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Detailed diagnosis in enterprise networks

Detailed diagnosis in enterprise networks. Srikanth Kandula, Ratul Mahajan , Patrick Verkaik (UCSD) , Sharad Agarwal, Jitu Padhye, Victor Bahl. Network diagnosis. Explaining faulty behavior. Current landscape of network diagnosis systems. Big enterprises Large ISPs. Small enterprises.

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Detailed diagnosis in enterprise networks

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  1. Detailed diagnosis in enterprise networks Srikanth Kandula, Ratul Mahajan, Patrick Verkaik (UCSD), Sharad Agarwal, Jitu Padhye, Victor Bahl

  2. Network diagnosis Explaining faulty behavior

  3. Current landscape of network diagnosis systems Big enterprises Large ISPs Small enterprises Network size ?

  4. Why study small enterprise networks separately? Big enterprises Large ISPs Small enterprises IIS, SQL, Exchange, …

  5. Our work • Shows that small enterprises need “detailed diagnosis” • Not enabled by current systems that focus on scale • Develops NetMedic for detailed diagnosis • Diagnoses application faults without application knowledge

  6. Understanding problems in small enterprises Symptoms, root causes 100+ cases

  7. And the survey says ….. Handle app-specific as well as generic faults Identify culpritsat a fine granularity Detailed diagnosis

  8. Example problem 1: Server misconfig Browser Web server Server config Browser

  9. Example problem 2: Buggy client SQL client C1 SQL server Requests SQL client C2

  10. Current formulations sacrifice detail (to scale) • Dependency graph based formulations (e.g., Sherlock [SIGCOMM2007]) • Model the network as a dependency graph at a coarse level • Simple dependency model

  11. Example problem 1: Server misconfig Browser Web server Server config Browser The network model is too coarse in current formulations

  12. Example problem 2: Buggy client SQL client C1 SQL server Requests SQL client C2 The dependency model is too simple in current formulations

  13. A formulation for detailed diagnosis Dependency graph of fine-grained components Component state is a multi-dimensional vector SQL client C1 Process OS Config Exch.svr IIS svr SQL svr SQL client C2 IIS config

  14. The goal of diagnosis Identify likely culprits for components of interest Without using semantics of state variables  No application knowledge C1 Process OS Config Svr C2

  15. Using joint historical behavior to estimate impact How “similar” on average states of D are at those times Identify time periods when state of S was “similar” D S C1 H Svr H L C2

  16. Robust implementation of impact estimation • Ignore state variables that represent redundant info • Place higher weight on state variables likely related to faults being diagnosed • Ignore state variables irrelevant to interaction with neighbor • Account for aggregate relationships among state variables of neighboring components • Account for disparate ranges of state variables

  17. Implementation of NetMedic Monitor components Diagnose edge impact path impact Target components Diagnosis time Reference time Component states Ranked list of likely culprits

  18. Evaluation setup IIS, SQL, Exchange, … . . . 10 actively used desktops Diverse set of faults observed in the logs

  19. NetMedic assigns low ranks to actual culprits

  20. NetMedic handles concurrent faults well 2 simultaneous faults

  21. Other results in the paper Netmedic needs a modest amount (~60 mins) of history It compares favorably with a method that understands variable semantics

  22. Conclusions NetMedic enables detailed diagnosis in enterprise networks w/o application knowledge Think small: Small enterprise networks deserve more attention

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