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Holding slide prior to starting show. C onstraint O riented N egotiation in O pen I nformation S eeking E nvironments for the G rid (CONOISE-G) Project. - V.Deora, W.A. Gray, J.Shao, G. Shercliff, P. J. Stockreisser -. Introduction. Virtual Organisations Lifecycle

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  1. Holding slide prior to starting show

  2. Constraint Oriented Negotiation in Open Information Seeking Environmentsfor the Grid (CONOISE-G) Project - V.Deora, W.A. Gray, J.Shao, G. Shercliff, P. J. Stockreisser -

  3. Introduction • Virtual Organisations • Lifecycle • Expectation Based QoS • Moving Conoise to the grid • Future Work

  4. Conoise Aims and Objectives • To develop models and techniques that will support the entire VO lifecycle • Realising this vision requires the development of: • Autonomous agents to represent the different problem solving entities. • Sophisticated interaction models that enable autonomous agents to form and interact within groups • Rich knowledge representation and information inter-change mechanisms

  5. Group Members Aberdeen Manage agent commitments and provide an intelligent decision making strategy Policing and governing of Social Laws Cardiff Service Discovery and Quality Assessment Quality Policy & Service Discovery Southampton Negotiation and Coalition Formation Trust and Reputation

  6. Virtual Organisations (VO) • Consist of semi-independent autonomous entities. • Each entity has • A range of problem solving capabilities • And a range of resources. Why create a VO? • Because the virtual organisation is greater than its parts and there is mutual benefit for all participants.

  7. Entity Behaviour • The entities co-exist but… • Can compete against one another in a virtual market place • Can form VOs to exploit a gap in the market • And attract custom through advertising the cost and quality of it’s services

  8. VO Sequence

  9. VO and Services Coalition – Forming a partnership with a competitor New Service – Forming a partnership with an entity with complementary expertise

  10. VO vs Entity The collection of independent entities will act as a single conceptual unit in the context of the proposed service (Co-operating and Co-ordinating Activities) Each entity retains its individual identity outside of this context. It may break a particular partnership if it decides it is in it’s own best interest.

  11. Service Provider (SP) Decisions Given a request for a service an SP will have several choices. Several factors may influence the decision making process Time Constraints Prior Commitments Availability of others

  12. FORMATION OPERATION DISSOLUTION RESTRUCTURING - Elimination - Expansion Conoise VO Lifecycle

  13. Formation • An entity attempts to form a VO to meet user requirements (Requester Agent) • Identifies current services (Yellow Pages Agent) • Issues a call for proposals • Needs to decide which proposals to accept to form VO (QoS Agent) Problems… • An RA may wish to break an existing commitment in order to take part in a more lucrative contract. • When is it most profitable for an agent to initiate a VO? • How will RA deal with such decisions?

  14. Operation/Restructure • Needs to meet contractual Agreements • Will have to adapt to market changes • New Service Provider • Decreased Utility

  15. Dissolution • Why would a VO need to disband? • Why is the organisation not formalised? • To survive in a rapidly changing market environment (Trends, Competition etc…) • Cost of formalisation • Limitations imposed by alliance may be too restrictive to development • Mutual benefit may only be available once

  16. A Example Scenario Package required by customer: • Movie subscription. • News service. • >50 free text messages per month. • 30 free phone minutes per month.

  17. Conoise Architecture • Built on JADE Agent Platform using java • Agents communication in ACL

  18. QoS – What Is It? • As Functionality? If one service offers more functionality than others, then it is considered to offer a better quality • As Conformance? If a service honours its “claims”, it is considered to offer good quality • As Reputation? Users’ perception of a service’s consistency over time

  19. A2 QoS Attributes A1 An …. Service S QoS as Conformance • QoS(Ai) = f (Aia, Aid) A measure of difference between delivered (Aid) and advertised (Aia) qualities. • QoS(S) = Σ wiQoS(Ai) A weighted average of individual qualities

  20. Update Frequency Availability News to PDA An Example • QoS(fr) = f (fra=24, frd=22) = 22/24 = 0.92 • QoS(av) = f (ava=7, avd=7) = 7/7 = 1.00 • QoS(news) = 0.8QoS(fr) + 0.2QoS(av) = 0.94

  21. How are they derived? A Real-World Example

  22. 1 out of 5 A Real-World Example (cont’d) Rating on a hotel room by Mr Fussy

  23. Rating on a hotel room by Mr Easy 5 out of 5 A Real-World Example (cont’d)

  24. Observations • What do the ratings mean? • Current approaches do not differentiate individual users’ expectations on QoS • Need a more user-centric QoS model

  25. QoS(Ai) = <Ru(Ai), Eu(Ai), Pu(Ai)> User rated quality of Ai User expected quality on Ai User perceived quality on Ai An Expectation Based Model QoS(Ai) = f (Aia, Aid)

  26. An Example • Suppose that we have 3 providers (SP1, SP2 and SP3) who offer news services to PDA • We wish to establish their qualities in terms of offering 24 updates per day (Frequency) • Assume that we have had 6 users (U1, U2, U3, U4, U5 and U6) who have used the services • We wish to determine which SP is the best

  27. Conventional Approach • Collect ratings only • SP2 is the best

  28. Our Approach • Collect expectation, perception and ratings

  29. Our Approach • Collect expectation, perception and ratings • You must give your expectation!

  30. If Expectation is 0.8 … • Assume range = [E-0.1, E+0.1] • SP1 is the best - QoS(SP1) = 0.43

  31. If Expectation is 0.5 … • Assume range = [E-0.1, E+0.1] • SP3 is the best - QoS(SP3) = 1.0

  32. If Expectation is unspecified • Our approach falls back a conventional one • SP2 is the best - QoS(SP2) = 0.67

  33. Ratings DB E P R E request for ratings E P R E E result ratings matching The QA’s Architecture Service Monitoring Service Agreement + Perception Rating Expectation The QA QoS Collector QoS Calculator

  34. Summary of Our Approach • Main Features • Attempt to calculate QoS in context • Dynamically aggregate QoS ratings on a case-by-case basis • Related Work • Rating based QoS measurement • QoS calculation in marketing research • Collaborative filtering • QoS taxonomies

  35. Conoise-G Proposal • Trust & Reputation • In an untrustworthy environment how reliable are the sources from which we obtain information and services? • Policing • How do we detect when a contract is broken and what reaction do we take? • Quality • Align Conoise services with a Grid-enabled environment • Determine how ontology and resource discovery mechanisms can employ services offered within the Grid • Determine required Grid structure to support Conoise work

  36. Conoise as a Grid • Virtual Organisations present in grid • Difference is in implementation rather than concept • Research areas within conoise/conoise-G contribute to grid research

  37. Possible Implementations • Grid enable Agent Platform • Agent Platform itself would make use of grid services • Requires no reimplementation of agents

  38. Possible Implementations • reimplement agents as grid services • long development period • requires additional support services

  39. Possible Implementations • Provide grid-interface for core agents • Can make use of grid services • Possibly use bridge to allow external grid services to interact with conoise agents

  40. Possible Implementations • Allows phased implementation • Both core agents and service providers can make use of grid services • Eventually remove agent platform

  41. Problem Issues • Progression of grid technology • Proposal written 3 years ago • Integration of Agents and Grid • How will agents interact with external grid services and software? • How can we utilise evolving grid standards for core functions such as security and resource discovery?

  42. Future Work @Cardiff • Continue research into QoS assessment • QoS attribute aggregation • QoS of composite services • Monitoring of QoS degradation

  43. 0.6 ~0.5 QoS Attribute Aggregation Avail=0.8 • Current model values each quality metric as equal Reliab=0.6 • Simply averaging the QoS components does not present an accurate QoS value for the service Accur=0.4 FrmRt=0.3 Reliab=0.3 • Need a model which produces a more meaningful value Accur=1.0

  44. A B C QoS of Composite Services • Consider a composite service A, composed of services B and C QoS=?? • Can we use the QoS attributes we already have for B and C to meaningfully assess the QoS of A QoS=0.3 QoS=0.9 • Current model assumes 100% confidence in QoS values • Current model assumes all QoS attributes are present

  45. QoS Degradation • Service Providers may actually be composite services • How do we determine at which point in the chain a QoS degradation is taking place? • Based on observation can we predict a QoS degradation before it occurs?

  46. Conclusions • Achievements of conoise • CLP for decision making • Negotiation for VO formation • Expectation Based QoS assessment • Future Work • VO operation and disbandment • Trust & Policing • Grid implementation

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