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Evaluating Clouds for Smart Grid Computing: early Results using GE MARS App Ketan Maheshwari ketan@cs.cornell.edu
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Agenda Objectives of this Study Application Characterization Clouds Implementation Results Conclusions
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Objectives 1.To evaluate cloud infrastructures for smart grid applications 2.To parallelize and port a smart grid application on clouds 3.Evaluate parallel scripting paradigm for usability and performance on clouds
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Application Characterization Two tasks: marsMain and marsOut marsMain Compute Intensive: 40-65 sec marsOut trivial: 3-10 sec A modest run=100 marsMain + 1 marsOut Intermediate results crucial Two tasks: marsMain and marsOut marsMain Compute Intensive: 40-65 sec marsOut trivial: 3-10 sec A modest run=100 marsMain + 1 marsOut Intermediate results crucial 150M/run
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Clouds Considered Amazon EC2 – Commercial, large – provides shared FS – Native interface Cornell RedCloud – Academic, small (96 CPUs) – Eucalyptus interface Futuregrid Cloud (NSF funded) – Academic, medium (~3000 CPUs) – Multiple interfaces (Nimbus, Eucalyptus, OpenStack)
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Implementation: Parallel Scripting App Definition Control parameters Parallel Invocation Application expressed in < 30 lines of code
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Overview of Results Experiments performed running MARS app: – On a local machine: serial and parallel – On individual clouds: serial and parallel – On multiple clouds Data staging experiments performed: – local -> local – local -> cloud instances – cloud instance -> S3 Cloud elasticity evaluated All experiments performed from a neutral external location to avoid network bias (especially since RedCloud is within Cornell network)
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Local: with and without Input Data staging Dramatic speedup from 1 to 8 cores Steady speedup from 8 to 32; can be only as fast as the execution time of slowest task Dramatic speedup from 1 to 8 cores Steady speedup from 8 to 32; can be only as fast as the execution time of slowest task
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Serial and Parallel on Individual Clouds Fast CPUs (2.8 GHz), low bandwidth New Cluster, high bandwidth, fast CPUs (2.6GHz) Seasoned! (2.3GHz)
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Multiple Clouds Slow CPUs and bottlenecks in data staging contributes to low scaling Slow CPUs and bottlenecks in data staging contributes to low scaling
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Cloud Data Movement locally mounted S3 not the fastest!
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Cloud Elasticity elastic not so elastic!
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Inter-cloud Bandwidth *=Gbits/sec
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Conclusions Cloud environments are diverse in properties – Interfaces, invocations, configurations, pricing – Require special tending to make them work seamlessly Academic clouds “not quite there” – Clouds can’t rescue slow, old infrastructures Data movement bottleneck: cloud-based, distributed data-store required? Parallel scripting well-suited to multi-staged computing and well interfaced to clouds
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Thanks! Thank you! Questions and comments welcome!
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