1. Recording Actor Provenance in Scientific Workflows Ian Wootten, Shrija Rajbhandari, Omer Rana I.M.Wootten@cs.cf.ac.uk Cardiff University, UK

2. What? Provenance is concerned with process  This may or may not be documented Data Provenance – The process which leads to a particular piece of data Actor Provenance - The process which leads to a particular actor state  How an actor (client or service) arrived at a particular state during an interaction (for stateless actors)

3. What? Actor Provenance Service Enactment Engine Service Interaction Assertions: Asserting the contents of a message by an actor sending or receiving it. A1A1 A2A2 B1B1 B2B2 Actor State Assertions: Asserting the state of an actor at a particular time during an interaction.

4. Metrics for Actor State Assertion Static  No variation in value over actor lifetime Per Node - Node identity, Operating system Per Actor - Actor identity, Name, Owner, Version Dynamic  Variation in value over actor lifetime Per Node - Memory usage, Network traffic Per Actor - Execution Time, Availability Instrumented  Actor is ‘Instrumented’ at Key Points in its Execution Description of internal data flow  Eg. German Aerospace Center (DLR) Completion states for action events and file transfers

5. How? Actor Provenance Service Enactment Engine Service B1B1 B2B2 M1M2 Instrumented Output Monitor Output Monitoring Sources: Service information derived from hosting platform via monitoring sources (eg Ganglia) Instrumented Actor: Service information obtained from instrumented points within an actor.

6. Why? Standalone and Combined Value Standalone State Assertion Value  Actor Selection Performance Evaluation of Past / Prediction of Future  Resource Allocation Actor administrator allocates resources according to performance metrics Combined Value - Putting Assertions into Context  Interaction – Through Actor State Assertions Determining the likely cause of error / results Understanding what an actor is doing  Actor – Through Interaction Assertions Understanding performance pattern observations Understanding instrumented metric observations

7. How? Actor Provenance Registry Attempt to provide a mechanism to specify and record actor state assertions for any application Generic Mechanism Problems  No Knowledge of Potential Resources Monitoring sources, containers  No Direct Knowledge of Implementation Instrumented Data Capture

8. How? Actor Provenance Registry Resource and Rule Registration  Resource – Monitoring Tool  Rule - User defined instructions Indirectly from Resources  Coordinator polls resources for information  Times of interest – Service Invocation, Request Directly from actor  Collection of Instrumented data Representation?

9. How? Actor Provenance Registry Integration with PReP [Groth et al.]

10. Data Mining Prototype Record assertions using registry during invocation of a data modelling service Service takes incoming data sets and generates a model based upon it  Uses Quantitative Structure-Activity Relationship (QSAR) to attempt to correlate biological activity to a chemical compound  Larger data set = longer run time

11. Performance Evaluation No rules 1 rule 5 rules

12. Conclusions / Future Work Actor Provenance data is important  Without it, we don’t get the full picture Prototype shows that it can be done  Room for improvement Interface to Monitoring System Caching of results  No inclusion of ‘instrumented’ actor capture  Requires service provider adoption to work

13. Prototype Configuration Single machine holding both client, service and registry Rules executed on invocation of service  XQuery  Invocations performed 100 times on datasets between 30KB – 340KB in size Coordinator records rule results to a local file store