Modular RADAR: Immune System Inspired Strategies for Distributed Systems


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Talk given at the 9th International Conference on Artificial Immune Systems (ICARIS), 2010, Edinburgh, UK

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Modular RADAR: Immune System Inspired Strategies for Distributed Systems

  1. 1. Modular RADAR: Immune System Inspired Strategies for Distributed Systems Soumya Banerjee and Melanie Moses University of New Mexico
  2. 2. Outline • Distributed systems and the natural immune system (NIS) operate under similar constraints • Effect of body size on NIS search and response times • Scale invariant detection and response • Hypothesis: architecture of the lymphatic system leads to invariant search and response times • Modular RADAR strategy • Number and size of lymph nodes increases with organism size • Distributed systems – P2P system – Multi-robot control • Future directions
  3. 3. Properties of Distributed Systems • Physical space is important • Resource constrained (power, bandwidth) • Performance scalability is a desirable feature
  4. 4. Properties of the Natural Immune System (NIS) • Operates under constraints of physical space • Resource constrained (metabolic input, number of immune system cells) • Performance scalability is an important concern (mice to horses)
  5. 5. Problems Faced by the NIS • Only a few NIS cells are specific to a particular pathogen (1 in 6 10 T-cells)
  6. 6. Search Problem • They have to search throughout the whole body to locate small quantities of pathogens
  7. 7. Response Problem • Have to respond by producing antibodies
  8. 8. West Nile Virus infection  25 species of birds and 4 species of mammals infected with WNV • Bunning et al. (2002) • Komar et al. (2002)  Unimodal peak at ~ 2 to 4 days post infection  Immune response rates and times are not correlated with host mass • assuming immune response causes peak • B-cell response in mice ~ 4 days Komar et al. 2002
  9. 9. Nearly Scale-Invariant Search and Response • Experimental data indicates that the NIS can search for pathogens and respond by producing antibodies in time invariant of organism body size
  10. 10. Nearly Scale-Invariant Search and Response • How does the immune system search and respond in almost the same time irrespective of the size of the search space?
  11. 11. Solution: Lymph Nodes (LN) • A place in which IS cells and the pathogen can encounter each other in a small volume • Form a decentralized detection network Crivellato et al. 2004
  12. 12. Modular RADAR • Search is now – modular – efficient – parallel We call this a modular RADAR (Robust Adaptive Decentralized search Automated Response)
  13. 13. Hypothesis • Architecture of the immune system is responsible for nearly scale-invariant search and response properties • We now focus on West Nile Virus
  14. 14. Lymph Node Dynamics
  15. 15. Lymph Node Dynamics
  16. 16. Lymph Node Dynamics
  17. 17. DC DC cTcell,DC T t detect t migrate t detect trecruit
  18. 18. Scaling of LN Size and Number T t local t global DC DC DC,cTcell T t detect t migrate t detect t recruit After minimizing we have 4 /7 N M ,where N is the number of LNs 3/7 VLN M ,where VLN is the size of a LN • this is in qualitative agreement with data • need more data Banerjee and Moses 2010, Swarm Intelligence (under review)
  19. 19. Modular RADAR Architecture T t local t global M1/ 7
  20. 20. Summary • There are increasing costs to global communication as organisms grow bigger • Semi-modular architecture balances the opposing goals of detecting pathogen (local communication) and recruiting IS cells (global communication) • This leads to scale invariant detection and response • Can we emulate this modular RADAR strategy in distributed systems?
  21. 21. Peer-to-Peer Systems • Used to provide distributed services like search, content integration and administration • Computer nodes store data or service • No single node has complete global information • Decentralized search using local information to locate data
  22. 22. Semantic Small World (SSW) P2P Overlay Network • Represents objects by a collection of attribute values derived from object content • Aggregates data objects with similar semantics close to each other in clusters in order to facilitate efficient search • It maintains short and long-distance connections between clusters. • The long-distance connections follow a precise probability distribution making the whole overlay network small-world (Kleinberg 2000) * M. Li et al. 2004
  23. 23. Semantic Small World (SSW) P2P Overlay Network adapted from M. Li et al. 2004
  24. 24. Bounds for Efficient Decentralized Search in SSW • Average search path length for search across clusters is 2 log (n /c) tglobal O l where n is the total number of nodes, c is the number of nodes in a cluster, l is the number of long-distance connections per node M. Li et al. 2004
  25. 25. SSW with Modular RADAR • Our contribution is to – vary the cluster size – vary the number of long-distance connections as l log(n /c) log(numclusters) t global O(log(n /c)) – such densification is seen as an emergent property of technological networks (Kleinberg 2004) and also incorporates redundant paths
  26. 26. Time to Search in SSW with Modular RADAR T t local t global 1/ 2 T 1c 2 log(n /c) minimizing by differentiating with respect to c we have c O(log 2 n) T O(log n log log n)
  27. 27. SSW with Modular RADAR
  28. 28. Wireless Mobile Devices: Original System adapted from Nair et al. 2008
  29. 29. Tradeoffs • Potential communication bottlenecks – local communication between robots and computer servers – global communication between computer servers • If both local and global communication are constrained, then sub-modular architecture balances tradeoff
  30. 30. System modified with modular RADAR
  31. 31. Future Directions • Strategy is widely applicable • A modular RADAR strategy can be used to augment – Intrusion Detection Systems (Hofmeyr and Forrest 1999) – Multi-Robot Control – Wireless Sensor Networks – Wireless Devices (Specknets: Hart and Davoudani 2009) – Collective Robotic Systems using Artificial Lymph Node Architectures (Mokhtar, Timmis, Tyrrell and Bi 2008)
  32. 32. Summary • The NIS and distributed systems operate under similar constraints • Physical space of organism body constrains NIS search and response times • The NIS has evolved a sub-modular RADAR architecture in which LN numbers and sizes increase with organism body size • This balances the tradeoff between local communication (pathogen detection) and global communication (antibody production); this leads to scale invariant detection and response • Similar tradeoffs also exist in distributed systems • Such a modular RADAR approach is shown to improve search times in P2P and multi-robot control systems • Can be applied in other distributed systems
  33. 33. Acknowledgements • Dr. Melanie Moses • SFI Complex Systems • Dr. Alan Perelson Summer School • Dr. Stephanie • Travel grants from Forrest PIBBS (Dept. of Biology, UNM) • Dr. Jedidiah Crandall • Travel grants from • Dr. Rob Miller RPT and SCAP • Dr. Sam Loker (UNM) • NIH COBRE CETI grant (RR018754)