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Priority-Aware Service Orchestration Using Big Data Analytics for Dynamic Slicing in 5G Transport Networks
KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS, Optical Network Laboratory (ON Lab).
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2017 (English)In: 2017 European Conference on Optical Communication (ECOC), Institute of Electrical and Electronics Engineers (IEEE), 2017Conference paper, Published paper (Refereed)
Abstract [en]

We demonstrate how to efficiently scale up/down resource slices allocated to tenants with different service priorities. Experimental results show that our proposed strategy - based on big data analytics - lowers service degradation by more than 51%, compared to priority unaware approaches.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-227881DOI: 10.1109/ECOC.2017.8346133Scopus ID: 2-s2.0-85046942314ISBN: 9781538656242 (print)OAI: oai:DiVA.org:kth-227881DiVA, id: diva2:1205533
Conference
43rd European Conference on Optical Communication, ECOC 2017, Gothenburg, Sweden, 17 September 2017 through 21 September 2017
Note

QC 20180515

Available from: 2018-05-14 Created: 2018-05-14 Last updated: 2018-09-19Bibliographically approved
In thesis
1. Orchestration Strategies for Slicing in 5G Networks: Design and Performance Evaluation
Open this publication in new window or tab >>Orchestration Strategies for Slicing in 5G Networks: Design and Performance Evaluation
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The advent of 5th generation of mobile networks (5G) will introduce new challenges for the infrastructure providers (InPs). One of the major challenges is to provide a common platform for supporting a large variety of services. Such a platform can be realized by creating slices, which can be dynamically scaled up/down according to variation of service requirements. An InP generates revenue by accepting a slice request, however it has to pay a penalty if a slice cannot be scaled up when required. Hence, an InP needs to design intelligent policies (e.g., using big data analytics (BDA) or reinforcement learning (RL)) which maximize its net profit.

This thesis presents the design and performance evaluation of different orchestration strategies for dynamic slicing of infrastructure resources. Apart from simulation, some strategies are also experimentally demonstrated. The analysis is presented for both single-tenant and multi-tenant cases.

For single-tenant case, this thesis proposes a dynamic slicing strategy for a centralized radio access network with optical transport. Results show that an InP needs to deploy 31.4% less transport resources when using dynamic slicing as compared to overprovisioning.  For multi-tenant case, this thesis presents MILP formulations and heuristic algorithms for dynamic slicing. Results show that, via dynamic slicing, it is possible to achieve 5 times lower slice rejection probability as compared to static slicing.

The analysis is then extended to how BDA can be used in the slice admission and scaling processes. The proposed BDA-based admission policy increases the profit of an InP by up to 49% as compared to an admission policy without BDA. Moreover, the proposed BDA-based scaling policy lowers the penalty by more than 51% as compared to a first-come-first-served policy. Finally, this thesis presents how RL can be used for slice admission. The proposed policy performs up to 54.5% better as compared to deterministic heuristics.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2018. p. iii-xvii, 85
Series
TRITA-EECS-AVL ; 2018:56
Keywords
software defined networking, network function virtualization, orchestration, dynamic slicing, 5G, big data analytics, reinforcement learning
National Category
Communication Systems
Research subject
Information and Communication Technology
Identifiers
urn:nbn:se:kth:diva-235262 (URN)978-91-7729-912-7 (ISBN)
Public defence
2018-10-18, Ka-Sal C (Sal Sven-Olof Öhrvik), Electrum, Kungl Tekniska högskolan, Kistagången 16, Kista, 10:00 (English)
Opponent
Supervisors
Note

QC 20180919

Available from: 2018-09-19 Created: 2018-09-19 Last updated: 2018-09-20Bibliographically approved

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Raza, Muhammad RehanWosinska, LenaMonti, Paolo

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