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Evaluating a Data Removal Strategy for Grid Environments Using Colored Petri Nets

Evaluating a Data Removal Strategy for Grid Environments Using Colored Petri Nets. Nikola Tr č ka , Wil van der Aalst, Carmen Bratosin, and Natalia Sidorova. Overview . Modeling Grid Architectures using Colored Petri nets (CPNs) Motivation (Colored) Petri Nets

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Evaluating a Data Removal Strategy for Grid Environments Using Colored Petri Nets

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  1. Evaluating a Data Removal Strategy for Grid Environments Using Colored Petri Nets Nikola Trčka, Wil van der Aalst, Carmen Bratosin, and Natalia Sidorova

  2. Overview • Modeling Grid Architectures using Colored Petri nets (CPNs) • Motivation • (Colored) Petri Nets • Grid Architecture in Terms CPNs • Data Removal Strategy • Motivation • Method and Evaluation • Conclusions

  3. Motivation • There is little consensus on the definition of a grid • Exact architecture hidden in code A good conceptual model for grids is needed! • CPNs are formal, well established, very expressive, graphical, executable, and with excellent tool support (CPN Tools and PROM).

  4. token place arc transition (task) Petri nets • Used to specify all kinds of systems (automotive, business, manufacturing, computer, etc.) • Capable of modeling parallelism, choices, synchronization, message exchange, timing and resource sharing • Business process example - complaints handling:

  5. Colored Petri nets • CPNs add programming language aspects to Petri nets • Tokens not just black but carry information • Modeling technique: • Control-flow described by Petri nets • Data and data manipulations described in ML • Modularity via hierarchy • Example:

  6. Grid Architecture (standard view) Application layer Middleware Fabric layer

  7. Grid architecture: CPN model – top page Job requests Scheduled jobs Information Retrieval

  8. It gets more complex inside - Scheduler

  9. Why do we need a data removal strategy for grids? • Grid data is typically large • Grid storage space is usually limited • Lack of space can cause delays • Data often occupies grid storage space unnecessarily too long Data should be deleted as soon as possible!

  10. Data removal method • Aim: Make the workflow delete data on time • Method: For every data element add a delete task at the exact place in the workflow from which this data is no longer needed • Algorithm (per data element): • Build the reachability graph of the workflow (state space) • Find the last state from which the data element is not used anymore (can be more than one such state) • Add a (prioritized) delete task, only enabled in this state

  11. Applying the algorithm • Simplified representation of a grid workflow enough • Example: a,b,c,d – some data elements • Data needs to be deleted • a - immediately after the first task (p2) • b - only at the end (p10) • c - when in p5 and p6 • d - when in p8 or p9

  12. Evaluation • Application: Small process mining experiment • Testbed: • 9 nodes • storage area: 5000, 7000, and 10000 • processing cost: 1, 5, and 10 • Min-Min scheduling • varied period • Varied arrival rate

  13. Conclusions • A reference grid architecture modeled in terms of CPNs • CPNs can model all layers of the infrastructure • The model is very descriptive and allows for efficient analysis • A data removal strategy introduced • Strategy evaluated by means of simulation, using the CPN model on a process mining experiment

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