A Multi-commodity Network Flow Model for Cloud Service Environments View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2016-08-19

AUTHORS

Ioannis M. Stephanakis , Syed Noor-Ul-Hassan Shirazi , Antonios Gouglidis , David Hutchison

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 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$D^2R^2+DR$$\end{document} 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. More... »

PAGES

186-197

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-44188-7_14

DOI

http://dx.doi.org/10.1007/978-3-319-44188-7_14

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1050999624


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