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Balasubramaneyam Maniymaran Ph.D. Student, Department of Electrical & Computer Engineering,

Resource Addressable Network: An Adaptive Peer-to-Peer Discovery Substrate for Internet-Scale Service Platforms. Balasubramaneyam Maniymaran Ph.D. Student, Department of Electrical & Computer Engineering, McGill University Supervisor: Dr. Muthucumaru Maheswaran. Introduction.

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Balasubramaneyam Maniymaran Ph.D. Student, Department of Electrical & Computer Engineering,

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  1. Resource Addressable Network: An Adaptive Peer-to-Peer Discovery Substrate for Internet-Scale Service Platforms Balasubramaneyam Maniymaran Ph.D. Student, Department of Electrical & Computer Engineering, McGill University Supervisor: Dr. Muthucumaru Maheswaran

  2. Introduction • On-demand computing (ODC) an emerging model for next generation systems. • Peer-to-peer (P2P) is one way of building ODC systems. • P2P Grid, P2P CDNs, public computing utilities. • To assemble ODC from P2P resource base. • Need a generalized resource discovery scheme. • Discover resources based on given requirements. • Resource addressable network (RAN). • Discovers resources based on attributes and location. • One of the major concerns in RAN is scalability: • Low overhead in managing overlay and information. • Three design concepts: fully decentralized, distributed knowledge, and adaptive design. Advanced Networking Research Laboratory, The School of Computer Science, McGill University, Montreal, QC, Canada.

  3. RAN discovery substrate Profile-based discovery Location-based discovery Resource Addressable Network • RAN: middle layer between services and resources. • Attribute-based and location-based discovery. ODC Service Naming the resources based on their attributes Profile-based naming Network positioning mechanism, assigning coordinates for each node in the network delay space Landmark-aided positioning Physical Resources Advanced Networking Research Laboratory, The School of Computer Science, McGill University, Montreal, QC, Canada.

  4. Network Positioning • Network positioning: assigning coordinates for the nodes in a virtual Cartesian space, from which real network delay can be predicted. Internet l12 l12 ≈ √[(x1-x2)2+(y1-y2)2] y (x2, y2) Cartesian space (x1, y1) x Advanced Networking Research Laboratory, The School of Computer Science, McGill University, Montreal, QC, Canada.

  5. Landmark Aided Positioning • Landmark aided positioning (LAP): the network positioning scheme for RAN • Using a set of landmarks. • Other nodes: • Select a subset of the total landmarks and ping them. • Run optimization algorithm to position themselves to minimize the total error in distance prediction. • Two phases of LAP: • Landmark positioning: positioning the landmarks. • Node positioning: positioning the normal nodes. Advanced Networking Research Laboratory, The School of Computer Science, McGill University, Montreal, QC, Canada.

  6. LAP:: Landmark Positioning • Each landmark calculates its coordinate relative to other landmarks. • Landmark positioning involves two loops: • Inner loop contains the iteration for node positioning. • Mostly affects the computational complexity. • Outer loop contains many node positioning phases. • Between each node positioning phase, nodes have to contact others to get their new coordinates  message complexity. • Simplex and Spring both found to be producing high outer loop iterations. • Introducing new algorithm called SpringEq. Advanced Networking Research Laboratory, The School of Computer Science, McGill University, Montreal, QC, Canada.

  7. LAP:: Landmark Positioning(cont…) • SpringEq (short for “spring in equilibrium”): • Inspired from Spring; same spring system concept. • But instead of minimizing the deformations in the spring, SpringEq consider the equilibrium condition. • The resultant force applied at each node is zero. • A spring system at equilibrium can be modelled by a set of simultaneous equations. • SpringEq solves this simultaneous equation using fast iterative process. Advanced Networking Research Laboratory, The School of Computer Science, McGill University, Montreal, QC, Canada.

  8. LAP:: Landmark Positioning (cont…) SpringEq Simplex Simplex Spring SpringEq Spring • Random network configuration; 100 landmarks. • Distance correlation: correlation between the ping and calculated distance matrices. • Simplex – good prediction, but too many iterations; Spring – comparatively few iterations, but bad prediction; • SpringEq outperforms both Simplex and Spring. No. of iteration vs. ping error. Distance correlation vs. ping error. Advanced Networking Research Laboratory, The School of Computer Science, McGill University, Montreal, QC, Canada.

  9. Clustered LAP • Non-random network errors severely impact positioning. • Data smoothing and optimization can not handle this. • Clustered landmark aided positioning (CLAP): • CLAP assumptions: • Network errors are created by abnormal congestion. • Peers within the same network segment can be trusted. • Congestion does not affect pings within a network segment. join a cluster ping others and share the values with others cluster initialization calculate clusterdiameter find coordinates using simple LAP (SLAP) inter exchange SLAP coordinates CLAP adjustment calculate clustercentroid tune SLAP coordinates so that it lies within cluster diameter Advanced Networking Research Laboratory, The School of Computer Science, McGill University, Montreal, QC, Canada.

  10. CLAP Performance CLAP Experiment with Planetlab data. CLAP’s minimum performance is better than SLAP’s maximum performance. SLAP CLAP is relatively robust Variation of distance correlation with increasing network congestion. Advanced Networking Research Laboratory, The School of Computer Science, McGill University, Montreal, QC, Canada.

  11. CLAP Performance (cont…) CLAP SLAP Cumulative distribution of relative distance error in the system for different amount of network congestion. Advanced Networking Research Laboratory, The School of Computer Science, McGill University, Montreal, QC, Canada.

  12. Location-based Discovery • Finding a resource at specific coordinate/range: • Multidimensional search – spatial data structure. • Chose Hilbert curve as the data structure. • Hilbert curve: • Provides a d-D to 1-D mapping. • Preserving proximity. • Hierarchical Hilbert index  location ID (LID). Advanced Networking Research Laboratory, The School of Computer Science, McGill University, Montreal, QC, Canada.

  13. Location-based Discovery (cont…) Hilbert mapping of the nodes in Planetlab network (n = 133, approximation level = 7) Advanced Networking Research Laboratory, The School of Computer Science, McGill University, Montreal, QC, Canada.

  14. Location-based Discovery (cont…) Routing table at node with LID = 2.3.3.1.0 • Routing table for location-based discovery. • Non-zero error in pings justifies fixed length LIDs. • Ring pointers ensuring connectivity; jump pointers enhancing route complexity. • Average search hop complexity = h (approx. level)  O(1). Advanced Networking Research Laboratory, The School of Computer Science, McGill University, Montreal, QC, Canada.

  15. Profile-based Discovery • Discovery systems implements naming schemes: • Label-based naming (LBN): DNS, IP Address. • Scalable, but not flexible. • Description-based naming (DBN): LDAP. • Flexible, but with high overhead due to information maintenance, complex matching algorithms. • Introducing profile based naming (PBN): • Labels popular attribute-value combinations. • Combines the goods of LBN and DBN. • Can not discover all the attribute-value combinations. • Trading off flexibility (performance) for scalability. Advanced Networking Research Laboratory, The School of Computer Science, McGill University, Montreal, QC, Canada.

  16. Profile-based Discovery(cont…) profiles profile space description 1 2 3 Profile IDs description space Profile 1: {Intel/AMD, ≤ 512MB} : 0.* Profile 2: {Intel with 1GB} : 1.0 Profile 3: {Intel/AMD, > 1GB} : [1.1,1.2] • Profile-based routing table is very similar to location-based routing table. Advanced Networking Research Laboratory, The School of Computer Science, McGill University, Montreal, QC, Canada.

  17. Related Works • Network positioning: • GNP: • Centralized implementation, fixed set of landmarks. • Vivaldi: • Dynamic landmarks: anybody can be a landmark. • New node disturbing others, requires RPC calls. • Others: NPS, PIC, big-bang simulation, PCoord. • LAP: • Semi-dynamic landmarks. • Low message overhead design. • CLAP improvement. • RAN infrastructure helps selecting landmarks. Advanced Networking Research Laboratory, The School of Computer Science, McGill University, Montreal, QC, Canada.

  18. Related Works(cont…) • Location-based discovery: • SkipNet: proximity based on DNS names – fails outside DNS structure. • Pastry, expressway of CAN: document discovery. • RAN: • Proximity information is available at any resolution. • No indirection. • Fixing the search hop complexity. • Attribute-based discovery: • Directory services: LDAP, MDS. • Intentional naming scheme/Twine: • Document discovery for resource discovery. • RAN: • Trading off performance for scalability. • No indirection. Advanced Networking Research Laboratory, The School of Computer Science, McGill University, Montreal, QC, Canada.

  19. Conclusion • Expected contributions: • Architecture: • Extending the concept of structured-document discovery to resource discovery: • Extracting a structure out of the unstructured metric space using Hilbert curve. • First discovery structure combining attribute-based and location-based discovery. • Network positioning: • CLAP: resilient to network congestion. • SpringEq: providing low message complexity. • Spring vs. Simplex comparison. Advanced Networking Research Laboratory, The School of Computer Science, McGill University, Montreal, QC, Canada.

  20. Conclusion(cont…) • Expected contributions (cont…): • Location-based discovery: • Efficient overlay design using Hilbert indices. • Fixing the search complexity by fixing the search resolution. • Profile-based naming: • Trading off flexibility for scalability. • Efficient profile-based routing overlay design. • Profile-based search complexity depends on popularity distribution. Advanced Networking Research Laboratory, The School of Computer Science, McGill University, Montreal, QC, Canada.

  21. Conclusion (cont…) • Roadmap to completion: • LAP: • Analysis of SpringEq for its convergence and stability. (Sep. 2005) • Architecture: • The deficiencies the routing mechanism can face due to the non-uniformity of metric space will be studied. (Oct. 2005) • Location-based discovery: • A practical value for search resolution will be found based on errors in pings and the applications requirements. (Nov. 2005) • Simulation study. (Mar. 2006) • Profile-based discovery: • Analysis of other possible schemes that can map description onto profile space. (May 2006) • Impact of incorporating virtual profiles. (July 2006) Advanced Networking Research Laboratory, The School of Computer Science, McGill University, Montreal, QC, Canada.

  22. Thank you Advanced Networking Research Laboratory, The School of Computer Science, McGill University, Montreal, QC, Canada.

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