An Online Method for Minimizing Network Monitoring Overhead

Simone Silvestri, Rahul Urgaonkar, Murtaza Zafer, Bong Jun Ko

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


Network monitoring is an essential component of network operation and, as the network size increases, it usually generates a significant overhead in large scale networks such as sensor and data center networks. In this paper, we show that measurement correlation often exhibited in real networks can be successfully exploited to reduce the network monitoring overhead. In particular, we propose an online adaptive measurement technique with which a subset of nodes are dynamically chosen as monitors while the measurements of the remaining nodes are estimated using the computed correlations. We propose an estimation framework based on jointly Gaussian distributed random variables, and formulate an optimization problem to select the monitors which minimize the estimation error under a total cost constraint. We show that the problem is NP-Hard and propose three efficient heuristics. In order to apply our framework to real-world networks, in which measurement distribution and correlation may significantly change over time, we also develop a learning based approach that automatically switches between learning and estimation phases using a change detection algorithm. Simulations carried out on two real traces from sensor networks and data centers show that our algorithms outperforms previous solutions based on compressed sensing and it is able to reduce the monitoring overhead by 50% while incurring a low estimation error. The results further demonstrate that applying the change detection algorithm reduces the estimation error up to two orders of magnitude.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE 35th International Conference on Distributed Computing Systems, ICDCS 2015
Number of pages10
ISBN (Electronic)9781467372145
StatePublished - Jul 22 2015
Event35th IEEE International Conference on Distributed Computing Systems, ICDCS 2015 - Columbus, United States
Duration: Jun 29 2015Jul 2 2015

Publication series

NameProceedings - International Conference on Distributed Computing Systems


Conference35th IEEE International Conference on Distributed Computing Systems, ICDCS 2015
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications


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