Xiao Liu, Jinjun Chen, Yun Yang CS3: Centre for Complex Software Systems and Services Swinburne University of Technology, Melbourne, Australia {xliu, jchen, Setting Temporal Constraints in Scientific Workflows

2 Introduction  Temporal Verification  Temporal QOS Framework Setting Temporal Constraints in Scientific Workflows  Problem Statement  A probabilistic strategy  Evaluation Conclusion Content

3 Introduction: Temporal Verification Scientific workflow verification: Structure, Performance, Resource, Authorisation, Cost and Time. In reality, complex scientific and business processes are normally time constrained. Hence:  Time constraints are often set when they are modelled as scientific workflow specifications.  Temporal consistency states, i.e. the tendency of temporal violations from consistency to inconsistency, need to be verified and treated proactively and accordingly. Temporal verification is to check the temporal consistency states so as to identify and handle temporal violations.

4 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 Adjustment  Handling different temporal violations.

5 Introduction  Temporal Verification  Temporal QOS Framework Setting Temporal Constraints in Scientific Workflows  Problem Statement  A probabilistic strategy  Evaluation Conclusion Content

6 Problem Statement Most current work adopts only few overall user specified temporal constraints without considering system performance.  Few overall constraints: not applicable for local verification and control.  User specified constraint: frequent temporal violations, huge exception handling costs.

7 Two Basic Requirements Temporal constraints should facilitate both overall coarse-grained control and local fine-grained control.  Coarse-grained constraints refer to those assigned to the entire workflow or workflow segments.  Fine-grained constraints refer to those assigned to individual activities. Temporal constraints should be well balanced between user requirements and system performance.

8 Probabilistic Strategy--Assumptions 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,σ 2 ).  Assumption 2: The activity durations are independent to each other.  Exception handling of assumptions : Using normal transformation and correlation analysis, or moreover, ignoring them first and then adding up afterwards.

9 Probabilistic Strategy--Definitions Weighted Joint Normal Distribution Specification of Activity Durations Probability based Temporal Consistency

10 Weighted Joint Normal Distribution The motivation for weighted joint normal distribution is to estimate the overall completion time of the entire workflow by aggregating the durations of all individual activities. However, they are not in a simple linear relationship. Our strategy is to model each activity duration as random variables and aggregate them according to four basic control- flow structures, i.e. sequence, iteration, parallelism and choice. Since most workflow process models can be easily built by the compositions of the four building blocks, similarly, we can obtain the weighted joint distribution of most workflow processes.

11 Specification of Activity Durations Maximum Duration, Mean Duration, Minimum Duration The 3σ rule 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]. In our strategy, we have the following specification of activity durations:  Maximum Duration D(a i )= µ+3 σ  Mean Duration M(a i )= µ  Minimum Duration d(a i )= µ-3 σ

12 Probability based Temporal Consistency

13 Probabilistic Strategy—Overview

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

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

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

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

18 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 guy who comes up with the strategy! Sorry, statistically, no predictions can be 100% sure! Example: Setting Coarse-grained Constraints

19 Example: 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 (µ i +λσ i ).  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 a i with a distribution of N(µ i, σ i 2 ) is (µ i +1.28σ i ).

20 Evaluation—System Environment Overview of SwinDeW-G environment

21 Step1: Weighted Joint Distribution

22 Step2: Coarse-grained Constraint Negotiation for coarse-grained constraint 6300s 6360s 6390s 6400s 66% 75% 79% 81% WS~N(6210,218 2 ) U(WS)=6400s, λ=0.87

23 Step3: Fine-grained Constraint

24 Introduction  Temporal Verification  Temporal QOS Framework Setting Temporal Constraints in Scientific Workflows  Problem Statement  A probabilistic strategy  Evaluation Conclusion Content

25 Conclusion Temporal verification is important in scientific workflows Setting temporal constraints is a prior task for temporal verification. Two basic requirements:  User requirements & System performance  Coarse-grained & Fine-grained temporal constraints A probabilistic setting strategy  Aggregation: Setting coarse-grained constraints  Propagation: Setting fine-grained constraints Evaluation proves to be effective

26 The End Thanks for your patience and attention!