A Multi-commodity network flow model for cloud service environments

Stephanakis, Ioannis M. and Shirazi, Syed Noor Ul Hassan and Gouglidis, Antonios and Hutchison, David (2016) A Multi-commodity network flow model for cloud service environments. In: Engineering Applications of Neural Networks : 17th International Conference, EANN 2016, Aberdeen, UK, September 2-5, 2016, Proceedings. Communications in Computer and Information Science . Springer, GBR, pp. 186-197. ISBN 9783319441870

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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.

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?? resiliencebig data cloudmulti-commodity flow networksdistributed algorithmsconsensus optimizationalternating direction method of multipliers (admm) ??
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14 Jun 2016 12:04
Last Modified:
21 Apr 2024 23:30