A Markov Model of Slice Admission Control

descriptionPublicationkeyboard_double_arrow_right Article , Preprint , Other literature type 01 Mar 2019Embargo end date: 01 Jan 2018Publisher:Institute of Electrical and Electronics Engineers (IEEE)Journal:IEEE Networking Letters, volume 1, pages 2-5 (eissn: 2576-3156,

Copyright policy )Funded by:EC | 5G-MoNArch

Authors: Bin Han 0004; Di Feng; Hans D. Schotten;

doi: 10.1109/lnet.2018.2873978 , 10.48550/arxiv.1804.01861

arXiv: 1804.01861

A Markov Model of Slice Admission Control

- Summary
- Subjects
- Metrics

Abstract

The emerging feature of network slicing in future Fifth Generation (5G) networks calls for efficient slice management. Recent studies have been focusing on the mechanism of slice admission control, which functions in a manner of state machine. This paper proposes a general state model for synchronous slice admission control, and proves it to be Markovian under a set of weak constraints. An analytical approximation of the state transition matrix to reduce computational complexity in practical applications is also proposed and evaluated.

Accepted by IEEE Networking Letters on Aug.30.2018

Related Organizations

Keywords

Computer Science - Networking and Internet Architecture, Networking and Internet Architecture (cs.NI), FOS: Computer and information sciences

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	27
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%