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Hidden Markov Model for short term churn forecast in the structured overlay networks

  • 1174: Futuristic Trends and Innovations in Multimedia Systems Using Big Data, IoT and Cloud Technologies (FTIMS)
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Abstract

The inherent scalability and flexibility of structured overlay networks makes them an excellent choice to support modern day applications with complex, volatile, mobile, and heterogeneous infrastructure. However, this heterogeneity and volatility of the infrastructure increase the need for more reliable maintenance mechanisms to guarantee the availability and performance of structured overlay networks in the presence of autonomous participants. This paper focus on the implementation of the state-based predictive maintenance mechanism that is based on the intelligent prediction of the dynamics of the neighbouring node (k closest successors as defined in a DHT finger table) to schedule proactive maintenance of the nodes having periodic availabilities. The paper provides the predictive analysis of the uptime patterns of the machines specified in the Microsoft trace file using rapid miner and yielded 95% prediction accuracy. The proposed predictive framework is also tested in the simulation environments and the results show significant performance improvements by implementing the predictive maintenance approach as compared to the state-of-the-art statically scheduled maintenance actions. The simulation results report 54.23% increase in the average lookup success ratio and 59% reduction in the maintenance overhead of chord-based overlay network by the implementation of the proposed technique.

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Acknowledgements

The author would like to thank Dr. Tomaž Klobučar and Dr. Dušan Gabrijelčič for their work in proofreading and technical editing of the manuscript.

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  1. Laboratory for Open Systems and Networks, Jožef Stefan Institute, Ljubljana, Slovenia

    Ramanpreet Kaur

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  1. Ramanpreet Kaur
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Correspondence to Ramanpreet Kaur.

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Kaur, R. Hidden Markov Model for short term churn forecast in the structured overlay networks. Multimed Tools Appl 81, 34481–34499 (2022). https://doi.org/10.1007/s11042-021-11831-x

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