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Data Management in Cloud Workflow Systems Dong Yuan

Data Management in Cloud Workflow Systems Dong Yuan Faculty of Information and Communication Technology Swinburne University of Technology. Outline. Cloud Computing & Cloud Workflow Systems Introduction to cloud workflow systems. A brief overview of grid workflow systems.

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Data Management in Cloud Workflow Systems Dong Yuan

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  1. Data Management in Cloud Workflow Systems Dong Yuan Faculty of Information and Communication Technology Swinburne University of Technology

  2. Outline • Cloud Computing & Cloud Workflow Systems • Introduction to cloud workflow systems. A brief overview of grid workflow systems. • Data Management in Cloud Workflow Systems • New features and research issues • Cloud Computing Environment and SwinDeW-C • Our simulation environment and cloud workflow system

  3. Cloud Computing & Cloud Workflow Systems

  4. Cloud Computing • Some new features of cloud computing • Large data centres with cheap hardware • Virtualisation • Internet based and SOA • SaaS, PaaS, IaaS • Market driven and cost model • Research of cloud computing has emerged in many areas • Data mining, Database, Parallel computing & Scientific application, Content delivery

  5. Cloud Workflow Systems • Grid workflow systems • Kepler, Pegasus, Taverna, MOTEUR, Triana, ASKALON • Gridbus, GridFlow • Build-time: focus on data modelling. • Kepler: actor-oriented data modelling. Taverna - Sculf. ASKALON - AGWL • Runtime: adopt Data Grid system • Grid DataFarm, GDMP, GridDB, SRB, RLS (P-RLS), GSB, DaltOn

  6. Cloud Workflow Systems • Architecture • Based on Internet • Platform as a Service • More distributed

  7. Data Management in Cloud Workflow Systems

  8. Data Management in Cloud Workflow Systems • New features and challenges • Independent of users and automatic • Cost driven • computation cost, storage cost, data transfer cost • Data dependency • Task – data, data – data, derivation • Some research issues • Data partition, placement, replication, synchronisation, provenance, catalogue, meta-data, consistence, reduction, storage, movement, etc.

  9. Data Placement in Cloud Workflow Systems • Data Placement: to decide where to store the application data in the distributed data centres • Aims: • Reduce data movement • Reduce task waiting time • Strategy: • Data dependency: dataset – dataset • Build-time: existing data, runtime: generated data (also intermediate data)

  10. Data Replication in Cloud Workflow Systems • Data replication: for one dataset, store several copies in different places (data centres) • Aims: • Increase data security • Fast data access • Reduce data movement • Strategy: • Dynamic replication.

  11. Intermediate Data Storage in Cloud Workflow Systems • Intermediate data storage is especially importance in scientific workflows • Aim: • Reduce system cost • Strategy: • Intermediate data can be regenerated with data provenance information • Selectively store some key intermediate datasets

  12. Cloud computing environment and SwinDeW-C

  13. Simulation Cloud

  14. Web Portal

  15. Related key system components of SwinDeW-C

  16. End • Questions? Thanks!

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