A Framework for Trust Management System in Computational Grids

1. By Grid Lab, Dept of I.T, Madras Institute of Technology Anna University Chennai A Framework for Trust Management System in Computational Grids

2. What we cover…. • Motivation • Trust Management System – Lifecycle & Metrics • Trust Based Scheduler • Trusted Grid Architecture • Experimental Results .. • Conclusion

3. Objectives • To define a trust management system with its life cycle to evaluate trustworthiness of Grid Resource Providers. • To develop trust resource broker that discovers suitable and trusted grid resource for reliable, accurate and in time successful job execution • To propose a standard architecture that enables Trust Based Scheduling in Grid Motivation • Grid is a dynamic collections of huge number of resources spanning multiple administrative domains, distributed across the globe to solve a computationally intensive problem. • It involves Resources and Information sharing with unknown parties that pose a great challenge in ensuring trustworthiness of resource providers • Current grid security mechanism lacks the ability to determine how “trustworthy” a resource provider is.

4. We define Trust… The degree of belief in the resource provider’s competence to complete user’s task dependably, securely and reliably in a specific context at a given time Agent / Resource Broker users Resources

5. Describes relying party’s trust in a service provider. The trustor trusts the trustee to provide a service that does not involve access to the trustor’s resource • A trustor trusts a trustee to use resources that he owns or controls. • It measures whether a resource provided by the resource provider is trustworthy. • It is the belief that information provided by the Information provider is reliable and accurate. • It is a measure of belief that a resource broker has discovered a trustworthy resource • Measures whether a resource provider is willing to offer his services to the user. • The previous behaviour / payment record may be considered for this trust Types of trust

6. Modify or update the value of trust periodically for each resource provider • Identify suitable parameters with which the respective trust can be defined Trust Integration Trust Metric Identification • Integrate the trust mechanism in the resource broker / Scheduler to find out the most trustworthy resource provider for successful job execution/task completion • Apply suitable methodology to determine the value of those metrics TMS Trust Value Updation Trust Metric Evaluation Trust Value Calculation • Determine the overall trust value using the values for various trust metrics obtained Trust Management Life Cycle

7. In Grid environment, where resources from diverse organizations are shared, the real challenge is determining the trustworthiness of the resource providers. Equipment Provision Trust Emphasis is on EQUIPMENT PROVISION TRUST for Computationally intensive problems to be solved. Our Focus is on Equipment Provision trust

8. Trust Management System for Equipment Provision Trust Estimates Trustworthiness of all Grid Resource Providers Periodically updates the trust value The trust calculation is based on Resource performance Metrics User feedback Metrics Resource Registration Metrics The Trust Management System integrated with a Grid Metascheduler acts as Grid Resource Broker

9. Resource Registration Metrics Resource Performance Metrics Equipment Provision Trust User Feedback Metrics Dependency Metrics These metrics reflect the throughput of the resources and their QoS Government / Private, Registration Number Affordability, Bandwidth, Success, Failure These metrics reflect the infrastructure of the organization. It is used to identify initial trust value of the resource provider These metrics reflect reputation of the resource in the user community Reputation through feedback

10. Parameters… In our context….

11. 1 How to obtain those parameters ? Issues 2 How to calculate overall trust ? Issues 3 How to integrate trust with metascheduler ?

12. 2100 2100 2100 2100 2100 2100 2100 2100 Tools to determine parameters - Success - Failure (Obviously) Gridway Metascheduler - Affordability - Bandwidth Local Scheduler & NWS Network Monitoring Tools (NMT)

13. Trust per Job Execution and Overall Trust

14. Integration with Gridway To propose a trust based scheduling mechanism

15. Position of Gridway !! Gridway • A metaschedulerthat uses Globus as core middleware. Performs • Resource Discovery • Job scheduling • Job submission • Job Execution Monitoring With… • Transparent Resource access • Adapting to dynamism of grid environment Gridway Metascheduler Globus core Middleware Users PBS cluster SGE cluster Condor cluster

16. Components of Gridway.. Information Manager Transfer Manager MAD Execution Manager MAD MAD User It receives resource request for executing the job Gridway Core Request Manager Responsible for job scheduling and initiates resource discovery Dispatch Manager Responsible for resource discovery and monitoring Scheduler Responsible for job execution MDS2 Grid Information services MDS4 Middleware Access Drivers Pre-WS GRAM WS- GRAM gFTP RFT Responsible for data transfer between the resources and staging of files Grid Execution services Grid File Transfer Services

17. Conventional Gridway Flow Trust Enabled Gridway Flow <job template> <job template> Job Submit Job Submit Invokes Scheduling Operation Invokes Scheduling Operation Gathers Available Resource Gathers Available Resource Selects Most Trusted Resource Performs Matchmaking Performs Matchmaking Trust DB Matches Against JobReq Invokes TMS TMS Matches Against JobReq Selects and submit Selects and submit R2 R1 R3 R2 R1 R3

18. Gridway Configuration File Trust Enabled Gridway Configuration File gwd.conf gwd.conf ---- ---- GWD_PORT = 6725 MAX_NUMBER_OF_CLIENTS = 20 NUMBER_OF_ARRAYS = 200 NUMBER_OF_JOBS = 5000 NUMBER_OF_HOSTS = 100 NUMBER_OF_USERS = 30 JOBS_PER_SCHED = 15 JOBS_PER_HOST = 10 JOBS_PER_USER = 30 ---- ---- ---- ---- GWD_PORT = 6725 MAX_NUMBER_OF_CLIENTS = 20 NUMBER_OF_ARRAYS = 200 NUMBER_OF_JOBS = 5000 NUMBER_OF_HOSTS = 100 NUMBER_OF_USERS = 30 # Trust_value=1 for the trust based resource selection # Trust_value=0 for the normal Gridway resource selection TRUST_VALUE = 1 JOBS_PER_SCHED = 15 JOBS_PER_HOST = 10 JOBS_PER_USER = 30 ---- --- -

19. Reaching the destination … Where do we evolve the architecture ? Integrating Trust Management System with gridway metascheduler will act as a Resource Broker that select grid resource based on its trust value With this resource broker, we hereby proposing a four layered grid architecture that facilitates grid resource discovery and selection of most trusted grid resource for job execution

20. Layered Architecture of Trust Resource Broker for Equipment Provision Trust Receives feedback from the user and resource registration information from the resource provider User Feedback Grid Resource Registration Application Portlets Application Portlets Application Layer Application Portlets Monitors Trust metrics, evaluates trust and makes decision based on the trust and facilitates job execution Trust Broker Data base Trust Management System Trust Layer Gridway Metascheduler Constitutes grid middleware, provides grid resource information to trust layer, and take care grid resource authentication NMT MDS GRAM GFTP/RFTP Grid Middleware Refers to the underlying grid resources where actual job execution takes place. They may use local job manager for monitoring job execution GSI Resources Grid Fabrics

21. Experimental Setup Trust Based Metascheduler g09.grid MITCluster 60 Nodes Connected with Garuda Resources VOCluster 15 Nodes RockCluster 10 Nodes

22. Results Most trustworthy resource will get more jobs for scheduling , i.e., a good shop will have huge crowd

23. Results The trust value of a resource that shows gradual decrease in the affordability

24. Portal to submit job

25. Portal displaying output

26. Portal to know job status

27. Portal to submit feedback after job execution

28. Conclusion • The trust management system integrated with gridway metascheduler enables discovery of a suitable resource that has the highest trust value • Executing job in a trusted resource facilitates satisfactory usage of grid resources with increased reliability and accuracy

29. References… • [Abr95] M.D. Abrams, M.V. Joyce. Trusted Computing Update. Computers and Security, 14(1): 57-68. 1995. • [Boe03] S. Boeyen et al. Liberty Trust Models Guidelines. In J. Linn (editor), Liberty Alliance Project. Liberty Alliance, draft version 1.0, 2003. • [Buy04] S. Venugopal, R. Buyya and L. Winton, “A Grid Service Broker for Scheduling Distributed Data-Oriented Applications on Global Grids”, Proceedings of the 2nd International Workshop on Middleware for Grid Computing (Co-located with Middleware 2004, Toronto, Canada, October 18, 2004), ACM Press, 2004, USA • [Cas98] C. Castelfranchi, R. Falcone. Principles of Trust for MAS: Cognitive Anatomy, Social Importance, and Quantification. In Y. Demazeau (editor), Proceedings of the Third International Conference on Multi-Agent Systems. IEEE C.S., Los Alamitos, 1998. • [Kin98] A. Kini, J. Choobineh. Trust in Electronic Commerce: Definition and Theoretical Consideration. Proceedings of 31st International Conference on System Sciences, IEEE, 1998. • [Gra00] T. Grandison, M. Sloman. A Survey of Trust in Internet Applications. IEEE Communications Survey and Tutorials, 3, 2000. • [Dim01] T. Dimitrakos. System Models, e-Risk and e-Trust. Towards Bridging the Gap? in Towards the ESociety: E-Business, E-Commerce, and E-Government, eds. B. Schmid, K. Stanoevska-Slabeva, V. Tschammer. Kluwer Academic Publishers, 2001.

30. References… • [Jos05] A. Josang, R. Ismail, C. Boyd. A Survey of Trust and Reputation Systems for Online Service Provision. Decision Support Systems, 2005. • [Chi04] Ching L., Vijay V. and Yan W. Vineet P., “Enhancing Grid Security with Trust Management”, Proceedings of the 2004 IEEE International Conference on Services Computing (SCC’04). • [Xia04] G. Xiaolin, X.Bing, L.Yinan, Q.Depei, “A Grid Security Infrastructure Based on Behaviors and • Trusts” GCC 2004 Workshops, LNCS 3252 pp. 482–489, Springer-Verlag Berlin Heidelberg, 2004. • Wang, Y., Vassileva, J., “Bayesian Network-Based Trust Model”, Web Intelligence, Halifax Canada, • 2003, pp 372-378. • [Nat05] G. Nathan, C. Kuo-Ming, “Experience-Based Trust: Enabling Effective Resource Selection in a Grid Environment”, iTrust 2005, LNCS 3477, Springer-Verlag Berlin Heidelberg 2005, pp. 240–255. • [Muh06] Muhammad Hanif Durad, Yuanda Cao,” A Vision for the Trust Managed Grid”, Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid Workshops (CCGRIDW'06)

31. References • [Dim04] T. Dimitrakos, D. Golby P. Kearney. Towards a Trust and Contract Management Framework for Dynamic Virtual Organisations. In eAdoption and the Knowledge Economy: eChallenges 2004. Vienna, Austria, 2004. • [Gra00] T. Grandison, M. Sloman. A Survey of Trust in Internet Applications. IEEE Communications Survey and Tutorials, 3, 2000. • [Bro03a] P.J. Broadfoot, G. Lowe. Architectures for Secure Delegation within Grids. Oxford University Computing Laboratory Technical Report, PRG-RR-03-19, 2003. • [Roo71] Rotter, J. B. 1971. Generalized expectancies for interpersonal trust. American Psychologist, 26: 443-452. • [Lew85] Lewis, J. D. & Weigert, A. J. 1985b. Social atomism, holism, and trust. The Sociological Quarterly, 2l6(4):455-471. • [Sur02] M. Surridge. A Rough Guide to Grid Security. Technical Report, IT Innovation Centre, V1.1a, 2002. • [Gas90] M. Gasser, E. McDermott. An Architecture for Practical Delegation in a Distributed System. IEEE • Symposium on Research in Security and Privacy, 1990.

32. References • [Fos98] I. Foster, C. Kesselman, G. Tsudki, S. Tuecke. A Security Architecture for Computational Grids. In Proceedings of 5th ACM Conference on Computer and Communication Security, 1998. • [Joh03] W.E. Johnston, J.M. Brooke, R. Butler, D. Foster and M. Mazzucato. Production Deployment: • Experiences and Recommendations. In [Fos03], 2003. • [Nag03] N. Nagaratnam, P. Janson, J. Dayka, A. Nadalin, F. Siebenlist, V. Welch, S. Tuecke, I. Foster. Security Architecture for Open Grid Services. Available at http://forge.gridforum.org/projects/ogsa-sec-wg. • [Ton06] N. Tonellotto, R. Yahyapour, Ph. Wieder, CoreGRID Technical Report ,Number TR-0015 January 11, 2006 • [Ji06] Ji Ma and Mehmet A. Orgun, Trust Management and Trust Theory Revision, IEEE Transactions On Systems, Man, And Cybernetics—Part A: Systems And Humans, Vol. 36, No. 3, May 2006. • [Ind04] Indrajit Ray and Sudip Chakraborty, “A vector Model of Trust for Developing Trustworthy Systems”, Proceedings of 9th European Symposium on Research in Computer Security (ESORICS'04), 2004.

33. References • [Dan01] Dan J. Kim, Y. Il Song, S. B. Braynov and H. R. Rao, “A B-to-C Trust Model for On-line Exchange”, Americas Conference on Information Systems(AMCIS), Boston, Massachusetts, August 3-5, .2001. • [Pat05] V.Patel, R.K.Shyamasundar, “Trust management for e-transactions”, sadana, vol. 30, April/June 2005, pp 141-158. • [Ros57] Rosenberg, M. Occupations and values. Glencoe, IL: Free Press. • http://www.mobilegrids.org/ • http://www.ist-daidalos.org/ • http://www.eu-egee.org/ • http://www.hpc4u.org/ • http://www.nextgrid.org/ • http://www.gridprovenance.org/ • http://www.simdat.org • http://www.eu-trustcom.com • http://www.unigrids.org

34. Thank you Questions

35. Backup Slides

36. Ganglia • Ganglia is a scalable distributed monitoring tool used for high-performance computing systems such as clusters and Grids. • Two unique daemons - gmetad (Ganglia Meta daemon) - gmond (Ganglia Monitoring daemon) • gmond - monitor/announce/listen to the changes in host state • gmetad - Runs in master node and gathers information from all nodes that runs gmond Node D (Master Node) gmetad gmond gmond gmond Node C Node A Node B

37. Network Weather Service • a generalized distributed monitoring system • periodically monitors and dynamically forecasts the performance of various network and computational resources • The nameserver running in the master node gathers network characteristics from all sensor nodes and stores in memory Node D (Master Node) nws-nameserver memory nws-sensor nws-sensor nws-sensor Node A Node C Node B

38. Whetstone/Dhrystone Benchmarks • Gives MIPS of an executable • Instruction count – Using Linux command MIPS = Instruction count / Execution time*106 Further Literature

39. Literature Survey Issues How to evaluate each trust metric? Implementation Ahead …..

40. Implementation – Parameter RetrievalActual Execution time, Success & Failure Trust Layer Gridway Metascheduler Gridway Metascheduler DRMAAs Obtains Actual Execution Time Actual Execution Time JAVA Module Success Success Failure Failure Reads Status Status of Execution Grid Middleware Layer Job Submission Fabric Layer Resource A

41. Implementation – Parameter RetrievalAvailability Gridway Trust Layer Down time JAVA Module JAVA Module queries Availability Up time Ganglia gmetad POLLS Grid Middleware Layer Ganglia gmond Fabric Layer Master Node of Resource A

42. Implementation – Parameter RetrievalBandwidth, Latency Trust Layer Gridway JAVA Module Bandwidth nws-nameserver Latency Memory Grid Middleware Layer nws-sensor nws-sensor nws-sensor Fabric Layer Master Node of A Master Node of B Master Node of C

43. JAVA Module User Feedback Resource Registration Database Portal InterfaceUser Feedback, Resource Registration Resource Provider Application Layer user Trust Layer

44. The Ultimate Flow … 6 NWS Database Whetstone/ Dhrystone Ganglia 6 6 12 4 6 5 Trust Management Portal 5 2 1 Gridway Metascheduler 9 MDS 8 10 11 users 3 Trust Resource Broker Resource Domain