A stochastic analytical modeling framework on ISP-P2P collaborations in multidomain environments

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Zhang, Xu ORCID: https://orcid.org/0000-0001-6557-6607, Wang, Ning, Cao, Yue, Peng, Linyu and Meng, Haining (2018) A stochastic analytical modeling framework on ISP-P2P collaborations in multidomain environments. IEEE Systems Journal, 12 (3). pp. 2320-2331. ISSN 1932-8184

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Abstract

Cooperation between peer-to-peer (P2P) overlays and underlying networks has been proposed as an effective approach to improve the efficiency of both the applications and the underlying networks. However, fundamental characteristics with respect to Internet service providers (ISP) business relationships and inter-ISP routing information are not sufficiently investigated in the context of collaborative ISP-P2P paradigms in multidomain environments. In this paper, we focus on such issues and develop an analytical modeling framework for analyzing optimized interdomain peer selection schemes concerning ISP policies, with the main purpose of mitigating cross-ISP traffic and enhancing service quality of end users. In addition, we introduce an advanced hybrid scheme for peer selections based on the proposed analytical theory framework, in accordance with practical network scenarios, wherein cooperative and noncooperative behaviors coexisting. Numerical results show that the proposed scheme incorporating ISP policies is able to achieve desirable network efficiency as well as great service quality for P2P users. Our analytical modeling framework can be used as a guide for analyzing and evaluating future network-aware P2P peer selection paradigms in general multidomain scenarios.

Item Type:	Article
Additional Information:	Publisher Copyright: © 2018 IEEE.
Uncontrolled Keywords:	analytical modeling,markov chain,peer selection algorithms,peer-to-peer (p2p) systems,control and systems engineering,information systems,computer science applications,computer networks and communications,electrical and electronic engineering ,/dk/atira/pure/subjectarea/asjc/2200/2207
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and AI
Related URLs:	http://www.scopus.com/inward/record.url?... https://openresearch.surrey.ac.uk/esplor...
Depositing User:	LivePure Connector
Date Deposited:	26 Jan 2024 02:15
Last Modified:	10 Dec 2024 01:43
URI:	https://ueaeprints.uea.ac.uk/id/eprint/94262
DOI:	10.1109/JSYST.2017.2725914

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