Introduces the characteristics of clouds and discusses the challenges that these pose for engineering scientific applications on the cloud. Key challenges are programming models, developing PaaS interfaces for high performance/high throughput computing and developing an SDE for cloud programmng.

1. Challenges for cloud software
engineering
Ian Sommerville
St Andrews University
Challenges for Cloud Software Engineering, 2012
Slide 1

2. Why is cloud software engineering
different – or is it?
What needs to be done to make cloud
software engineering easier for the
research community?
Challenges for Cloud Software Engineering, 2012
Slide 2

3. Public, private, community clouds
Challenges for Cloud Software Engineering, 2012
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4. There will be a number of public cloud
providers offering different capabilities at
different prices at different times
Public cloud computing prices will
continue to fall and it will be the most
economical approach
Challenges for Cloud Software Engineering, 2012
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5. Private (institutional) clouds for science
research are not really viable
Community clouds make sense for data-
intensive computing if they are located
close to the sources of the data
Community clouds hosting standard
software may be a useful approach –
possibly hosted on public clouds
Challenges for Cloud Software Engineering, 2012
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6. What makes the cloud different (and
challenging)?
Challenges for Cloud Software Engineering, 2012
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7. Scale
Thousands of nodes that may be
distributed across the world
Challenges for Cloud Software Engineering, 2012
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8. Elasticity
No fixed hardware architecture
Scale out and scale in
Challenges for Cloud Software Engineering, 2012
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9. Pay as you go pricing
Variable costs for applications
Payment according to resources used
Challenges for Cloud Software Engineering, 2012
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10. Cloud services
Challenges for Cloud Software Engineering, 2012
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11. Infrastructure as a service
Challenges for Cloud Software Engineering, 2012
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12. Instead of buying and running physical
equipment (computers, storage, etc.),
you ‘rent’ these as required from a cloud
provider
You ship your programs and data to a
remote data centre and run them there
rather than locally
The familiar Grid computing model
Challenges for Cloud Software Engineering, 2012
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13. Platform as a service
Challenges for Cloud Software Engineering, 2012
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14. The cloud provider makes a set of APIs
available which you use in your program
to interact with the platform
Scaleability is transparent. Automatic
scale out/scale in as demand changes
Primarily geared to writing web-based
applications
Challenges for Cloud Software Engineering, 2012
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15. Software as a service
Challenges for Cloud Software Engineering, 2012
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16. Software is shared by different users and
accessed using a web browser
Services are stateless, functional
abstractions
Multi-tenant databases for efficient
operation
Accessed via a service interface – SOAP
or RESTful interface
Challenges for Cloud Software Engineering, 2012
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17. Levels of cloud usage
Challenges for Cloud Software Engineering, 2012
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18. Cloud hosting
You use the cloud to provide server
capability rather than incurring the capital
costs of server purchase
Applications need not be ‘cloud aware’
Fixed server infrastructure
Can run existing codes in Fortran/C
Challenges for Cloud Software Engineering, 2012
Slide 18

19. If server type and platform available, then
should not require any application
changes
Almost certainly poorer performance
than local execution.
But makes sense for occasional runs/
demonstrations etc.
Challenges for Cloud Software Engineering, 2012
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20. Problems
Relatively low level model of interaction.
You need to know about lots of stuff (such as
certificates, security keys, etc. that have nothing to
do with your work)
Performance is difficult to predict
License issues
Costs may be high for high volumes of
data transfer in/out of clouds
Challenges for Cloud Software Engineering, 2012
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21. Software engineering challenge
Build a software development
environment that radically simplifies
hosting scientific applications on a range
of clouds
Challenges for Cloud Software Engineering, 2012
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22. Cloud interfacing
You build applications that make use of
the cloud providers APIs (PaaS) to access
common services.
‘High-level’ cloud awareness. The platform
takes care of scaleability on demand.
Limited programming language support
Challenges for Cloud Software Engineering, 2012
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23. Challenge is using existing PaaS APIs for
computationally-intensive applications
– Systems are set up to support web-based
applications
– Google Apps – 30 second time limit on tasks
Significant application change required
Several experiments here – mixed
conclusions.
Challenges for Cloud Software Engineering, 2012
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24. Software engineering challenge
Investigate how to adapt applications that
are computation/data intensive to run
within the constraints set by the PaaS
interfaces from cloud providers
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25. Cloud software engineering
Build ‘cloud aware’ applications which can
adapt to the cloud capabilities that are
available
Challenges for Cloud Software Engineering, 2012
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26. Application architecture designed for the
cloud
Underlying infrastructure management
done in the program itself, depending on
performance and cost requirements
What abstractions do we need for this?
Challenges for Cloud Software Engineering, 2012
Slide 26

27. Three grand challenges
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28. Programming models for the cloud
Building a PaaS for high performance/
throughput computing
Cloud-aware software development
environments
Challenges for Cloud Software Engineering, 2012
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29. Scaling up and scaling out
Traditional model to cope with increased
computational demand is to scale up
Supported by some cloud providers who
offer different instance types.
Scaling out is the more usual cloud
model
Challenges for Cloud Software Engineering, 2012
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30. Coping with scaling out
Redesigning algorithms and code to work
with a flexible rather than a fixed
number of resources
– Interaction between program and platform to know
what resources are being used and what resources are
available
– Structuring program into a flexible number of
computational components
– Avoiding sequential computations (Amdahl’s Law)
Challenges for Cloud Software Engineering, 2012
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31. Programming models
Are there programming models that can
take advantage of elasticity and massive
parallelism?
– Invent new models for parallel computation
– Adapt existing problems to the current map/reduce
programming model
Challenges for Cloud Software Engineering, 2012
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32. Map reduce explained
http://www.kchodorow.com/blog/2010/03/15/mapreduce-the-fanfiction/
Challenges for Cloud Software Engineering, 2012
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33. Map-reduce on a slide
-- Split the data into a number of computationally tractable chunks.
in = split (thedata)
n = size (in)
-- MAP. For each chunk, process in parallel
parfor i = 1 to n do
{
out (i) = F (in (i))
}
-- REDUCE. Apply a function R to the output that computes the required
result
-- (of course, A can be a list)
A = R (out)
Challenges for Cloud Software Engineering, 2012
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34. Map-reduce assumptions
Input data set can be split into chunks for
independent parallel processing
The reduce action is a relatively simple,
fast computation
Clearly, does not fit all types of problem
Challenges for Cloud Software Engineering, 2012
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35. Can scientific codes be re-engineered to
take advantage of the map-reduce model?
Elastic + static architecture
Use the elastic cloud for data
preparation/analysis (MAP)
Use a cloud cluster for computation
(REDUCE)
Challenges for Cloud Software Engineering, 2012
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36. Opportunistic computing
Taking advantage of low-price/free
resources when these are available?
Challenges for Cloud Software Engineering, 2012
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37. Fixed budget computation
Compute till you run out of money
Get as much computational capability as
your budget will allow
Use spot prices
Stop and start instances
Challenges for Cloud Software Engineering, 2012
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38. Budget-driven programming
Develop systems that take advantage of
variable pricing in different clouds/at
different times
Potentially significant for
computationally-intensive applications
Challenges for Cloud Software Engineering, 2012
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39. PaaS for Science
Develop a set of APIS for data/compute
intensive applications to support cloud
interaction
Long-running applications
Cost/demand awareness
Performance measurement/tuning
Intermittent execution
Challenges for Cloud Software Engineering, 2012
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40. SDE for the cloud
Creating an environment for the
development of cloud-aware applications
Hiding low level details of IaaS cloud
interaction
Testing environment
Occasional computation
Preliminary work – ELVIRA, St Andrews
http://cloudresearch.jiscinvolve.org/wp/category/projects/
elvira/
Challenges for Cloud Software Engineering, 2012
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41. Software as a service
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42. Effective way for a community to share services
Scope for applying external computations to
collected data – data mining, visualisation, etc.
What are appropriate abstractions?
How can large amounts of state be managed?
Limiting data transfer between nodes
As with all software reuse, the community
rather than the project benefits. Needs central
funding to pay for reusable element creation
Challenges for Cloud Software Engineering, 2012
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43. Conclusions
Clouds will become the dominant computing
environment in future so costs will fall
significantly – major driver for developing for
the cloud
Offers a level of computational resource that
cannot really be replicated in an institution
Software engineering issues need to be
addressed to make the most effective use of
the cloud
Challenges for Cloud Software Engineering, 2012
Slide 43