Temporal Verification in Grid/ Scientific Workflows

1. Temporal Verification in Grid/ Scientific Workflows Xiao Liu CITR - Centre for Information Technology Research Swinburne University of Technology, Australia xliu@swin.edu.au

2. Content • Grid/Scientific Workflows • Temporal QOS Framework • Setting Temporal Constraints in Scientific Workflows • SwinDeW-G Grid Workflow Management System • Additional Information • Research areas in Workflow Technology Program • Data Mining Techniques in Workflow area • Optimization Algorithms in Workflow area

3. Grid/Scientific Workflow • Grid Workflow Management System • A type of workflow management system aiming at supporting large-scale sophisticated scientific and business processes in complex e-science and e-business applications, by facilitating the resource sharing and computing power of underlying grid infrastructure. • Scientific Workflow Management System • A type of workflow management system aiming at supporting complex scientific processes in many e-science applications such as climate modelling, astronomy data processing. It may or may not be built upon grid infrastructure. Can be cluster or P2P.

4. How Are Grid Used Utility computing High-performance computing Collaborative design Financial modeling High-energy physics E-Business Drug discovery Life sciences Data center automation E-Science Natural language processing & Data Mining Collaborative data-sharing From www.gridbus.org

5. An Example Grid Application From www.gridbus.org

6. Grid Architecture From www.gridbus.org

7. Grid Workflow Engine From www.gridbus.org

8. Where Are We • Grid/Scientific Workflows • Temporal QOS Framework • Setting Temporal Constraints in Scientific Workflows • SwinDeW-G Grid Workflow Management System • Additional Information • Research areas in Workflow Technology Program • Data Mining Techniques in Workflow area • Optimization Algorithms in Workflow area

9. Temporal Verification • In reality, complex scientific and business processes are normally time constrained. Hence, time constraints are often set when they are modelled as grid workflow specifications. • Temporal constraints mainly include: upper bound, lower bound and fixed-time • Upper bound constraint • Lower bound constraint • Fixed-time constraint • Temporal verification is used to identified any temporal violations so that we can handle them in time.

10. Temporal QOS Framework • Constraint Setting • Setting temporal constraints according to temporal QOS Specifications • Checkpoint Selection • Selecting necessary and sufficient checkpoints to conduct temporal verification • Temporal Verification • Verifying the consistency states at selected checkpoints • Temporal Consistency: SC (Strong Consistency), WC (Weak Consistency), WI (Weak Consistency), SI (Strong Consistency) • Temporal Adjustment • Handling temporal violations

11. Where Are We • Grid/Scientific Workflows • Temporal QOS Framework • Setting Temporal Constraints in Scientific Workflows • SwinDeW-G Grid Workflow Management System • Additional Information • Research areas in Workflow Technology Program • Data Mining Techniques in Workflow area • Optimization Algorithms in Workflow area

12. Setting Temporal Constraints • Problem Statement • In scientific workflow systems, temporal consistency is critical to ensure the timely completion of workflow instances. To monitor and guarantee the correctness of temporal consistency, temporal constraints are often set and then verified. However, most current work adopts user specified temporal constraints without considering system performance, and hence may result in frequent temporal violations that deteriorate the overall workflow execution effectiveness. • Granularity of temporal constraints • Coarse-grained constraints refer to those assigned to the entire workflow or workflow segments. • Fine-grained constraints refer to those assigned to individual activities.

13. A Motivating Example • This workflow segment contains 12 activities which are modeled by SPN (Stochastic Petri Net) with additional graphic notations. For simplicity, we denote these activities as X1 to X12. The workflow process structures are composed with four SPN based building blocks, i.e. a choice block for data collection from two radars at different locations (activities X1 to X4), a compound block of parallelism and iteration for data updating and pre-processing (activities X6 to X10), and two sequence blocks for data transferring (activities X5 ,X11 to X12).

14. Two Basic Requirements • Temporal constraints should be well balanced between user requirements and system performance. • It is common that clients often suggest coarse-grained temporal constraints based on their own interest while with limited knowledge about the actual performance of workflow systems. Therefore, user specified constraints are normally prone to cause frequent temporal violations. • Temporal constraints should facilitate both overall coarse-grained control and local fine-grained control. • Both coarse-grained temporal constraints and fine-grained temporal constraints should be supported. However, note that coarse-grained temporal constraints and fine-grained temporal constraints are not in a simple relationship of linear culmination and decomposition. Meanwhile, it is impractical to set fine-grained temporal constraints manually for a large amount of activities in scientific workflows.

15. A Probabilistic Strategy • Probability based temporal consistency • A novel probability based temporal consistency which utilise the weighted joint distribution of workflow acitivity durations is proposed to facilitate setting temporal constraints. • Two assumptions on activity durations • Assumption 1: The distribution of activity durations can be obtained from workflow system logs. Without losing generality, we assume all the activity durations follow the normal distribution model, which can be denoted as N(µ,σ2) . • Assumption 2: The activity durations are independent to each other. • Exception handling of assumptions : Using normal transformation and correlation analysis, or moreover, ignoring first when calculating joint distribution and then added up afterwards.

16. Weighted Joint Normal Distribution • Joint normal distribution • If there are n independent variables of Xi~N (µi,σi2) and n real numbers θi, where n is a limited natural number, then the joint distribution of these variables can be obtained with the following formula: • Weighted joint normal distribution • For a scientific workflow process SW which consists of n activities, we denote the activity duration distribution of activity ai as N (µi,σi2) with (1≤i≤n). Then the weighted joint distribution is defined as: where wi stands for the weight of activity ai that denotes the choice probability or iteration times associated with the workflow path where ai belongs to.

17. Probabilistic Specification of Activity Durations • Maximum Duration, Mean Duration, Minimum Duration • The 3σrule depicts that for any sample comes from normal distribution model, it has a probability of 99.73% to fall into the range [µ-3 σ, µ+3 σ] of which is a systematic interval of 3 standard deviation around the mean. According to this, in our strategy, we have the following specification of activity durations: • Maximum Duration D(ai)= µ+3 σ • Mean Duration M(ai)= µ • Minimum Duration d(ai)= µ-3 σ

18. Probability based Temporal Consistency

19. Setting Strategy

20. Stpe1: Weighted Joint Normal Distribution • Here, to illustrate and facilitate the calculation of the weighted joint distribution, we analyse basic SPN based building blocks, i.e. sequence, iteration, parallelism and choice. These four building blocks consist of basic control flow patterns and are widely used in workflow modelling and structure analysis. Most workflow process models can be easily built by their compositions, and similarly for the weighted joint distribution of most workflow processes.

21. Step2: Setting Coarse-grained Constraints Let me check the probability I Want the process be completed in 48 hours The negotiation process

22. Step2: Setting Coarse-grained Constraints Sir, its 70%, do you agree? That’s not good, how about 52 hours Adjust the constraint

23. Step2: Setting Coarse-grained Constraints Then, it increases to 85% Err… how long will it take if I want to have 90% Adjust the probability

24. Step2: Setting Coarse-grained Constraints Ok, that’s the deal! Let’s do it! It will take us 54 hours Negotiation result

25. Step2: Setting Coarse-grained Constraints Ok! But, sir, I need to remind you that this is only a guarantee from statistic sense. If we cannot make it, please blame the stupid guy who invents the strategy! Sorry, statistically, no predictions can be 100% sure!

26. Step3: Setting Fine-grained Constrains • Setting fine-grained constraints for individual activities • Assume the probability gained from the last step is θ% that is with a normal percentile of λ. Then the fine-grained constraints for individual activities are (µi +λσi). • For example, if the coarse-grained temporal constraints are of 90% consistency, that is a normal percentile of 1.28, then the fine-grained constraint for activity ai with a distribution of N(µI, σi) is (µi +1.28σi).

27. Evaluation--Specification

28. Setting Results: Coarse-grained Constraint • Negotiation for coarse-grained constraint WS~N(6210,2182) 6300s 66% 6360s 75% 6390s 79% 6400s 81% U(WS)=6400, λ=0.87

29. Setting Results: Fine-grained Constraint

30. Where Are We • Grid/Scientific Workflows • Temporal QOS Framework • Setting Temporal Constraints in Scientific Workflows • SwinDeW-G Grid Workflow Management System • Additional Information • Research areas in CITR Workflow Technology Program • Data Mining Techniques in Workflow area • Optimization Algorithms in Workflow area

31. SwinDeW-G Grid Workflow System • SwinDeW-G stands for Swinburne Decentralised Workflow for Grid. • SwinDeW-G is a peer-to-peer based scientific grid workflow system running on the SwinGrid (Swinburne service Grid) platform. Swinburne CITR (Centre for Information Technology Research) Node, Swinburne ESR (Enterprise Systems Research laboratory) Node, Swinburne Astrophysics Supercomputer Node, and Beihang CROWN (China R&D environment Over Wide-area Network) Node in China. They are running Linux, GT4 (Globus Toolkit) or CROWN grid toolkit 2.5 where CROWN is an extension of GT4 with more middleware, hence compatible with GT4.

32. Where Are We • Grid/Scientific Workflows • Temporal QOS Framework • Setting Temporal Constraints in Scientific Workflows • SwinDeW-G Grid Workflow Management System • Additional Information • Research areas in CITR Workflow Technology Program • Data Mining Techniques in Workflow area • Optimization Algorithms in Workflow area

33. Research Areas in WT • http://www.swin.edu.au/ict/research/citr/wt/research.php • Peer-to-peer based, service oriented and grid workflows • SwinDeW-A: SwinDeW with agent enhanced negotiation • SwinDeW-B: SwinDeW incorporating BPLE4WS (past) • SwinDeW-G: peer-to-peer based service grid workflow system • SwinDeW-S: SwinDeW incorporating Web services (past) • SwinDeW-V: temporal constraint verification in grid workflows • SwinDeW: peer-to-peer based decentralised workflow system (past) • Service-oriented computing • SwinGrid - a Swinburne Service Grid Platform which connects Swinburne CITR nodes and Swinburne Supercomputer with external nodes nationally and internationally, forming a Grid computing environment.

34. Recent Publications in WT • http://www.ict.swin.edu.au/personal/yyang/Publications.html • X. Liu, J. Chen and Y. Yang, A Probabilistic Strategy for Setting Temporal Constraints in Scientific Workflows, Proc. 6th International Conference on Business Process Management (BPM2008), Sept. 2008 Milan, Italy. • K. Ren, X. Liu, J. Chen, N. Xiao, J. Song, W. Zhang, A QSQL-based efficient Planning Algorithm for fully-automated Service Composition in Dynamic Service Environments, Proc. of IEEE International Conference on Services Computing (SCC2008), Honolulu, Hawaii, USA, July 2008. • J. Chen and Y. Yang, A Taxonomy of Grid Workflow Verification and Validation. Concurrency and Computation: Practice and Experience, Wiley, 20(4):347-360, 2008. • J. Chen and Y. Yang, Adaptive Selection of Necessary and Sufficient Checkpoints for Dynamic Verification of Temporal Constraints in Grid Workflow Systems. ACM Transactions on Autonomous and Adaptive Systems, 2(2):Article6, June 2007. • Q. He, J. Yan, R. Kowalczyk, H. Jin, Y. Yang, Lifetime Service Level Agreement Management with Autonomous Agents for Services Provision. Information Sciences, Elsevier, to appear. • K. Liu, J. Chen, Y. Yang and H. Jin, A Throughput Maximisation Strategy for Scheduling Transaction Intensive Workflows on SwinDeW-G. Concurrency and Computation: Practice and Experience, Wiley, to appear. • J. Yan, Y. Yang and G. K. Raikundalia. SwinDeW - A Peer-to-peer based Decentralized Workflow Management System. IEEE Transactions on Systems, Man and Cybernetics, Part A, 36(5):922-935, 2006.

35. Where Are We • Grid/Scientific Workflows • Temporal QOS Framework • Setting Temporal Constraints in Scientific Workflows • SwinDeW-G Grid Workflow Management System • Additional Information • Research areas in CITR Workflow Technology Program • Data Mining Techniques in Workflow area • Optimization Algorithms in Workflow area

36. Data Mining Techniques in Workflow area • Process Mining Overview 2) process model 3) organizational model 4) social network 5) performance characteristics 1) basic performance metrics 6) auditing/security If …then … From www.processmining.org

37. Process Mining • Process Discovery • Conformance testing • Log based verification From www.processmining.org

38. ProM Framework From www.processmining.org

39. Other Workflow Mining Topics • Successful Termination Prediction. • To choose an activity from a given set of potential activities which is the choice performed in the past that had more frequently led to a desired final configuration. • Identification of Critical Activities. • To discover those activities that can be considered critical in the sense that they are scheduled by the system in every successful execution. • Failure/Success Characterization. • By analysing the past experience, a workflow administrator may be interested in knowing which discriminate factors characterize the failure or the success in the executions. • Workflow Optimization. • The information collected into the logs of the system can be profitably used to reason on the “optimality” of workflow executions. • Workflow Performance Related Analysis and Prediction • Time series mining used in the prediction of activity durations, setting temporal constraints and dynamic temporal verification

40. References on Workflow Mining • G. Greco, A. Guzzo, G. Manco and D. Sacca, Mining and Reasoning on Workflows, IEEE Trans. on Knowledge and Data Engineering, Vol. 17, No. 4, pp.519-534, APRIL 2005. • W.M.P. van der Aalst, B.F. van Dongen, J. Herbst, L. Maruster, G. Schimm, and A.J.M.M. Weijters, Workflow Mining: A Survey of Issues and Approaches. Data and Knowledge Engineering, Vol. 47, No. 2, pp.237-267, 2003. • A.K.A. de Medeiros, W.M.P. van der Aalst, and A.J.M.M. Weijters, Workflow Mining: Current Status and Future Directions, CoopIS 2003, volume 2888 of Lecture Notes in Computer Science, pages 389-406. Springer-Verlag, Berlin, 2003.   • W.M.P. van der Aalst, H.T. de Beer, and B.F. van Dongen, Process Mining and Verification of Properties: An Approach based on Temporal Logic, CoopIS 2005, volume 3760 of Lecture Notes in Computer Science, pages 130-147. Springer-Verlag, Berlin, 2005.

41. Where Are We • Grid/Scientific Workflows • Temporal QOS Framework • Setting Temporal Constraints in Scientific Workflows • SwinDeW-G Grid Workflow Management System • Additional Information • Research areas in CITR Workflow Technology Program • Data Mining Techniques in Workflow area • Optimization Algorithms in Workflow area

42. Core Grid Infrastructure Services Information Services MonitoringServices SecurityServices Grid Resource Manager PBS Grid Resource Manager LSF Grid Resource Manager … Local Resource Management Resource Resource Resource Grid Resource Management System Higher-Level Services User/Application Grid Middleware ResourceBroker From http://www.coregrid.net

43. Grid Workflow Scheduling Grid User Grid-Scheduler Scheduler Scheduler Scheduler time time time Schedule Schedule Schedule Job-Queue Job-Queue Job-Queue Machine 1 Machine 2 Machine 3 From http://www.coregrid.net

44. A taxonomy of Grid workflow scheduling algorithms

45. GA based Scheduling Fundamentals for GA based Scheduling 1. Encoding/Decoding 2. Genetic Operators: Crossover, Mutation and Selection. 3. Fitness Evaluation Function

46. Others • Simulated Annealing • Ant Colony • Workflow Rescheduling • When any QOS constraints are violated, how to handle those violations by rescheduling current task list to compensate, e.g. time or budget deficits.

47. Summary • Grid/Scientific Workflows • Temporal Verification and Temporal Adjustment to Support Temporal QOS Framework • Workflow Mining (More than process mining ) • Optimization Algorithms for Workflow Scheduling and Rescheduling

48. Useful Links • www.swinflow.org • Our work on temporal verification in scientific/grid workflows • http://is.tm.tue.nl/staff/wvdaalst/ • Home page of Pro. Wil van der Aalst, Workflow Research • http://www.buyya.com/ • Home page of Dr. Rajkumar Buyya, Grid Research • http://www.cs.ucr.edu/~eamonn/ • Home page of Eamonn Keogh, Time Series Mining • http://www.cs.ucr.edu/~eamonn/time_series_data/ , UCR Time Series Database

49. The End • Any questions or comments?