SkEyeNet: Neural Network–Based Autonomic Management of Structured P2P Overlays
ID:80 View Protection:PUBLIC Updated Time:2026-10-12 17:26:34 Hits:196 Online

Start Time:2026-10-13 10:45(Asia/Ho_Chi_Minh)

Duration:15min

Session:S2 Track 2: IoT and applications » S2-3Track 2: IoT and applications

Abstract
The peer-to-peer paradigm can harness the resources of edge devices while reducing dependence on centrally operated servers and infrastructure. To maintain the quality of peer-to-peer systems, monitoring and management mechanisms are required to provide reliable services despite unreliable, autonomous, and potentially insecure nodes. In this paper we present a monitoring and management framework for structured peer-to-peer systems, termed SkEyeNet, that enables the system to monitor its behaviour and to adapt to the current status of demand. It captures the current state of a peer-to-peer network using an aggregated statistical representation. Following the principles of autonomic computing, the system automatically adapts its configuration when deviations from predefined quality targets are detected. Evaluation shows that the monitoring is precise and lightweight and that preset quality goals are reached and kept automatically.
Keywords
Speaker
Kalman Graffi
Professor RheinMain University of Applied Sciences

Submission Author
Kalman Graffi RheinMain University of Applied Sciences
Michael Behrisch Utrecht University
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Important Date
  • Conference Date

    Oct 11

    2026

    to

    Oct 14

    2026

  • Dec 30 2025

    Contribution Submission Deadline

  • Sep 28 2026

    Early Bird Registration

  • Oct 14 2026

    Draft paper submission deadline

  • Oct 15 2026

    Registration deadline

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