Home > Research > Publications & Outputs > A Multi-commodity network flow model for cloud ...

Electronic data

  • mnfm-paper

    Accepted author manuscript, 626 KB, PDF-document

View graph of relations

A Multi-commodity network flow model for cloud service environments

Research output: Contribution in Book/Report/ProceedingsConference contribution

Published
Publication date2016
Host publicationEngineering Applications of Neural Networks: 17th International Conference, EANN 2016, Aberdeen, UK, September 2-5, 2016, Proceedings
EditorsChrisina Jayne, Lazaros Iliadis
Place of PublicationCham
PublisherSpringer
Pages186-197
Number of pages12
ISBN (Electronic)9783319441887
ISBN (Print)9783319441870
<mark>Original language</mark>English
Event17th International Conference on Engineering Applications of Neural Networks - Aberdeen, United Kingdom

Conference

Conference17th International Conference on Engineering Applications of Neural Networks
Abbreviated titleEANN 2016
CountryUnited Kingdom
CityAberdeen
Period2/09/165/09/16
Internet address

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer
Volume629
ISSN (Print)1865-0929

Conference

Conference17th International Conference on Engineering Applications of Neural Networks
Abbreviated titleEANN 2016
CountryUnited Kingdom
CityAberdeen
Period2/09/165/09/16
Internet address

Abstract

Next-generation systems, such as the big data cloud, have to cope with several challenges, e.g., move of excessive amount of data at a dictated speed, and thus, require the investigation of concepts additional to security in order to ensure their orderly function. Resilience is such a concept, which when ensured by systems or networks they are able to provide and maintain an acceptable level of service in the face of various faults and challenges. In this paper, we investigate the multi-commodity flows problem, as a task within our D 2 R 2 +DR resilience strategy, and in the context of big data cloud systems. Specifically, proximal gradient optimization is proposed for determining optimal computation flows since such algorithms are highly attractive for solving big data problems. Many such problems can be formulated as the global consensus optimization ones, and can be solved in a distributed manner by the alternating direction method of multipliers (ADMM) algorithm. Numerical evaluation of the proposed model is carried out in the context of specific deployments of a situation-aware information infrastructure.