi-Access Edge Computing (MEC) in 5G
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. (Pervasive and Mobile Computing)ORCID iD: 0000-0002-3090-7645
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Luleå University of Technology, Centre for Critical Infrastructure and Societal Security.ORCID iD: 0000-0003-0244-3561
Department of Computer Science and Engineering, University of Chittagong.
2019 (English)Conference paper, Oral presentation only (Other academic)
Abstract [en]

5G networks will enable people and machines to communicate at high speeds and very low latencies, in a reliable way. This opens up opportunities for totally new usage patterns and the fully connected Industry 4.0-enabled enterprise covering the entire value chain from design, production, deployment, to usage of products. 5G will be rolled out across the whole world, including Sweden where the first 5G test network was launched late 2018. One important new feature in 5G is the emerging edge computing capabilities, where users can easily offload computational tasks to the network’s edge very close to the user. At the same time, computational tasks traditionally performed in central nodes can be offloaded from remotely located data centres to the network’s edge. Multi-access Edge Computing (MEC) is a promising network architecture delivering solutions along these lines offering a platform for applications with requirements on low latencies and high reliability. This paper targets this environment with a novel Belief-rule-based (BRB) unsupervised learning algorithm for clustering helping 5G applications to take intelligent decisions on software deployment. The scenarios consist of different combinations of numbers of users and connections and mobility patterns. The target environment is built up using a three-tier structure with a container-based solution where software components can easily be spread around the network.

Place, publisher, year, edition, pages
2019. p. 13-17
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ltu:diva-74031OAI: oai:DiVA.org:ltu-74031DiVA, id: diva2:1318043
Conference
15th Swedish National Computer Networking Workshop (SNCNW 2019), Luleå, June 4-5, 2019
Available from: 2019-05-24 Created: 2019-05-24 Last updated: 2019-08-12

Open Access in DiVA

fulltext(285 kB)34 downloads
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Islam, Raihan UlAndersson, Karl
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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Output format
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