ATMoN: Adapting the 'temporality' in large-scale dynamic networks

Demetris Trihinas, Luis F. Chiroque, George Pallis, Antonio Fernández Anta, Marios D. Dikaiakos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

With the widespread adoption of temporal graphs to study fast evolving interactions in dynamic networks, attention is needed to provide graph metrics in time and at scale. In this paper, we introduce ATMoN, an open-source library developed to computationally offload graph processing engines and ease the communication overhead in dynamic networks over an unprecedented wealth of data. This is achieved, by efficiently adapting, in place and inexpensively, the temporal granularity at which graph metrics are computed based on runtime knowledge captured by a low-cost probabilistic learning model capable of approximating both the metric stream evolution and the volatility of the graph topology. After a thorough evaluation with real-world data from mobile, face-to-face and vehicular networks, results show that ATMoN is able to reduce the compute overhead by at least 76%, data volume by 60% and overall cloud costs by at least 54%, while always maintaining accuracy above 88%.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 38th International Conference on Distributed Computing Systems, ICDCS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages400-410
Number of pages11
ISBN (Electronic)9781538668719
DOIs
Publication statusPublished - 19 Jul 2018
Event38th IEEE International Conference on Distributed Computing Systems, ICDCS 2018 - Vienna, Austria
Duration: 2 Jul 20185 Jul 2018

Publication series

NameProceedings - International Conference on Distributed Computing Systems
Volume2018-July

Conference

Conference38th IEEE International Conference on Distributed Computing Systems, ICDCS 2018
CountryAustria
CityVienna
Period2/07/185/07/18

Keywords

  • Adaptive Monitoring
  • Dynamic Networks
  • Edge Computing
  • Temporal Graphs

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    Trihinas, D., Chiroque, L. F., Pallis, G., Fernández Anta, A., & Dikaiakos, M. D. (2018). ATMoN: Adapting the 'temporality' in large-scale dynamic networks. In Proceedings - 2018 IEEE 38th International Conference on Distributed Computing Systems, ICDCS 2018 (pp. 400-410). (Proceedings - International Conference on Distributed Computing Systems; Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDCS.2018.00047