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A Personal Cloud Controller

A Personal Cloud Controller. Yuan Luo School of Informatics and Computing , Indiana University Bloomington, USA PRAGMA 26 Workshop. Problems.

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A Personal Cloud Controller

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  1. A Personal Cloud Controller Yuan Luo School of Informatics and Computing, Indiana University Bloomington, USA PRAGMA 26 Workshop

  2. Problems • To fully leverage existing resources for their applications, users often need to manually configure available resources to a custom configuration, a time consuming task. • Labs may have a suite of applications with different requirements and runtime profiles (e.g., compute intensive, data intensive, data sensitive) necessitating different scheduling approaches. • Collaborative e-Science projects typically require data processing to be performed on distributed data sets. Some data are so sensitive that they cannot be processed out of certain administrative boundaries. • Checkpointing is a direction to achieve fault tolerance. But in some cases, checkpointing is expensive as it consumes large amount of time and resources.

  3. Solution Space • User Controllability • Resource Allocation • Sensitive Data Processing • Fault Tolerance for Long-Run Applications

  4. Personal Cloud Controller(Solution to user controllability) • Personal Cloud Controller (PCC) integrates and leverages PRAGMA tools to easily deploy and manage virtual clusters. • PCC is lightweight and extends HTCondorfor scheduling and fault tolerance capabilities. PCC • Provides flexible interface to enable a high level of user controllability, • Automates configuration, deployment, and fault recovery on behalf of user. Personal Cloud Controller Web Interface PCC-HTCondor PRAGMA tools (pragma_boot, ViNE, iPOP) Rocks OpenNebula …

  5. Personal Cloud Controller – cont’dFundamentals of the following… • Resource Allocation • Resources and applications advertising • Match resources and applications • Sensitive Data Processing • PCC enabled PRAGMA Cloud • Long-Run Applications Fault Tolerance • HTCondor’scheckpointing mechanism (complementary to provenance-based fault tolerance)

  6. PCC Enabled PRAGMA Cloud PCC-HTCondor Master Cluster D Cluster A Cluster Master Node Unclaimed Resource Cluster C Cluster B Allocated Resource Physical Network Virtual Network The PRAGMA Cloud Provenance Collecting Path

  7. Central Manager PCC-HTCondor Architecture Startd Negotiator Collector Schedd … Machine 1 (submit) Machine N (execute) Startd Startd Schedd Schedd Shadow Starter VM GAHP Communication Path Process Invoke PRAGMA Cloud tools (pragma_boot)

  8. Ongoing and Future Work • Develop a set of application-driven resource allocation strategies and scheduling algorithm based on data locality, sensitivity, etc. • Extend the Hierarchical MapReduce model to support sensitive data processing. • Develop a provenance-based fault-tolerance model to handle long-run applications.

  9. Acknowledgement • The author would like to thank • Dr. Beth Plale(Indiana University) • Dr. Philip Papadopoulos, and ShavaSmallen(UCSD) • This work funded in part by NSF Award OCI 1234983

  10. Thank you!

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