1. Holding slide prior to starting show

2. Constraint Oriented Negotiation in Open Information Seeking Environments for 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. Conoise VO Lifecycle FORMATIONOPERATIONDISSOLUTION RESTRUCTURING - Elimination - Expansion

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. SPMovies (pcm) News (daily) Text (#free) Phone (#free) SP11024 SP272 SP312030mins SP4530mins

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. QoS as Conformance QoS(A i ) = f (A i a, A i d ) A measure of difference between delivered (A i d ) and advertised (A i a ) qualities. QoS(S) = Σ w i  QoS(A i ) A weighted average of individual qualities S A1A1 A2A2 AnAn …. Service QoS Attributes

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

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

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

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

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. An Expectation Based Model QoS(A i ) = f (A i a, A i d ) User rated quality of A i User perceived quality on A i QoS(A i ) = User expected quality on A i

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 Users SP1 Frequency SP2 Frequency SP3 Frequency U10.3 U20.80.9 U30.31.0 U40.8 U50.50.1 U60.60.3 Aggregate rating 0.500.670.47  Collect ratings only  SP2 is the best

28. Our Approach UsersSP1 SP2 SP3 U1 U2 U3 U4 U5 U6  Collect expectation, perception and ratings

29. Our Approach UsersSP1 SP2 SP3 U1 U2 U3 U4 U5 U6  Collect expectation, perception and ratings  You must give your expectation!

30. If Expectation is 0.8 … UsersSP1 SP2 SP3 U1 U2 U3 U4 U5 U6  Assume range = [E-0.1, E+0.1]  SP1 is the best - QoS(SP1) = 0.43

31. If Expectation is 0.5 … UsersSP1 SP2 SP3 U1 U2 U3 U4 U5 U6  Assume range = [E-0.1, E+0.1]  SP3 is the best - QoS(SP3) = 1.0

32. If Expectation is unspecified UsersSP1 SP2 SP3 U1 U2 U3 U4 U5 U6  Our approach falls back a conventional one  SP2 is the best - QoS(SP2) = 0.67

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

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. Other Issues 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?

37. 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

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

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

40. 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

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

42. 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?

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

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

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

46. 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? QoS Degradation

47. 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