Research Issues on Grid Resource Brokers

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    Research Issues on Grid Resource Brokers - Presentation Transcript

    1. Research Issues of Grid Resource Brokers
    2. Definition
      • A Grid broker is a tool used for matching Grid resources to users’ requests.
      • Many brokers include
        • Resource discovery
        • Resource selection
        • Resource monitoring
        • Job submission
        • Job monitoring
        • Historical storage
    3. General View
      • Related Entities
        • Resources (e.g., CPU, storage, applications, etc.)
        • Virtual Organization = interconnected Grid sites
        • Providers : offer resources to VOs
        • Consumers : utilize resources on VOs
      • A broker is a coordinator of all entities
      • A broker usually is able to run on a specific Grid middleware (e.g., GT2, GT3, UNICORE)
      • Users can access the broker by using command-line, Grid portal, and GUI
    4. Why we need a Grid broker
      • Grid middleware (e.g., GT) generally provide many services, but users do not have a single point of services to access resources
      • A broker (or brokerage service) is a common gateway to access Grid resources.
      • Brokers can give (best) resources to consumers by using a policy-based decision
        • Consumer-oriented policy
        • Provider-oriented policy
        • Grid-oriented policy (or VO-oriented policy)
        • Economy-driven policy
    5. Taxonomy of Grid Resource Broker [1]
      • Grid Middleware
        • Non-service based: GT2,GT3, NorduGrid/ARC, Unicore
        • Service-based: GT4, gLite
      • Job handling
        • Interface (API, Command-line, Services ,GUI)
        • Job Type (Sequetial/Parallel)
        • Job Description Languageg (RSL, AJO, JSDL)
        • QoS (Accounting/Billing, SLA, WS-Agreement, Fault Tolerance)
      • Scheduling
        • Information support (MDS2, MDS3, MDS4)
        • Scheduling architecture ( Decentralized / Hierarchical / Centralized )
        • Matchmakinig model (Static / Dynamic: Prediction-based)
        • Scheduling methods (Grid-Oriented, User/Provider oriented)
    6. Open Issues [1]
      • Mostly provided by many brokers
        • Grid-Middleware: GT (especially GT2 & GT3)
        • Job handling: command-line, RSL, rescheduling
        • Scheduling: central-scheduling
      • Rarely provided
        • Grid-Middleware: GT4+
        • Job handling: JSDL, API, advance reservation
        • Job Scheduling: economy/market broker, provider-oriented policy, checkpointing, job migration
      • New trends: Interoperable Broker and Meta-Broker
    7. Some Existing Grid Resource Brokers
      • JSS RB [4]: G4-based, interoperable , WSRF, JSDL, GLUE, WS-Agreement, Advance reservation, benchmark-based scheduling
      • GGB [7]: GT3-based, P2P-based resource discovery , SOA-based resources, scheduling (random and round-robin)
      • HPC RB [6]: GT2-based, centralized topology, MPI-based oriented applications
      • GRIP [9]: UNICORE -based (able to define VO boundaries), Ontology engine (via AJO)
      • eNANOS [2]: GT3-based, dynamic policy management, fault tolerance ( reschedule and checkpointing )
      • GRUBER [3]: GT3-based, SLA policies , scheduling (round-robin, least used, last recently used)
      • Nimrod/G [5]: economy-driven , interoperable , parameter sweep application oriented
    8. My Interesting “Economic Grid Brokers”
      • Propose a Grid economic broker framework
        • Provider-side services/tools
          • Specifying providers’ SLA-based policies [3]
          • Publishing resources to VOs, Monitoring, Accounting, Billing, Trading
          • Decision-support system to define cost of resources based on balancing between supply and demand quantity
        • Customers-side services/tools
          • Resource discovery by expressing requirements
          • Job submission (e.g., using WS-GRAM) and job monitoring
          • Advance reservation [4]
          • Accounting, Billing, Trading (e.g., payment and bidding)
          • Decision-support system to choose a VO and/or providers
        • VO-side services/tools (managed by Grid committees)
          • Specifying VOs’ SLA-based policies [3]
          • Specifying VOs’ boundaries (or VO formation)
          • Monitoring resources located on a VO or VOs
          • Defining a market for trading resources based an economic model [5]
        • Virtual Bank (managed by Grid committees)
    9. My Interesting “Economic Grid Brokers”
      • Propose prediction-based and economic-based scheduling algorithms
        • Predict budget and time to execute a job
        • Predict which resources are appropriate to users’ jobs
        • Tradeoff among fairness, providers’ policies, consumers’ policies, budget, overall Grid utilization, and job deadline
      • Choose a type of brokers
        • Interoperable-broker [4] / Meta-broker [8] / both
      • Fault tolerance issues: rescheduling and checkpointing
      • Apply standards&services [4](e.g, JSDL, WS-Agreement, WSRF, GLUE, WS-GRAM)
      • Leverage accounting/billing modules (e.g., MOGAS, SCMS, SGAS)
        • Generic usage: monitoring, billing, and reporting
        • Historical data for decision-support-systems / predictions [1, 2, 4]
      • Plug-in and API-support for user-defined applications (e.g., Grid portal), system customization, and adaptability of various middleware/brokers
      • Introduce a P2P computing environment to extend the Grid community
    10. References
      • Attila Kertész and Péter Kacsuk, A Taxonomy of Grid resource Brokers , in Distributed and Parallel Systems From Cluster to Grid Computing, Springer US, 2007
      • Ivan Rodero, Julita Corbalán, Rosa M. Badia, Jesús Labarta, eNANOS Grid Resource Broker , in European Grid Conference (EGC2005), Springer Berlin / Heidelberg, 2005
      • Catalin L. Dumitrescu and Ian Foster, GRUBER: A Grid Resource Usage SLA Broker , in Euro-Par 2005, Spring Berlin Heidelberg, 2005
      • Eric Elmroth and Johan Tordsson, An Interoperable, Standards-based Grid Resource Broker and Job Submission Service , in Proceedings of the First International Conference on e-Science and Grid Computing (e-Science’05), 2005
      • David Abramson, Rajkumar Buyya, and Jonathan Giddy, A Computational Economy for Grid Computing and its Implementation in the Nimrod-G Resource Broker , in Future Generation Computer Systems (FGCS) Journal, 2002
    11. References
      • Chao-Tung Yang, Po-Chi Shih and Kuan-Ching Li, A High-Performance Computational Resource Broker for Grid Computing Environments , in the Proceedings of the 19th International Conference on Advanced Information Networking and Application, 2005
      • Sonchaya Rojanavipat, Sivadon Chaisiri, and Putchong Uthayopas, A Development to Grid Resource Management using Grid Broker , in the 10th Annual National Symposium on Computational Science and Engineering, Thailand, 200 6
      • Attila Kert´esz and P´eter Kacsuk, META-BROKER for Future Generation Grids: A New Approach for a High-Level Interoperable Resource Management , Grid Middleware and Services Challenges and Solutions, Springer US, 2008
      • John M. Brooke and Donal K. Fellows, An Architecture for Distributed Grid Brokering , Euro-Par2005, Springer Berlin Heidelberg, 2005

    + Sivadon ChaisiriSivadon Chaisiri, 2 years ago

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