A context-aware system for efficient peer-to-peer content provision

E. Markakis, G. Mastorakis, D. Negru, E. Pallis, C. X. Mavromoustakis, A. Bourdena

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The peer-to-peer (P2P) computing paradigm is considered to be a scalable and cost-effective solution for content distribution that is becoming widely deployed over the current Internet infrastructure. Moreover, several commercial deployments already exist, providing video on demand and IPTV services to a large number of end-users and customers. When compared to content distribution network (CDN)-assisted content delivery, or other managed network-based approaches, P2P schemes become very attractive solutions. Taking into consideration that a home-box (HB) entity has built-in storage, stable presence, monitoring capability, and management interface, it makes it a good candidate for participating in P2P networks. In this framework, an HB P2P Engine module can be exploited to assist the content distribution. Instead of retrieving content from the servers solely, the HB can download content from other HBs, which are caching and forwarding/seeding the content. Under these circumstances, this chapter proposes a context-aware system to support an efficient resource sharing among different HBs, by exploiting P2P approaches and configurations.

Original languageEnglish
Title of host publicationPervasive Computing
Subtitle of host publicationNext Generation Platforms for Intelligent Data Collection
PublisherElsevier Inc.
Pages131-151
Number of pages21
ISBN (Electronic)9780128037027
ISBN (Print)9780128036631
DOIs
Publication statusPublished - 13 Apr 2016

Keywords

  • Content distribution networks
  • Context-aware systems
  • Efficient resource sharing
  • Home-box
  • Peer-to-peer computing

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    Markakis, E., Mastorakis, G., Negru, D., Pallis, E., Mavromoustakis, C. X., & Bourdena, A. (2016). A context-aware system for efficient peer-to-peer content provision. In Pervasive Computing: Next Generation Platforms for Intelligent Data Collection (pp. 131-151). Elsevier Inc.. https://doi.org/10.1016/B978-0-12-803663-1.00005-X