Abstract
In this paper, we propose to use graph-mining techniques to understand the communication pattern within a data-centre. We present techniques to identify frequently occurring sub-graphs within this temporal sequence of communication graphs. We argue that identification of such frequently occurring sub-graphs can provide many useful insights about the functioning of the system. We demonstrate how the existing frequent sub-graph discovery algorithms can be modified for the domain of communication graphs in order to provide computationally light-weight and accurate solutions. We present two algorithms for extracting frequent communication sub-graphs and present a detailed experimental evaluation to prove the correctness and efficiency of the proposed algorithms.
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Natu, M., Sadaphal, V., Patil, S., Mehrotra, A. (2011). Mining Frequent Subgraphs to Extract Communication Patterns in Data-Centres. In: Aguilera, M.K., Yu, H., Vaidya, N.H., Srinivasan, V., Choudhury, R.R. (eds) Distributed Computing and Networking. ICDCN 2011. Lecture Notes in Computer Science, vol 6522. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17679-1_21
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DOI: https://doi.org/10.1007/978-3-642-17679-1_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17678-4
Online ISBN: 978-3-642-17679-1
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