1. Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis Thesis Defense: Ashish Nagavaram Graduate student Computer Science and Engineering Advisor: Dr. Gagan Agrawal Committee: Dr. Rajiv Ramnath Dr. Michael Freitas

2. Introduction  Cloud computing Resources on demand pay-as-you-go Elasticity  Resource Allocation on the cloud Dynamic resource allocation Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 2

3. Motivation  Use elasticity of cloud for executing scientific applications Over provisioning and Under provisioning Avoid wastage of resources  No Generalized scientific workflow to execute application in dynamic fashion  Allocate resources during the execution  Meet time constraints by using more resources Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 3

4. Background-MassMatrix  Developed by Dr. Hua Xu and Dr. Michael Freitas at Ohio State University  A database search program with rapid characterization of proteins and peptides Supports multiple data formats like.mgf,.mzXML and raw data The input database are of the formats.fasta or.BAS Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 4

5. MassMatrix Application Flow Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis Theoretical Protein database Digest the sequence Has the sequence been searched before? Do not add it to the final result Full scan search for finding matching peptides Clear insignificant peptides Statistical analysis to generate results results MS/MS data input file yes no 5

6. Contributions (1/2)  Providing a framework for parallelization of the MassMatrix application  Creating a dynamic workflow Resources are allocated adaptively QOS is achieved by parameter prediction Gives user control by using benefit function Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 6

7. Contributions (2/2)  Allows to specify the time constraint in which the application should be completed  “ A cloud-based Dynamic Workflow for Mass spectrometry Data Analysis” - Ashish Nagavaram, Gagan Agrawal, Michael Freitas, Gaurang Mehta 7 th IEEE Conference on E-Science, Dec 2011 Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 7

8. Outline  Introduction  Motivation  Background  Parallelization of MassMatrix  Adaptive Resource allocation  Experimental Results  Parameter Prediction  Conclusion Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 8

9. Parallel MassMatrix  Parallelize the full-scan search phase Takes the longest time to execute The rest of the phases are sequential  A split-merge approach is followed The user can specify the number of splits Splits are made based on specific tags Index embedded in the file-split name Other options also considered Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 9

10. Parallel MassMatrix (contd.)  Only input file split When we split database also leads to redundant results When split both input and database we have the same problem  The intermediate files are written to disk Pointers serialized Written as comma separated values  A python script keeps polling the job queue to check if the parallel phase has been completed Suspends the sequential phase until then Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 10

11. Parallel MassMatrix (Contd.)  The intermediate files are read back in and re- indexed while merging  The merging process is complicated Complex data structures (matrix of matrices) Have to get inside each data-structure to maximize them Intermediate files are indexed among each other While re-indexing maintain both local and global index The data structures are also re-numbered while merging Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 11

12. Parallel MassMatrix (contd.)  Intermediate files are merged in order of the split they process  Unnecessary intermediate files are not loaded back Saves memory Helps in case of large data files Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 12

13. MassMatrix Flow (Parallel) Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 13 Configuration File Input File Input Database Python Script splitN split2 split1 Sequential phase Merge massmatrix

14. Experimental results (Parallelization) Experimental setup:  8 core Intel Xeon node with 6GB of DDR400 RAM  The theoretical database used was of 20 MB.fasta format database is used  The code was run for 6 different datasets Each had 50,000 records on average Is of.mgf format  Experiments are run for 1, 2, 4 and 8 splits Run on a single node with 8 cores Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 14

15. Experimental results (Parallelization) Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis Execution times when datasets are run for 1, 2, 4 and 8 splits 15

16. Experimental results (Parallelization) Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis Execution times for datasets when run on 1, 2, 4 and 8 cores 16

17. Background (Pegasus)  Used to help creating adaptive version of MassMatrix Is a software system to manage workflows Manages resources on local, grid and cloud Provides API’s to create workflows  Creates a DAG to represent dependencies DAG has a connection between nodes if there is dependency  Creates a plan for the execution of the application Executes application according to this plan. Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 17

18. Background (Condor)  Uses wrangler to start nodes in the cloud New nodes added to cluster automatically Uses Amazon private and public keys to identify user Configuration specified in xml file  Condor is the job scheduler used Developed at University of Wisconsin Jobs are stored in a queue Jobs submitted from queue to the cluster in FIFO Provides fault tolerance through check pointing Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 18

19. The Pegasus workflow Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis Pegasus workflow showing the workflow of MassMatrix Application 19

20. Parallel Pegasus workflow Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis Pegasus workflow for parallel version of MassMatrix application 20

21. Adaptive Resource Allocation  An approach for dynamic resource allocation Decision based on rate of execution Calculates number of additional resources to meet time constraint  Initial assumption that input is divided into equal splits  Decision made on the basis of execution time of initial N splits Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 21

22. Adaptive Resource Allocation (Contd.)  The code initially is run with N resources  For our case we used N=4  Let T per_split be the execution time of a single split  T constraint be the user specified time constraint  Then we can say that T time_constraint = T constraint – ( 2 × T per_split ) (1) Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 22

23. Adaptive Resource Allocation (Contd.)  Another N splits must have already started execution Hence we do not consider them in calculation  Hence if we use N resources the predicted execution time is T execution_pred = T per_split × ( {split_count} - 2 × N )(2) Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 23

24. Adaptive Resource Allocation (Contd.)  Based on equations (1) and (2) we can calculate the number of needed as  Nodes required is the number of additional nodes that need to be spawned Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 24

25. Adaptive Algorithm Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis Algorithm showing the steps involved in calculating the additional resources needed to meet the time constraint 25

26. Experimental Goals  To evaluate efficiency of our system with different datasets  The framework is effective calculates the additional nodes required Meets the time constraints Tested for different time constraints Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 26

27. Experimental results (Adaptive) Experimental setup:  Cloud infrastructure: Amazon EC2  submit host to submit jobs to the cloud  Pegasus version 3.0.2  Condor job scheduler version 7.5.6  Results for 2 datasets and different time constraints Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 27

28. Experimental Results (contd.) Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis Results obtained when algorithm is ran for different time constraints on the dataset1 28

29. Experimental Results (contd.) Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis Results Obtained for dataset2 when run with same time constraints 29

30. Benefit function and Parameter prediction (QOS) Motivation:  Provide Quality of service Tradeoff between execution time vs. quality of results Quality depends on the parameter values Provide a way for the user to control the quality of results Quality defined as equation in terms of parameters  User has flexibility to decide which parameter has more importance  Makes prediction such that execution time is as close as possible to time constraint Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 30

31. Benefit function and Parameter prediction (QOS)  Benefit function - is an equation made of some or all parameters of the application We use this equation to set the parameter importance This is the minimal set of equations needed to obtain the required quality  The goal is to maximize this benefit function within the user specified time constraint Calculated for different parameter combinations  Decision made using tables constructed from data of previous executions Hash tables are used Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 31

32. Benefit function and Parameter prediction (QOS)  Tables contain parameter combination to execution time mappings and vice versa  Multiple datasets can be used for prediction Parameters are mapped to average execution time Reduces error percentage Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 32

33. Parameter prediction process Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 33

34. Experimental Results  Experiments conducted on a Linux desktop machine with 2 cores and 1 GB of memory  The tables are populated using two datasets data1.mgf and data2.mgf  The parameter combinations are predicted for two other datasets data3.mgf and data4.mgf Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 34

35. Experimental Results Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis Parameter Prediction results when run for different Benefit function and constraints 35

36. Experimental Results Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis Parameter Prediction results for a different Benefit Function 36

37. Conclusion  Displayed a framework for dynamic execution of scientific workflows  User specified time constraint can be used to drive the allocation of resources  Effective dynamic allocation  Maximizing Benefit function Parameter prediction within this value Provide quality results based on user requirements Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 37

38. Thank you Cloud based Dynamic workflow with QOS for Mass Spectrometry Data Analysis 38