SCALABLE PARALLEL COMPUTING ON CLOUDS : EFFICIENT AND SCALABLE ARCHITECTURES TO PERFORM PLEASINGLY PARALLEL, MAPREDUCE AND ITERATIVE DATA INTENSIVE COMPUTATIONS ON CLOUD ENVIRONMENTS Thilina Gunarathne
Figure 1: A sample MapReduce execution flow Figure 2: Steps of a typical MapReduce computation
Map Task Task Scheduling Data read
Map execution Collect Reduce Task
Spill Merge Shuffle
Merge Reduce Write
Execution output Figure 3: Structure of a typical dataintensive iterative application
X: Calculate invV Reduce Merge Map (BX) New Iteration
Calculate Stress Map Reduce
Merge Figure 5 : Bio sequence analysis pipeline Figure 6: Classic cloud processing architecture for pleasingly parallel computations
Figure 7: Hadoop MapReduce based processing model for pleasingly parallel computations Figure 8 Cap3 application execution cost with different EC2 instance types
Figure 9 : Cap3 applciation compute time with different EC2 instance types C o m p u te T im e ( s )
2000 1500 1000
500 0 C a p 3 C o m p u te T i m e
Figure 10: Parallel efficiency of Cap3 application using the pleasingly parallel frameworks Figure 11: Cap3 execution time for single file per core using the pleasingly parallel frameworks
Figure 12 : Cost to process 64 BLAST query files on different EC2 instance types Figure 13 : Time to process 64 BLAST query files on different EC2 instance types 2500
B L A S TC o m p u te T i m e C o m p u te T im e ( s ) 2000 1500
1000 500 0 Figure 14: Time to process 8 query files using BLAST application on different Azure instance types
Figure 15 : BLAST parallel efficiency using the pleasingly parallel frameworks Figure 16 : BLAST average time to process a single query file using the pleasingly parallel
frameworks Figure 17 : Cost of using GTM interpolation application with different EC2 instance types Figure 18 : GTM Interpolation compute time
with different EC2 instance types 600 G T M C o m p u te T i m e C o m p u te T im e ( s )
500 400 300 200 100 0
Figure 19: GTM Interpolation parallel efficiency using the pleasingly parallel frameworks Figure 20 : GTM Interpolation performance per core using the pleasingly parallel frameworks Figure 21: MapReduceRoles4Azure: Architecture for
implementing MapReduce frameworks on Cloud environments using cloud infrastructure services Figure 22: Task decomposition mechanism of SWG pairwise distance calculation MapReduce application
Figure 29: Cap3 MapReduce computational cost in cloud infrastructures Figure 30: Twister4Azure iterative MapReduce programming model
Job Start Map Combine
Map Combine Reduce
Merge Add Iteration? Broadcast
Map Combine Reduce
Data Cache Hybrid scheduling of the new iteration Yes No
Job Finish Figure 31: Cache Aware Hybrid Scheduling Figure 32: Twister4Azure tree based broadcast
over TCP with Azure Blob storage as the persistent backup. Blob Storage Workers
N3 N3 N2 N4 N5
N1 N1 N6 N10
Figure 33: MDS weak scaling. Workload per core is constant. Ideal is a straight horizontal line Figure 34: MDS Data size scaling using 128 Azure small instances/cores, 20 iterations
Figure 35: Twister4Azure Map Task histogram for MDS of 204800 data points on 32 Azure Large Instances (graphed only 10 iterations out of 20). Two adjoining bars represent an iteration (2048 tasks per iteration), where each bar represent the different applications inside the iteration.
Figure 36: Number of executing Map Tasks in the cluster at a given moment. Two adjoining bars represent an iteration. Figure 37: KMeans Clustering Scalability. Relative parallel efficiency of strong scaling using 128 million data points.
Figure 38: KMeansClustering Scalability. Weak scaling. Workload per core is kept constant (ideal is a straight horizontal line). Figure 39: Twister4Azure Map Task execution time
histogram for KMeans Clustering 128 million data points on 128 Azure small instances. Figure 40: Twister4Azure number of executing Map Tasks in the cluster at a given moment
Figure 41: Performance of SW-G for randomly distributed inhomogeneous data with 400 mean sequence length. Figure 42: Performances of SW-G for skewed distributed inhomogeneous data with 400
mean sequence length Figure 43: Performance of Cap3 for random distributed inhomogeneous data. Figure 44: Performance of Cap3 for skewed
distributed inhomogeneous data Figure 45: Virtualization overhead of Hadoop SW-G on Xen virtual machines Figure 46: Virtualization overhead of Hadoop
Cap3 on Xen virtual machines Figure 47: Sustained performance of cloud environments for MapReduce type of applications Figure 48: Execution traces of Twister4Azure MDS
Using in-memory caching on small instances. (The taller bars represent the MDSBCCalc computation, while the shorter bars represent the MDSStressCalc computation and together they represent an iteration. ) Figure 49: Execution traces of Twister4Azure MDS using Memory-Mapped file based caching on Large
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