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A Multi-commodity network flow model for cloud service environments

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A Multi-commodity network flow model for cloud service environments. / Stephanakis, Ioannis M. ; Shirazi, Syed Noor Ul Hassan ; Gouglidis, Antonios; Hutchison, David.

Engineering Applications of Neural Networks: 17th International Conference, EANN 2016, Aberdeen, UK, September 2-5, 2016, Proceedings. ed. / Chrisina Jayne; Lazaros Iliadis. Cham : Springer, 2016. p. 186-197 (Communications in Computer and Information Science; Vol. 629).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Stephanakis, IM, Shirazi, SNUH, Gouglidis, A & Hutchison, D 2016, A Multi-commodity network flow model for cloud service environments. in C Jayne & L Iliadis (eds), Engineering Applications of Neural Networks: 17th International Conference, EANN 2016, Aberdeen, UK, September 2-5, 2016, Proceedings. Communications in Computer and Information Science, vol. 629, Springer, Cham, pp. 186-197, 17th International Conference on Engineering Applications of Neural Networks, Aberdeen, United Kingdom, 2/09/16.

APA

Stephanakis, I. M., Shirazi, S. N. U. H., Gouglidis, A., & Hutchison, D. (2016). A Multi-commodity network flow model for cloud service environments. In C. Jayne, & L. Iliadis (Eds.), Engineering Applications of Neural Networks: 17th International Conference, EANN 2016, Aberdeen, UK, September 2-5, 2016, Proceedings (pp. 186-197). (Communications in Computer and Information Science; Vol. 629). Springer.

Vancouver

Stephanakis IM, Shirazi SNUH, Gouglidis A, Hutchison D. A Multi-commodity network flow model for cloud service environments. In Jayne C, Iliadis L, editors, Engineering Applications of Neural Networks: 17th International Conference, EANN 2016, Aberdeen, UK, September 2-5, 2016, Proceedings. Cham: Springer. 2016. p. 186-197. (Communications in Computer and Information Science).

Author

Stephanakis, Ioannis M. ; Shirazi, Syed Noor Ul Hassan ; Gouglidis, Antonios ; Hutchison, David. / A Multi-commodity network flow model for cloud service environments. Engineering Applications of Neural Networks: 17th International Conference, EANN 2016, Aberdeen, UK, September 2-5, 2016, Proceedings. editor / Chrisina Jayne ; Lazaros Iliadis. Cham : Springer, 2016. pp. 186-197 (Communications in Computer and Information Science).

Bibtex

@inproceedings{d9af6070b2134f74a1227ee6a5545031,
title = "A Multi-commodity network flow model for cloud service environments",
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.",
keywords = "Resilience, Big data cloud, Multi-commodity flow networks, Distributed algorithms, Consensus optimization, Alternating direction method of multipliers (ADMM)",
author = "Stephanakis, {Ioannis M.} and Shirazi, {Syed Noor Ul Hassan} and Antonios Gouglidis and David Hutchison",
year = "2016",
language = "English",
isbn = "9783319441870",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "186--197",
editor = "Chrisina Jayne and Lazaros Iliadis",
booktitle = "Engineering Applications of Neural Networks",
note = "17th International Conference on Engineering Applications of Neural Networks, EANN 2016 ; Conference date: 02-09-2016 Through 05-09-2016",
url = "http://eann.org.uk/eann2016/index.php",

}

RIS

TY - GEN

T1 - A Multi-commodity network flow model for cloud service environments

AU - Stephanakis, Ioannis M.

AU - Shirazi, Syed Noor Ul Hassan

AU - Gouglidis, Antonios

AU - Hutchison, David

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Resilience

KW - Big data cloud

KW - Multi-commodity flow networks

KW - Distributed algorithms

KW - Consensus optimization

KW - Alternating direction method of multipliers (ADMM)

M3 - Conference contribution/Paper

SN - 9783319441870

T3 - Communications in Computer and Information Science

SP - 186

EP - 197

BT - Engineering Applications of Neural Networks

A2 - Jayne, Chrisina

A2 - Iliadis, Lazaros

PB - Springer

CY - Cham

T2 - 17th International Conference on Engineering Applications of Neural Networks

Y2 - 2 September 2016 through 5 September 2016

ER -