Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Low complexity content replication through clustering in Content-Delivery Networks
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-9810-3478
2017 (English)In: Computer Networks, ISSN 1389-1286, E-ISSN 1872-7069, Vol. 121, 137-151 p.Article in journal (Refereed) Published
Abstract [en]

Contemporary Content Delivery Networks (CDN) handle a vast number of content items. At such a scale, the replication schemes require a significant amount of time to calculate and realize cache updates, and hence they are impractical in highly-dynamic environments. This paper introduces cluster-based replication, whereby content items are organized in clusters according to a set of features, given by the cache/network management entity. Each cluster is treated as a single item with certain attributes, e.g., size, popularity, etc. and it is then altogether replicated in network caches so as to minimize overall network traffic. Clustering items reduces replication complexity; hence it enables faster and more frequent caches updates, and it facilitates more accurate tracking of content popularity. However, clustering introduces some performance loss because replication of clusters is more coarse-grained compared to replication of individual items. This tradeoff can be addressed through proper selection of the number and composition of clusters. Due to the fact that the exact optimal number of clusters cannot be derived analytically, an efficient approximation method is proposed. Extensive numerical evaluations of time-varying content popularity scenarios allow to argue that the proposed approach reduces core network traffic, while being robust to errors in popularity estimation.

Place, publisher, year, edition, pages
Elsevier B.V. , 2017. Vol. 121, 137-151 p.
Keyword [en]
Content clustering, Content replication, Content-Delivery Networks, Coordinated caching, Internet, Approximation methods, Content delivery network, Content popularities, Dynamic environments, Overall network traffics, Complex networks
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-216295DOI: 10.1016/j.comnet.2017.04.043ISI: 000403515300012Scopus ID: 2-s2.0-85018471278OAI: oai:DiVA.org:kth-216295DiVA: diva2:1164555
Note

QC 20171211

Available from: 2017-12-11 Created: 2017-12-11 Last updated: 2017-12-11Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Authority records BETA

Fischione, Carlo

Search in DiVA

By author/editor
Fischione, Carlo
By organisation
Automatic ControlACCESS Linnaeus Centre
In the same journal
Computer Networks
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf