Incentive Mechanisms for Large Collaborative Resource Sharing

1. Objectives: Why Resource harnessing Examples of resource harnessing Grid computing P2P computing Resource sharing Assumptions Considerations What are incentives? Trust as a mechanism to provide incentives Incentive Mechanisms for Large Collaborative Resource Sharing

2. Resource Harnessing • Huge interest in linking up resources • Grid computing, P2P computing, computing utilities, etc. • It is all about sharing • Quality of Service • Security • Participation versus Cost

3. Resource Harnessing: Grid Example • Virtual Private Grids (PVG) is a framework for “renting” collection of resources • “Collection” is defined as follows: • able to deliver predefined performance metrics • performance delivered at predefined geographical locations • cost of provisioning is optimized or bounded

4. Grid Resource base Resource Harnessing: Grid Example VPGR GR GR GR multiplex Grid Domain Grid Resource Grid Resource Grid Resource

5. Resource Harnessing: Grid Example SO • SO (service originator) presents the VPG Spec. via a VPG Manager (VPGM) • VPGM negotiates with different Grids via a MetaGrid Resolver (MGR) • Grids (GRs) bid for the VPG creation requests • VPGM selects the best bid • Location spec • QoS specs • Cost preference VPGS VPGM Contract negotiation Admission Control MGR bid with (QoS/cost) VPG creation request Grid Engineering GR GR …… GR

6. Resource Sharing • Assumptions • Resource owners have committed their resources • Honestly • To be used efficiently • To be used for the overall good of the community • Considerations • Free riding • Malicious entities • Non cooperative entities Incentives are needed for resources to cooperate honestly

7. Resource Harnessing: P2P Example • Since, we deal with public resources, we need to address the following • How can we encourage resources to cooperate • 70% of all users do not share files • 50% of all requests are satisfied by the top 1% sharing hosts • How can we deal with security • We do not want security to become an overhead! • Can we use “trust” as an incentive?

8. Trust Considerations • How can we define “trust” in an operational way? Who will evaluate trust? • Trust maintenance can result in an efficient process especially in a very large-scale system. Hence, our task is to come up with an efficient model for maintaining trust • Techniques for managing and evolving trust in a large-scale distributed system • Mechanisms for maintaining trust from ongoing transactions

9. Overall Trust Model

10. Trust Terminology • Identity trust • Behavior trust • Honesty • Accuracy • Set of recommenders • Set of trusted allies

11. Trust Model Characteristics • To make the trust model efficient • the overall NC system is divided into NCDs • trust is a slow varying attribute • the number of contexts is limited to printing, storage, and computing

12. Why Behavior Trust

13. Notation • Let and represent recommenders set and trusted allies set, respectively • Let the honesty of recommender as observed by be denoted as • Let denote the recommendation for given by to at time for context • Let denote the recommendation for given by to where for the same and

14. Computing Honesty • Let • The value of will be less than a small value if recommender is honest • Therefore, is computed as

15. Computing Accuracy • Let denote the true trust level of obtained by as a results of monitoring the transaction • Let • The value of will be an integer value ranging from 0 to 4 • Therefore, is computed as

16. Computing Trust & Reputation • Before can use the recommendation given by to calculate the reputation of , needs to be adjusted to reflect the accuracy of recommender • This shift is given by

17. Computing Trust & Reputation • Trust relationship expressed as • Direct trust relationship and the reputation of expressed as and ,respectively. • The decay function is expressed as • Let and

18. Simulation Setup • A discrete event simulator was used • The transactions arrival process modeled using a Poisson random process • 30 NCDs were used in the simulation • The size of R is fixed and set to 4 • The size of T is fixed and set to 3 • The TL were randomly generated from [1-5]

19. Performance Measurement • The measure of performance used is the ability of the trust model to correctly predict the trust that exists between two NCDs • This is quantified by determining the success ratio as follows:

20. Performance Evaluation • Using accuracy & honesty measures: Success ratio with 150 transactions per relation

21. Performance Evaluation • Using the accuracy measure: Success ratio with 150 transactions per relation

22. Performance Evaluation • Using Accuracy & honesty measures: Success ratio progress

23. Case Study: Trust Modeling on P2P Grids • The P2P Grid is segmented into Grid domains (GDs) • Two virtual domains are associated with each GD • resource domain and client domain • Each resource domain has 3 attributes: • Ownership • Type of Activities (ToA) it supports • TL for each ToA • Similarly, each client domain has 3 attributes

24. Case Study: Trust Modeling on P2P Grids • Suppose that client from wanting to engage in activities and on resource at • Offered TL (OTL) = min(TL for , TL for ) • There are two required TLS (RTLs) • one from the client domain • one from the resource domain • Expected trust supplement (ETS) = RTL - OTL

25. Case Study: Trust Modeling on P2P Grids • An example of the ETS table

26. Case Study: Trust Modeling on P2P Grids • A batch mode mapping heuristic called “Sufferage heuristic” was used

27. Case Study: Trust Modeling on P2P Grids • Two different classes of Expected Execution Cost (EEC) were used: • Consistent Low task low machine (LOLO) heterogeneity • models networks that have “related” machines which are “similar” in performance • Inconsistent Low task low machine (LOLO) heterogeneity • models networks were machines are not related

28. Case Study: Performance Evaluation