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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
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/ISSN › Conference contribution/Paper › peer-review
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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 -