Resource usage prediction for optimal and balanced provision of multimedia services

Yiannos Kryftis, Constandinos X. Mavromoustakis, George Mastorakis, Evangelos Pallis, Jordi Mongay Batalla, Georgios Skourletopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

25 Citations (Scopus)

Abstract

This paper proposes a novel network architecture for optimal and balanced provision of multimedia services, exploiting a resource prediction system. This architecture enables for the long-term prediction of multimedia services future demands, based on the history of previous network resources usage. The proposed research approach provides the opportunity for the optimal distribution of streaming data, among Content Delivery Networks, cloud-based providers and Home Media Gateways. The short-term prediction that is performed, enables for making the proper decisions by the system, according to specific network metrics, towards achieving higher Quality of Service and Quality of Experience for the end users. The validity of the proposed system is verified through several sets of extended experimental simulation tests, carried out under controlled simulation conditions.

Original languageEnglish
Title of host publication2014 IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages255-259
Number of pages5
ISBN (Electronic)9781479957255
DOIs
Publication statusPublished - 5 Feb 2014
Event2014 IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2014 - Athens, Greece
Duration: 1 Dec 20143 Dec 2014

Other

Other2014 IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2014
Country/TerritoryGreece
CityAthens
Period1/12/143/12/14

Keywords

  • Content Delivery Networks
  • Multimedia Services Systems
  • Quality of Experience
  • Resource Prediction Engine

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