Standard
A Multi-commodity network flow model for cloud service environments. / Stephanakis, Ioannis M.
; Shirazi, Syed Noor Ul Hassan ; Gouglidis, Antonios et al.
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/ISSN › Conference contribution/Paper › peer-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). Epub 2016 Aug 19.
Author
Stephanakis, Ioannis M.
; Shirazi, Syed Noor Ul Hassan ; Gouglidis, Antonios et al. /
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 -
