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Efficient Messaging Overhead for Distributed Detection and Inference Systems

This study by Soumya Sen (UPenn) evaluates the impact of messaging overhead in Distributed Detection and Inference (DDI) systems. The research focuses on implementing a scalable gossip-based messaging scheme for membership maintenance to enhance system efficiency. The evaluation includes the effect of packet duplication on detection accuracy in the DDI system architecture. Key challenges addressed include bootstrapping, isolation recovery, and partition prevention. Figures depict the extent of LR duplication and the effect of varying effective fanout on worm detection.

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Efficient Messaging Overhead for Distributed Detection and Inference Systems

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  1. Estimation of Messaging Overhead Distributed Detection and Inference (DDI) Soumya Sen (UPenn), Supervisors: Carl Livadas, Eve Schooler, DDI team • Objective – Understand overhead of DDI messaging • Challenge – Design/evaluate scalable scheme for membership maintenance • Approach – Implement a gossip-based messaging and membership scheme • Achievement –Evaluation of effect of packet duplication on detection The DDI System Architecture Messaging & Membership Scheme • Local Detectors (LDs) gossip their Local Reports (LRs) to random subset of Global Detectors (GDs) • LDs and GDs maintain random partial membership view • Membership views exchanged between LDs and GDs to ensure view uniformity and prevent node isolation and overlay partition • Challenges: Bootstrapping, Isolation Recovery, Partition Prevention/Recovery, Un-subscriptions Effect of LR Duplication on Detection Extent of LR duplication Fig: % of non-duplicate LRs Vs Effective Fanout Fig. Effect of varying effective fanout on worm detection

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