1.IEEE eScience 2012 2012113036@

2. eScience -- NeSC 2000 CI 4-- Jim Gray (Microsoft) Science ParadigmsT empirical Science natural phenomenadescribingData intensiveExperimental Theoretical ComputationalParadigm (eScience)theoretical 2using models, generalizations 4 2= K3 2computationalsimulating complex phenomenaTdata exploration 2 3. IEEE eScience 2012 201210812 eScience 250 30% (48/160) Microsoft eScience workshop IEEE eScience 5 Collaborative research using eScience infrastructure and high speed networks (NESC) OGF36 GLIF3 4. Microsoft eScience workshop 9 Jim Gray Award: Antony John Williams (Royal Society of Chemistry) e.g., FP7 VENUS-C e.g., ODataOGC 2019190,000 -- McKinsey 4 5. NESC workshop eScience ESnetSURFnet Scientic Workows using Dynamically Provisioned High-Speed Circuits (UNC-CH) ORCA/ExoGENI Evaluation of RoCE over a 40 Gbps Transcontinental Layer-2 Circuit (LBNL) ESnetANI 100GRDMA over Converged Ethernet SCRDMA 5 6. IEEE eScience 2012 : 4 Mythbusting Knowledge Transfer with nanoHUB.org: A software as a service cloud focused on end-to-end application users, application developers, and 24/7 service, Gerhard Klimeck Dark Matter, Public Health, and Scientic Computing, Gregory Wilson The Reality of Reproducibility of Computational Science, Carole Goble Predictability and Understanding of Our Climate Risk: Approximations, Bugs and Insight, Leonard Smith : 2 Designing and Operating Cities using Open Data Funding Agency Plan : 15 Workow 1/2, Ecology, Process of eScience, Workow and the Users, Applications and Data, Technologies, Data Provenance, Biology, Semantics, Data Mining and Machine Learning, Runtime and Middleware, Data Infrastructure, Data Access and Management, Application Systems and Frameworks 6 7. Runtime and Middleware "P*: A Model of Pilot-Abstractions", Andre Luckow, et. al. CIAPI XSEDEEGICondor-GPBSPilot API "MARISSA: MApReduce Implementation for Streaming Science Applications", Elif Dede, et. al. MapReduceHadoop Streaming Condor "Cooperative VM Migration for a Virtualized HPC Cluster with VMM-Bypass I/O devices", Ryousei Takano, et. al. InnibandVMMI/OVM "Using Promethee Methods for Multi-Criteria Pull-based Scheduler on DCIs", Mircea Moca and Gilles Fedak CI 7 8. Cooperative VM Migration (1/2) I/O PCIVMKVM (IB) KVM (virtio)300 BMM (IB)BMM (10GbE) VM1VM1 KVM (IB)KVM (virtio)250 Guest OS Guest OS Execution time [seconds]200 PhysicalGuest driver driver150100 VMMVMM 50Physicaldriver0 BT CG EP FTLUThe overhead of I/O virtualization on the NAS IB QDR HCA 10GbE NICParallel Benchmarks 3.3.1 class C, 64processes.BMM: Bare Metal Machine8 9. Cooperative VM Migration (2/2) VM VMMI/O VMMOSMPI 1400linkuppostcopy migration Execution time [seconds] 1200 hotplug precopy migration+105 s application 1000 +103 s800 +97 s600+299 s400 200 0baseline precopy postcopy baseline precopy postcopy baseline precopy postcopy baseline precopy postcopy BT CG FT LU9 10. (Data) Provenance sourceorigin Provenance OS c.f., Provenance-aware storage system [USENIX06] c.f., Provenance and Scientic Workows [SIGMOD08] Provenance Open Provenance Model, W3G XG-Prov 10 11. eScience MapReduce Data Provenance 11