Resource Discovery & Matchmaking
Main Classes of Resource Discovery  [1] <ul><li>Centralized third party </li></ul><ul><ul><li>Single server provides info ...
Convergence of P2P + Grid   [2] <ul><li>Grid  </li></ul><ul><ul><li>Nature: complexity (variety of resources + application...
Two Main Classes of P2P <ul><li>Unstructured P2P </li></ul><ul><ul><li>Flooding / forwarding queries </li></ul></ul><ul><u...
Iamnichi and Foster ‘s  [2] <ul><li>Architectural Components:  </li></ul><ul><ul><li>Membership protocol </li></ul></ul><u...
Sivadon and Putchong ’s   [3] <ul><li>Unstructured Hierarchical P2P </li></ul><ul><ul><li>Form top-most  to lowest: Global...
Condor’s Matchmaking  [4]   <ul><li>Condor’s Matchmaking </li></ul><ul><ul><li>Most distributed systems are basing on name...
References <ul><li>K. Vanthournout, G. Deconinck, and R. Belmans, “A Taxonomy for Resource Discovery, Personal and Ubiquit...
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Research Issues on Resource Discovery & Matching Making

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Some issues of resource discovery & match making for distrbuted systems

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Research Issues on Resource Discovery & Matching Making

  1. 1. Resource Discovery & Matchmaking
  2. 2. Main Classes of Resource Discovery [1] <ul><li>Centralized third party </li></ul><ul><ul><li>Single server provides info of resources </li></ul></ul><ul><ul><li>Example: DNS, LDAP, Napster, Globus </li></ul></ul><ul><li>Distributed third party </li></ul><ul><ul><li>Replication of multiple servers to provide info </li></ul></ul><ul><ul><li>Example: UDDI, NetSolve, CORBA </li></ul></ul><ul><li>Multicast </li></ul><ul><ul><li>Limitation of LAN boundaries </li></ul></ul><ul><ul><li>Example: Jini, Salutation, UPnP, Ninja </li></ul></ul><ul><li>P2P </li></ul><ul><ul><li>Genuinely distributed system </li></ul></ul><ul><ul><li>Example: Chord, CAN, Gnutella, Pastry, Tapestry </li></ul></ul>
  3. 3. Convergence of P2P + Grid [2] <ul><li>Grid </li></ul><ul><ul><li>Nature: complexity (variety of resources + applications), administrative management + policy-based </li></ul></ul><ul><ul><li>Requirement: scalability, intermittent participations </li></ul></ul><ul><li>P2P </li></ul><ul><ul><li>Nature: specific applications (either file sharing or CPU cycles), anonymity, intermittent participants </li></ul></ul><ul><ul><li>Requirement: complexity and attributed-based search </li></ul></ul><ul><li>Grid + P2P </li></ul><ul><ul><li>Massively scalable sharing environment </li></ul></ul>
  4. 4. Two Main Classes of P2P <ul><li>Unstructured P2P </li></ul><ul><ul><li>Flooding / forwarding queries </li></ul></ul><ul><ul><li>Example: Gnutella, Freenet, BitTorrent </li></ul></ul><ul><ul><li>Problem: Nondeterministic search, N/W traffic </li></ul></ul><ul><li>Structured P2P </li></ul><ul><ul><li>Distributed Hash Table (DHT) </li></ul></ul><ul><ul><li>Example: Chord, CAN, Tapestry, Pastry, P-Grid </li></ul></ul><ul><ul><li>Problem: must be exact-key search, complex algo </li></ul></ul>
  5. 5. Iamnichi and Foster ‘s [2] <ul><li>Architectural Components: </li></ul><ul><ul><li>Membership protocol </li></ul></ul><ul><ul><li>Overlay Construction </li></ul></ul><ul><ul><li>Preprocessing </li></ul></ul><ul><ul><li>Request Processing </li></ul></ul><ul><li>Emulated Grid: unstructured P2P of 32,768 virtual nodes </li></ul><ul><li>Evaluated Resource Discovery Algorithms </li></ul><ul><ul><li>Random Walk: worst response but no cache </li></ul></ul><ul><ul><li>Learning-based: best but require cache </li></ul></ul><ul><ul><li>Best-neighbor: good for many distinct requests </li></ul></ul><ul><ul><li>Learning-based + best-neighbor: unpredictable </li></ul></ul>
  6. 6. Sivadon and Putchong ’s [3] <ul><li>Unstructured Hierarchical P2P </li></ul><ul><ul><li>Form top-most to lowest: Global, VO, Super, Edge </li></ul></ul><ul><li>Flooding-based query algorithms </li></ul><ul><ul><li>Query-filter </li></ul></ul><ul><ul><li>Backing links </li></ul></ul><ul><ul><li>Backing resource links </li></ul></ul><ul><li>Simulation (implemented by Sugree’ Hypersim) </li></ul><ul><ul><li>100,000 peers located in 8 VOs </li></ul></ul><ul><ul><li>Evaluated algorithms by determining swamping problem + response time </li></ul></ul>
  7. 7. Condor’s Matchmaking [4] <ul><li>Condor’s Matchmaking </li></ul><ul><ul><li>Most distributed systems are basing on name/keyword search </li></ul></ul><ul><ul><li>Matchmaking’s idea: advertise resources via classads </li></ul></ul><ul><ul><li>Classads = advertisement + query </li></ul></ul><ul><li>Two Phases of Matchmaking </li></ul><ul><ul><li>Matching </li></ul></ul><ul><ul><ul><li>“ rank” = preference </li></ul></ul></ul><ul><ul><ul><li>“ constraint” = requirement </li></ul></ul></ul><ul><ul><li>Claiming </li></ul></ul><ul><ul><ul><li>Try each one in the list of matching resources </li></ul></ul></ul><ul><ul><ul><li>e.g., security issue + support different allocation models </li></ul></ul></ul>
  8. 8. References <ul><li>K. Vanthournout, G. Deconinck, and R. Belmans, “A Taxonomy for Resource Discovery, Personal and Ubiquitous Computing”, Springer Verlag London, UK, Volume 9, Issue 2, 2005 </li></ul><ul><li>A. Iamnitchi and I. Foster, “A Peer-to-Peer Approach to Resource Location in Grid Environments”, Grid Resource Management, Kluwer Publishing, 2003 </li></ul><ul><li>S. Chaisiri and P. Uthayopas, “Performance Evaluation of Peer-to-Peer Approach to Resource Discovery for Large Scale Grid Environments”, Proceedings of The 8th Annual National Symposium on Computational Science and Engineering, Thailand, 2004 </li></ul><ul><li>R. Raman and M. Livny, “Matchmaking: Distributed Resource Management for High Throughput Computing”, Proceedings of the 17th IEEE International Symposium on High Performance Distributed Computing, 1998 </li></ul>

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