Transcript slides
Recording Actor Provenance in Scientific Workflows Ian Wootten, Shrija Rajbhandari, Omer Rana [email protected] Cardiff University, UK 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) What? Actor Provenance A2 A1 B2 B1 Service Service Enactment Engine Actor State Assertions: Asserting the state of an actor at a particular time during an interaction. Interaction Assertions: Asserting the contents of a message by an actor sending or receiving it. 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 How? Actor Provenance Instrumented Output Monitor Output M1 B2 B1 M2 Service Service Enactment Engine Instrumented Actor: Service information obtained from instrumented points within an actor. Monitoring Sources: Service information derived from hosting platform via monitoring sources (eg Ganglia) 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 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 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? How? Actor Provenance Registry Integration with PReP [Groth et al.] Local Store Record Provenance Provenance Store Record Provenance Local Store Registry Monitoring Sources Client Service Registry Invoke Result Record Actor Provenance Record Actor Provenance Monitoring Sources 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 Performance Evaluation 5 rules 40000 35000 1 rule Invocation Time (ms) 30000 No rules 25000 20000 15000 10000 5000 0 0 50 100 150 200 Size of Data Set (KB) 250 300 350 400 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 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