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Enhancing Grid Computing Through Socially-Inspired Resource Metrics

This paper focuses on introducing economic paradigms and social behavior into Grid Computing architectures to overcome challenges and improve resource management. The proposed solution involves Social Grid Agents and Grid4C framework for resource brokerage and control. The architecture includes social and production layers to enable efficient resource allocation and service evaluation. The system also addresses pricing mechanisms, arbitration issues, and trust management within Grid environments. The conclusion highlights the prototype's flexibility and potential integration with other systems and middlewares for enhanced Grid operations.

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Enhancing Grid Computing Through Socially-Inspired Resource Metrics

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  1. Enabling Social and Economic Behaviour based on Reliable Resource Metrics Gabriele Pierantoni, and Keith Rochford. Trinity College Dublin CGW 07 – Krakow Tuesday, October 16th 2007

  2. Motivation for the work. • Economic paradigms are increasingly used in Grid Computing architectures. • Economics is based on social behaviour, conventions and characteristics such as trust, arbitration, ownership, control and authority. • These first economic-based systems in Grid Computing face difficulties due to the lack of “social structure” in Grids.

  3. Proposed Solution • We propose to bring together two different agent-based systems to cope with some of these difficulties. • This architecture encompasses: • Social Grid Agents (SGA) • Grid4C

  4. Social Grid Agents • Social Grid Agents are a high level resource brokerage system. • Social Grid Agents are GT4 Grid Services • Social Grid Agents architecture is based on two layers: • Social layer: where agents engage in exchange and resource allocation. • Production layer: where agents implement the production steps decided by the social agents.

  5. Grid4C • Distributed Control Plane for Grid resources • Provides resource properties and management capabilities • Standards-based Web Services interface (WSDM) • Primary Aim • To develop a Command and Control system for Grid Infrastructures

  6. Grid4C - Architecture

  7. Social Grid Agents implement A high level brokerage systems based on social paradigms. They assess the value of a resource based on the metrics and social relationships. SGA Agent Grid4C agents provides performance-related metrics from the underlying infrastructure to the social layer. Grid4C Agent LCG2 Agent roles

  8. Usage scenarios • Trusted assessment of the value of resources and services • Arbitration.

  9. Value and Price • The decision of a price is a complex issue in economics. It depends on the pattern of interactions between the different actors and their social relationship. • Pricing mechanisms: • Auctions, • Posted Price, • etc.. • It is important that clients feel they are charged a “fair” price.

  10. Grid4C Agent PGA SGA Service LCG2 Added Value Price of the service p = f(v) Value of the executed Service. v = f(s, r) Service Metrics (s) Resource Consumption Metrics (r)

  11. Arbitration. • Arbitration issues can arise when a job fails. • The client may not be willing to pay for a failed job. • The resource owner may want to be paid in any case for the consumed resources. • An agent trusted by both can act as an arbitrator.

  12. Arbitrator SGA Agent Grid4C Agent Client Implicit Trust LCG2 Simple Trust chain. TRUST TRUST TRUST TRUST TRUST

  13. The arbitrator • Decides prices, • Resolves disputes.

  14. Conclusion, future work • First prototype of this architecture showed a good level of flexibility for the implementation of different social topologies enabling economic-based behaviour in Grid Computing. • Encompass other systems such as APEL and DGAS, • Encompass other middlewares such as GT4.

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