A.R.M.S. Active Resource

Management Services

For Big Data Processing

Revised Presentation One

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Outline• 1: Title• 2: Outline• 3: Members• 4: Mentor• 5-6: Societal Issue• 7: History• 8-9: Dr. Li• 10-11: Cluster Computing• 12-14: Case Study• 15: Accuracy• 16: Current Major Functional

Component Diagram• 17: Current Process Flow• 18: Problem Statement

• 19: Proposed Major Functional Component Diagram

• 20: Proposed Process Flow• 21-24: Dinosolve Walkthrough• 25: Dinosolve Issues• 26: Software• 27: Hardware• 28: Solution Statement• 29: Competition Identified• 30-32: 508 Compliance• 33: Objectives• 34: Benefits of Solution• 35: Conclusion• 36-39: References• 40-44: Appendix

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Group Members and Roles

• Scott Pardue (Team Leader)• Michael Rajs (Risk Manager)• Adam Willis (Algorithm Specialist)• Sybil Acotanza (Documentation

Specialist)• Jordan Heinrichs (Database Designer)• David Crook (User Interface

Designer)

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Dr. Yaohang Li

•Associate Professor in the Department of Computer Science at Old Dominion University.•Research interests include:

•Computational Biology: applies computational simulation techniques to solve biological problems•Markov Chain Monte Carlo (MCMC) methods: statistical algorithm for sampling from probability distributions•Parallel Distributed Grid Computing: uses multiple computers communicating via Internet to solve a problem

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How do researchers handle the massive amounts of data they are collecting in

order to benefit their research?

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“Every day, [mankind] create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone.”1

3/21/2013 6http://www-01.ibm.com/software/data/bigdata/

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• Large Hadron Collider 2

– 150 million sensors report 40 million times per second

• Facebook 3

– 2.5 billion – content items shared– 2.7 billion – “Likes”– 300 million – photos uploaded

• Walmart 2

– 1 million customer transactions– 2.5 x 10^15 bytes of data

3/21/2013 http://techcrunch.com/2012/08/22/how-big-is-facebooks-data-2-5-billion-pieces-of-content-and-500-terabytes-ingested-every-day/

Data Management Examples

Dr. Li’s Research• Ideally, his research can be used to

develop new protein-modeling programs. Computational approaches can be more efficient and less expensive than biologists, chemists and others experimenting in lab settings

• Leads to the manufacturing of additional drugs to fight conditions as varied as Alzheimer’s disease, cystic fibrosis and mad cow disease

http://diverseeducation.com/article/13348/

Dr. Li’s Grants

• Dinosolve, his current project, was secured for a five year, $400,000 CAREER Award from the National Science Foundation

• Dr. Li has been the principal or co-principal investigator on research grants totaling more than $15.3 million

Big Data Analysis Hardware• Cluster Computing 4

• A cluster consists of many nodes (computers).• Big data can be generated and analyzed quicker by

spreading the workload amongst the nodes.

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Head Node• Logging data• Job submission3 Computation Node• 2 Processors each

• 4 Execution slots per processor

24 total execution slots

Head node packages data from the computation nodes and presents it in a readable format so that it is usable by the research community

Managing the Cluster

Distributed Resource Management Systems (D-RMS)

–Job management subsystem–Physical resource management subsystem–Scheduling and queuing subsystem

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Dr. Yaohang Li and Dinosolve

• Dinosolve examines a protein sequence of amino acids and determines if the protein can be manipulated by an addition of a disulfide bond

• Each computational result enhances the prediction accuracies for future results

3/21/2013 http://hpcr.cs.odu.edu/dinosolve/index.php

Dinosolve Case Study• Bioinformatics7– Disulfide bond

prediction program

– Disulfide bond creation is important to the research community

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Dinosolve Users• Drug design• Pharmaceutical companies

• Antibody design• To combat viruses

• Bio-energy development• Creation of new fuels to replace diminishing

fossil fuels• Genetic mapping5• Research to cure cancer, HIV, and other diseases

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Accuracy of Popular Tools

Dinosolve DiANNA Scrath Protein Predictor

Accuracy 90.8% 81% 87%

3/21/2013 Reference 13,14 and 15 15

More users use Dinosolve because of the enhanced accuracy

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What is the problem?

• Processing time on big data sets is computationally expensive and as the volume of queries grows the system will progressively drop in performance until the system fails.

• 300 simultaneous requests will cause the web served to crash

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User interface will be improved to be more aesthetically pleasing

Working with DinosolveInput titleInput protein sequenceInput e-mail addressSubmit, then wait for confirmation...

Protein Sequence: string of alphabetic characters, each of which represent a particular amino acid in the protein

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Working with DinosolveConfirmation of requestNow wait for results

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Working with DinosolveCheck your e-mail,Click the link providedThe results are displayed

Dinosolve IssuesAs it continues to grow in popularity, these are expected to occur:

• Hard resources for computation– CPU cycles– Memory– Disk space– Network bandwidth

• Server crashes

Goal is to prepare the system to be able to continue to support the research community in light of its expected growth in requests

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Software

• Unix operating system installed on the Dinosolve cluster

• Dinosolve algorithm• Sun Grid Engine which will be our

Distributed Resource Management System (D-RMS) installed on the cluster.

• MySQL (database software) • Web-based user interface (website)

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Hardware

• MySQL database server• A computer cluster to run the

Dinosolve algorithm• Web server for web-based user

interface

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How will we correct the problem?

Configure a distributed resource management system

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Competing Distributed Resource Management Systems

• Sun Grid Engine (SGE)• Portable Batch System (PBS)• Load Sharing Facility (LSF)

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Dinosolve DiANNA Scrath Protein Predictor

508.22 compliance percentage

67% 85% 67%

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508 compliance

• Amended Rehabilitation Act of 1998–  require Federal agencies to make their

electronic and information technology accessible to people with disabilities [32]

–  enacted to eliminate barriers in information technology, to make available new opportunities for people with disabilities, and to encourage development of technologies that will help achieve these goals [32]

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Why is it important to be compliant?

If an entity wishes to receive government funding then any electronic form the entity uses

must be 508 compliant.

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Objectives

• Interpret and visualize current usage statistics

• Configure, utilize, and optimize the SGE

• Aesthetically pleasing and professional user interface

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What benefits will come from attaining the goals?

• Efficient utilization of available resources• Increased throughput of the cluster• An intuitive and professional user

interface• Rise in popularity due to excellent

accuracy, efficiency, and professional design

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Conclusion

With the updated user interface and correctly configured Sun Grid Engine, Dr. Li hopes to establish a

reputable, reliable, and aesthetically pleasing Disulfide

Bonding Prediction Server.

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References for history1. http://www-01.ibm.com/software/data/bigdata/2. http://en.wikipedia.org/wiki/Big_data3. http

://techcrunch.com/2012/08/22/how-big-is-facebooks-data-2-5-billion-pieces-of-content-and-500-terabytes-ingested-every-day/

4. http://en.wikipedia.org/wiki/Computer_cluster

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References for case study

5.  Li, Y. (2010, September 1). CAREER: Novel Sampling Approaches for Protein Modeling Applications [Abstract]. National Science Foundation Award Abstract #1066471.

6.  Li, Y., & Yaseen, A. (2012). Enhancing Protein Disulfide Bonding Prediction Accuracy with Context-based Features. Biotechnology and Bioinformatics Symposium

7.  bioinformatics. 2011. In Merriam-Webster.com. Retrieved February 15, 2013, from http://www.merriam-webster.com/dictionary/bioinformatics

8. Cronk, J. D. (2012). Disulfide Bond. Retrieved February 15, 2013, from Biochemistry Dictionary: http://guweb2.gonzaga.edu/faculty/cronk/biochem/D-index.cfm?definition=disulfide_bond

9.  Yan, Y., & Chapman, B. (2008). Comparative Study of Distributed Resource Management Systems–SGE, LSF, PBS Pro, and LoadLeveler. Technical Report-Citeseerx.

10. Li, Y., & Yaseen, A. (2012). Dinosolve. Retrieved from http://hpcr.cs.odu.edu/dinosolve/

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References for competition11. Arvind Krishna, “Why Big Data? Why Now?”, IBM , 2011 URL: http://almaden.ibm.com/colloquium/resources/Why%20Big%20Data%20Krishna.PDF12. Yonghong Yan, Barbara M. Chapman, Comparative Study of Distributed Resource Management Systems - SGE, LSF, PBS Pro, and LoadLeveler, Department of Computer Science, University of Houston, May 2005 (pdf)13. Dr. Li’s site http://hpcr.cs.odu.edu/dinosolve/14. Scratch Predictor http://scratch.proteomics.ics.uci.edu/15. DiANNA server http://clavius.bc.edu/~clotelab/DiANNA/Portable Batch System (PBS)16. http://resources.altair.com/pbs/documentation/support/PBSProUserGuide12-2.pdf17. http://www.pbsworks.com/SupportDocuments.aspx?AspxAutoDetectCookieSupport=118. http://resources.altair.com/pbs/documentation/support/PBSProRefGuide12-2.pdf19. http://resources.altair.com/pbs/documentation/support/PBSProAdminGuide12-2.pdf20.http://www.pbsworks.com/(S(tykrsyqbemmlf3o5zwrmjrgf))/images/solutions-en-US/PBS-Pro_Datasheet-USA_WEB.pdf21.http://agendafisica.files.wordpress.com/2011/05/pbs.pdfMoab HPC Suite22.http://www.adaptivecomputing.com/publication/420/wppa_open/IBM Platform LSF23.http://public.dhe.ibm.com/common/ssi/ecm/en/dcd12354usen/DCD12354USEN.PDFApache Hadoop with Zookeeper24. http://zookeeper.apache.org/doc/current/zookeeperOver.html25. http://www.cloud-net.org/~swsellis/tech/solaris/performance/doc/blueprints/0102/jobsys.pdf

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Reference for 508 Compliance

26. http://en.wikipedia.org/wiki/Section_508_Amendment_to_the_Rehabilitation_Act_of_1973

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Appendix• 40: Competition Matrix for Resource Management

Systems• 41-43: 508.22 Compliance Statistics for Dinosolve

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Competing Resource Management Systems

Features of systems

PBS LSF SGE

Supported platforms

Unix Unix & NT Unix

Multi-clustersupport

Yes Yes No

System level checkpoint

restart

No Yes Yes

User level checkpoint

restart

No No Yes

Large computational grid support

No No No

Massive Scalability

Yes Yes Yes

Parallel job support with Sun HPC ClusterTools

Loose Integration

Tight Integration Loose Integration

Distribution format of end

product

Source Binary only Binary and Source

Free? Yes No YesPosix 1002.2d

complianceYes No Yes3/21/2013 Reference 19 41

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