Overview

Summary

Workflow

Data

Cloud platform

Issues/Gaps

A study of cost and performance of the application of cloud computing to Astronomy ¹

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Part I - Performance of three workflow applications

Summary

• For CPU-bound applications, virtualization overhead on Amazon EC2 is generally small.
• The resources offered by EC2 are generally less powerful than those available in HPC. Particularly for I/O-bound applications.
• Amazon EC2 offers no cost benefit over locally hosted storage, but does eliminate local maintenance and energy costs, and does offer high-quality, reliable storage.
• As a result, commercial clouds may not be best suited for large-scale computations ^c.

Cloud platform

• Cloud platform: Amazon EC2 (http://aws.amazon.com/ec2/)
• Workflow ^a applications
  Three different workflow applications are chosen.
  • Montage (http://montage.ipac.caltech.edu) from astronomy: 10,429 tasks, read 4.2 GB of input data, and produced 7.9 GB of output data.
    I/O-bound because it spends more than 95% of its time waiting on I/O operations
  • Broadband (http://scec.usc.edu/research/cme) from seismology: 320 tasks, read 6 GB of input data, and produced 160 MB of output data.
    Memory-limited because more than 75% of its runtime is consumed by tasks requiring more than 1 GB of physical memory.
  • Epigenome (http://epigenome.usc.edu) from biochemistry: 81 tasks, read 1.8 GB of input data, and produced 300 MB of output data.
    CPU-bound because it spends 99% of its runtime in the CPU and only 1% on I/O and other activities.
• Methods
  The experiments were all run on single nodes to provide an unbiased comparison of the performance of workflows on Amazon EC2 and Abe.

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1. Montage (I/O-bound)
   The processing times on abe.lustre are nearly three times faster than the fastest EC2 machines ^b.
2. Broadband (Memory-bound)
   The processing advantage of the parallel file system largely disappears. And abe.local's performance is only 1% better than cl.xlarge.
   For memory-intensive application, Amazon EC2 can achieve nearly the same performance as Abe.
3. Epigenome (CPU-bound)
   The parallel file system in Abe provides no processsing advantage for Epigenome. The machines with the most cores gave the best performance for CPU-bound application.

Issues/Gaps

• Storage Cost: Cost to store VM images in S3 and cost of storing input data in EBS.
  The table summarizes the monthly storage cost

  Application	Input Volume	Monthly Storage Cost
  Montage	4.3 GB	$0.66
  Broadband	4.1 GB	$0.66
  Epigenome	1.8 GB	$0.26

• Transfer cost: AmazonEC2 charges $0.10 per GB for transter into the cloud and $0.17 per GB for transfer out of the cloud.
  The data size and transfer costs are summarized in the tables below.
  Data transfer size per workflow on Amazon EC2

  Application	Input	Output	Logs
  Montage	4,291 MB	7,970 MB	40 MB
  Broadband	4,109 MB	159 MB	5.5 MB
  Epigenome	1,843 MB	299 MB	3.3 MB

  Costs of transferring data into and out the EC2 cloud

  Application	Input	Output	Logs	Total
  Montage	$0.42	$1.32	$<0.01	$1.75
  Broadband	$0.40	$0.03	$<0.01	$0.43
  Epigenome	$0.18	$0.05	$<0.01	$0.23

• Cost effectiveness study
  Cost calculations based on processing reqeusts for 36,000 mosaic of 2MASS images (Total size 10TB) of size 4 sq deg over a period of three years (typical workload for image mosaic service).
  Results show that Amazon EC2 is much less attractive than a local service for I/O-bound application due to the high costs of data storage in Amazon EC2.

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Seeking Supernovae in the Clouds: A Performance Study ²

Summary

Nearby Supernova Factory(SNfactory) experiment measures the expansion history of the Universe to explore the nature of Dark Energy with Type Ia supernovae. SNfactory is a pipeline of serial processes executing various image processing algorithms in parallel on ~10TBs of data. SNfactory is ported to Amazon Web Services environment.

Cloud platform

• Cloud Platform: Amazon Web Services
  • EC2 32-bit highCPU medium instances (c1.mediu: 2 virtual cores, 2.5 ECU each)
  • 80-core runs were used.
• Design: Port the environment into EC2 first, then decide the location of data and the size of compute resource.
• Setup virtual cluster in EC2. Create EBS volume for shared file system.
• Data size:
  • Raw data: 10TB
  • Processed data: 20TB

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• EBS vs S3
  • In the 80-core experiment, a run of processing took ~7 hours for EBS variants and only 3 hours for S3.
  • Output data loading time into S3 is an order of magnitude smaller than into EBS.
  • Cost: data transfers between EC2 and S3 are free ^d. S3 storage is better than EBS for SNfactory.
  • Input data and application data will be stored in EBS and output data will be writen to S3.

Issues/Gaps

• Need to replicate HPC cluster environment in EC2 or the application must be modified.
• Mean rate of failure is higher in EC2 than in traditional cluster environments which needs to be handled.
• Inability to acquire all of the VMI requested because insufficient resources are available, so need to modify the application to adapt this.
• Transient errors.

Application of Cloud computing to the creation of image mosaic and management of their provenance ³

Summary

Workflow

Data

Cloud platform

Cloud performance

Issues/Gaps

Scientific workflow applications on Amazon EC2 ⁴

Summary

Workflow

Data

Cloud platform

Cloud performance

Issues/Gaps

Data Sharing Options for Scientific Workflows on Amazon EC2 ⁵

Summary

Workflow

Data

Cloud platform

Cloud performance

Issues/Gaps

Using MapReduce for Image Coaddition ⁶

Summary

Workflow

Data

Cloud platform

Cloud performance

Issues/Gaps

CANFAR: Canadian Advanced Network for Astronomical Research ⁷

Summary

Workflow

Data

Cloud platform

Cloud performance

Issues/Gaps

A Multi-Dimensional Classification model for Scientifc workflow Characteristics ⁸

Summary

Workflow

Data

Cloud platform

Cloud performance

Issues/Gaps

Trident Scientific Workflow Workbench for Data Management in the cloud ⁹

Summary

Workflow

Data

Cloud platform

Cloud performance

Issues/Gaps

On the use of cloud computing for scientific workflows ¹⁰

Summary

Workflow

Data

Cloud platform

Cloud performance

Issues/Gaps

Experiences using cloud computing for a scientific workflow application ¹¹

Summary

Workflow

Data

Cloud platform

Cloud performance

Issues/Gaps

References

1. Berriman, G.B. et al. Sixth IEEE International Conference on e-Science, 1-7 (2010)
2. Jackson, K.R. et al. Proc. ACM Int. Symp. HPDC, 421-429 (2010)
3. Berriman, G.B. et al. SPIE Conference 7740: Software and Cyberinfrastructure for Astronomy (2010)
4. Juve, G. et al. Cloud Computing Workshop in Conjunction with e-Science Oxford, UK: IEEE (2009)
5. Juve, G. et al. SC(2010)
6. Wiley, K. et al. Publications of the Astronomical Society of the Pacific 123 366-380 (2011)
7. Gaudet, S. et al. Proc SPIE (2010)
8. Ramakrishnan, L. et al. Wands '10 (2010)
9. Simmhan, Y. et al. ADVCOMP 09' (2009)
10. Hoffa, C. et al. ESCIENCE 08' (2008)
11. Vockler, J. et al. ScienceCloud '11 (2011)

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