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PI: Prof. Yelena Yesha and Prof. Milton Halem Sponsored by NASA

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Presentation on theme: "PI: Prof. Yelena Yesha and Prof. Milton Halem Sponsored by NASA"— Presentation transcript:

1 A Scalable Workflow Scheduling and Gridding of CALIPSO Lidar/Infrared Data
PI: Prof. Yelena Yesha and Prof. Milton Halem Sponsored by NASA Presented by: Phuong Nguyen and Frank Harris IAB Meeting Research Report Dec 18, 2012

2 Project objectives Make use of the a scalable workflow scheduling system developed by CHMPR/MC2 (implemented on top of Hadoop) on a real Big Data scientific use case perform analysis of global climate change from decadal satellite data infrared radiance records stored in two distinct archives obtained from AIRS and MODIS instruments. perform gridding and subsequent monthly, seasonal and annual trend inter-comparisons with Surface Temperatures from ground station records and compare with model output reanalysis. Gridding other satellite data such as CALIPSO Lidar aerosols and delivery gridded data products

3 A Scalable Workflow Scheduling System

4 A Scalable Workflow Scheduling System
We have developed a A Scalable Workflow Scheduling and Implemented as a workflow system on top of Apache Hadoop Expresses and dynamically schedules parallel data intensive workflow computations: data flows in Directed Acyclic Graph rather than control flow optimizes the level of concurrency shares cluster resources using fine grain scheduling (HybS) support scientific data format (e.g HDF) and computation using float arrays performance predictive model Available JAVA APIs: DagJob, DagBuilder, Graphs … and Libraries: gridding, statistic routines, statistic model Available HybS Hadoop plug in scheduler – configurable to work as Hadoop scheduler in current Hadoop distribution 1.0.1

5 Use case: global climate changes from AIRS and MODIS
Atmospheric Infrared Sounder (AIRS) km Footprint 2378 IR Spectral Channels 5.5 TB / year (L1B) 55 Terabytes; 876,000 HDF files, each file 135x90x2378 (28,892,700 elements) 60MB Moderate Resolution Imaging Spectroradiometer (MODIS) 1 - 4km Footprint (Infrared) 16 IR Spectral Channels 17 TB / year (L1B) 170 Terabytes; 1,051,200 HDF files Produces data product 10 year AIRS FCDR anomalies At 0.50x1.00 lon-lat (100km) from

6 Global climate changes from AIRS and MODIS

7 AIRS gridding using MapReduce approaches
Step 1: Parallel upload AIRS/ MODIS HDF files from NSF/PVFS into Hadoop HDFS Step2: Run gridding AIRS/MODIS using MapReduce jobs Output written to HBASE tables Step3: Analysis on gridded data from HBASE tables or Loading data out of HBASE/HDFS to store HDF files in NFS/PVFS for other analysis Gridding using MapReduce input for Map function a HDF file and output (key, value). key grid cell (latxlon) value is array of sum and count of radiances for all spectral channel Reduce function avg all values with the same key and output into Hbase tables

8 Spatial data locality Bounding box is implemented
Image source: David Chapman Bounding box is implemented Reduce local before shuffle Output stores in Hbase tables for queries e.g monthly, seasonal and annual trend inter-comparisons

9 Improvement of AIRS gridding
Estimated based on daily gridding Used bounding box for spatial data locality Gridding: compare with regular method, embarrassing parallel, gain 35% improvement in total processing time Benefits: scaling, failure handling, gridding at high resolutions, queries by random data access on Hbase tables.

10 Gridding CALIPSO Lidar aerosols Background
Cloud-Aerosol LIDAR Infrared Pathfinder Satellite Observations (CALIPSO) is an Infrared/Lidar satellite, joint project between NASA and CNES (France) Fourth satellite in the A-train formation, follows CloudSat by 15 s, and Aqua by 165 s Launched in 2006

11 Instruments Cloud-Aerosol Lidar with Orthogonal Polarization (CALIPSO)
Detects reflectance of 20 ns laser pulses at nm (IR) and 532 nm (vis) 333 m footprints at full spatial resolution Imaging Infrared Interferometer (IIR) Provides a 3-channel infrared product at 8.65, 10.6, and μm at 1 km spatial resolution Wide Field Camera (WFC) 1-channel visible product at 1 km resolution

12 Progress Developed serial gridder in C, tested on subset of IIR data
Acquired 14 months of IIR data, 333 days, average 1.5 GB per day, total ~ 500 GB In addition, 2 months of CALIPSO data downloaded, for a total of 625 GB, for a total of 3.7 TB/year

13 Gridded Product Full 360x180 degree image
At full-resolution, image is 36000x18000 pixels and 2.4 GB in size Shows expected swath path for sun-synchronous satellite Shows limited coverage of nadir imaging Subset of gridded image Shows high detail within individual swaths Also shows significant moiré interference as a result of my gridding algorithm Plan to improve gridding via inverse distance weighting interpolation in the near future

14 What's Next Acquire rest of dataset (3 TB IIR, 22 TB CALIOP)
When naïve sequential approach done, process using map- reduce Interference, sparse coverage and file size problems can be dealt with by significantly lowering resolution of product to 1°x1° Use NCAR Graphics library instead of reusing built-from- scratch internal code Produce gridded products, monthly and yearly averages Possible scientific applications: Solar reflectances to generate cloud maps, using altimetry data from CALIOP as correction for existing datasets

15 Project Status Have developed AIRS and MODIS gridding and analysis using MapReduce approaches (make use of the workflow system) Showed gridding CALIPSO using serial approach Future work work on gridding CALIPSO using the MapReduce approach test, evaluate and produce data products Phuong Nguyen Working on Open source workflow system and Hybs Hadoop plug-in scheduler.

16 Publications Phuong Nguyen, Tyler Simon, Milton Halem, David Chapman, and Quang Le, "A Hybrid Scheduling Algorithm for Data Intensive Workloads in a MapReduce Environment”, The 5th IEEE/ACM International Conference on Utility and Cloud Computing 2012 Phuong Nguyen, David Chapman, Jeff Avery, and Milton Halem, “A near fundamental decadal data record of AIRS Infred Brightness Temperatures” IEEE Geoscience and Remote Sensing Symposium 2012 Phuong Nguyen PhD dissertation “Data intensive scientific compute model for multiple core clusters” submitted to UMBC Dec 3, 2012 Phuong Nguyen, Milton Halem,“A MapReduce Workflow System for Architecting Scientific Data Intensive Applications”, ACM International Workshop on Software Engineering for Cloud Computing proceeding of ICSE 2011

17 Questions?

18 Back up

19 Difference between Scientific Workflows and business workflows
Service-Oriented Computing: Semantics, Processes, Agents UCL Department of Computer Science August 2004 Difference between Scientific Workflows and business workflows Scientific workflows Business workflows Thousands of service instances (partners) << service instances Thousands of basic service invocations; ten thousands of SOAP messages << invocations and SOAP messages Large numbers of sub-workflows for parallel execution << opportunities for parallel execution Very large amounts of data to be analysed routinely << amount of data to be analysed BPEL is primarily targeted at business workflows Scientific workflows differ in a number of ways The main difference is one of scale along several dimensions Partner, Invocations, Messages When compared to scientific workflows, business workflows usually define a relatively small number of BPEL partners with whom to interact. Scientific workflows may involve thousands of service instances that will need to be modelled as partners. Furthermore, scientific workflows will often execute thousands of basic service invocations and, consequently, send ten thousands of SOAP messages to be exchanged among service partners. Business workflows, in the majority of instances, operate on a smaller scale. Parallel Execution Scientific workflows apply complex computational models that generate large amounts of data and then analyse this data. Therefore, such workflows contain large numbers of similar independent sub-workflows that may be executed concurrently to, for example, run models concurrently and to filter and extract data resulting from an experiment. Business workflows do not usually display such massively parallel execution of very similar sub-workflows on such a scale. Amount of Data Need powerful data manipulation primitives. Think of data analysis pipeline Experiment A scientific workflow represents an experiment that is likely to be run only a limited number of times, before new ideas and insights will need to be incorporated. Frequent changes and re-deployment need to be supported and be made simple. A business workflow captures a set of activities and their relationships in order to describe a business process. The overall aim is to be able to automate this process and execute it repeatedly over possibly long periods of time. © Singh & Huhns

20 Background: MapReduce/Hadoop
Distributed computation on large cluster Each job consists of Map and Reduce tasks Job stages Map tasks run computations in parallel Shuffle combines intermediate Map outputs Reduce tasks run computations in parallel M R M M R R M M Source slide: Brian Cho UIUC

21 Background: MapReduce/Hadoop
Distributed computation on large cluster Each job consists of Map and Reduce tasks Job stages Map tasks run computations in parallel Shuffle combines intermediate Map outputs Reduce tasks run computations in parallel Map input/Reduce output stored in distributed file system (e.g. HDFS) Scheduling: Which task to run on empty resources (slots) Job 1 Job 3 M R M M R R R M R R M M M R M M R M M M M M M M Job 2 Source slide: Brian Cho UIUC

22 Why new workflow scheduling system?
Characteristics of data intensive scientific apps Repeat experiments on the different data Computations on high dimension arrays: spatial, temporal, spectral Variety of data formats, need math libraries Complex components e.g. model prediction Lack of a scientific workflow system to deal with scale scalability, reliability, scheduling, data management, provenance, low overhead Current limitation: Hadoop is a scalable systems built to run at large scales (e.g. runs on cores) commodity clusters Still need to improve key performance metrics Limited support for scientific apps

23 HBASE design for multiple satellites: gridded data
RowKey Column family rowKeyId Resolution-Statistics GeolocationData 100km_AvgBT 100km_Stdev 1km_AvgBT 250m_AvgBT Lat AIRS_ _ch528_20N20S10E10W …data… Scan into MapReduce computation (Table, RowKey, Family, Column, Timestamp) → Value Hbase Index on rowKey value The rowKey design for multiple satellite instruments <InstrumentID>_ <DateTime>_<SpectralChannel>_<Spatial Index> Column families e.g. Resolution-Statistics column1_100km, column_1Km. Spatial Index lat, lon bounding box Index by Instruments, Date Time, Spatial Index and Spectral Channel Scan rows (which columns) into MapReduce computation

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