Trust and Grid Computing Systems

1. Trust and Grid Computing Systems Presented By: Woodas Lai

2. Agenda • What is Grid? • What is Trust? • Our Trust Model • Future Work

3. What is Grid? • Two facts: • Advanced Technologies lead to the large, complex and resource-intensive applications • Moore’s Law: power of network, storage, and computing resources is projected to double every 9, 12, and 18 months, respectively  Network performance outperforms CPU performance

4. What is Grid? • Conclusion: Difficult to gather enough computational resources for running applications at a single location How to overcome?

5. What is Grid? • Grid is a technology which brings together a set of resources distributed over wide-area networks that can support large-scale distributed applications • Grid coordinates resource sharing and problem solving in dynamic, multi-institutional, virtual organizations

6. Gird Example

7. Grid Computing • Each company could be regarded as a domain • Each domain will have its own security policy • The primary goal of Grid environment is to encourage domain-to-domain interactions to share the resources

8. How to share the resources? • To encourage the controlled sharing of resources: • The security overhead should be minimized so that the sharing is appealing • The security mechanism applied should be scalable • Domains should not lose control over their own resources This is where our trust model comes and is applied

9. What is Trust • Trust is to model the human social behavior • When I use a credit card to pay the bill, the bank trust me that I will pay back the money later • When I use the e-banking service to perform a transaction, I trust the bank that it will perform the transaction for me

10. Definition of Trust • Trust is the firm belief in the competence of an entity to behave as expected such that this firm belief is a dynamic value associated with the entity and is subject to the entity’s behavior and applies only within a specific context at a given time

11. Trust • Trust value is a continuous and dynamic value in the range of [0,1] • 1 means very trustworthy • 0 means very untrustworthy • It is built on past experience • It is context based (under different context may have different trust value)

12. Reputation • When making trust-based decisions, entities can rely on others for information regarding to a specific entity. • The information regarding to a specific entity x is defined as the reputation of entity x.

13. Definition of Reputation • The reputation of an entity is an expectation of its behavior based on other entities’ observations or information about the entity’s past behavior within a specific context at a given time.

14. Evaluating Trust and Reputation • Trusts decays with time • Entities may form alliances and they may trust their allies and business partners more than others • Trust value is based on the combination of direct trust and reputation

15. Evaluating Trust and Reputation • Let Di and Dj be two domains of entities • The trust relationship based on a specific context c at a given time t is T(Di,Dj,t,c) • Let the direct trust relationship for the context c at time t be dT(Di,Dj,t,c) • Let the reputation of Dj for the context c at time t be R(Dj,t,c)

16. Evaluating Trust and Reputation T(Di,Dj,t,c) =  x dT(Di,Dj,t,c) +  x R(Dj,t,c) where  and  are the weights given to direct and reputation relationships respectively

17. Evaluating Trust and Reputation Direct trust relationship is computed as a product of the trust level in the direct trust table (DTT) and the decay function (t-tij,c) where c is the specific context t is the current time tij is the time of the last update or the last transaction between Di and Dj

18. Evaluating Trust and Reputation • dT(Di,Dj,t,c) = DTT(Di,Dj,c) x (t-tij,c)

19. Evaluating Trust and Reputation • The reputation of Dj is computed as the average of the product of the trust level in the reputation trust table (RTT), the decay function ((t-tkj,c)), and the recommender trust factor (r(Dk,Dj)) for all domains k.

20. Recommender trust factor • It is used to prevent cheating via collusions among a group of domains • It is a value between 0 and 1 • Higher value if Dk and Dj are unknown or have no prior relationship • Lower value if Dk and Dj are allies or business partner

21. Evaluating Trust and Reputation • R(Dj,t,c) =  RTT(Dk,Dj,c) x r(Dk,Dj) x (t-tkj,c)  Dk

22. Trust Model • Each Domain will maintain its own Direct Trust Table (DTT) and Reputation Trust Table (RTT).

23. Context Domains D1 D2 …… Dj C1 Trust Value Trust Value …… Trust Value …… …… …… …… …… Ci Trust Value Trust Value …… Trust Value Trust Model Direct Trust Table maintained By Dk

24. Trust Model In Grid Computing, there is always a chain of service calls Time duration for this service invocation = t4-t1

25. Trust Model • We define another time decay function: (texpected-tduration,c) • Where texpected is the expected time duration for this service call • tduration is the actual time duration for this service call • C is the context

26. Trust Model • Our Direct Trust Relationship will be modified as follows: • dT(Di,Dj,t,c) = DTT(Di,Dj,c) x (t-tij,c) x (texpected-tduration,c)

27. Updating Direct Trust Table • Our formula is: DTT(Di,Dj,c) = (1-)x DTT(Di,Dj,c) +  x Tv(tij,c) where Tv(tij,c) is the trust value for context c resulted from the direct trust relationship between Di and Dj  is between 0 and 1. If  > 0.5, more preference will be given to current direct trust value

28. Required Trust Value • The required trust value is defined as a value between 0 and 1, such that if T(Di,Dj,t,c) >= RTv, the interaction is trusted and the request is granted if T(Di,Dj,t,c) < RTv, the interaction is not trusted and enhance security mechanism is enforced (authentication using X.509 certificate)

29. Initial Trust Value • Itv is define as the initial trust value. • At the very beginning, Di and Dj may not know each other. • Dj will then send the X.509 certificate to Di so as to verify the identity, if the verification is successful, Dj will be assigned the trust value of Itv and then the transaction starts. • After the transaction, some trust metrics like last transaction time and duration time will be updated. • After that, our trust model will continue to evolve as described before.

30. Future Work • Simulation or Experiments should be done in order to test our trust model. • In this model, the behavior of the entity is not monitored. (Like the entity consumes more resources than requested or reading some memory out of the allocated boundary). Intrusion Detection Systems (IDSs) may be studied so as to address this behavioral issue.

31. Thanks for your attention Q&A