Research Issues on Resource Discovery & Matching Making
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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

Some issues of resource discovery & match making for distrbuted systems

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