CloudML and self-adaptive cloud- based applications · Technology for a better society CloudML and...

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s Technology for a better society CloudML and self-adaptive cloud- based applications Alessandro Rossini INF5120 28 Apr 2014

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Page 1: CloudML and self-adaptive cloud- based applications · Technology for a better society CloudML and self-adaptive cloud-based applications Alessandro Rossini INF5120 28 Apr 2014 .

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CloudML and self-adaptive cloud-based applications

Alessandro Rossini

INF5120

28 Apr 2014

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s  

•  Largest research organisation in Scandinavia

•  Technology, Medicine, Social Science

•  Headquarters in Trondheim and Oslo

•  Offices in Norway, Denmark, USA, Brazil, Chile

•  2100 employees from 70 countries

•  1000 employees holding a Ph.D.

•  350 million euros annual turnover

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Alessandro Rossini

Education •  2004-2006 M.Sc., University of L'Aquila, Italy

University of Bergen, Norway •  2008-2011 Ph.D., University of Bergen, Norway Employment •  2007 Software engineer, Integrator, Bergen •  2008-2012 Research fellow, University of Bergen •  2012 Lecturer, Bergen University College •  2012- Research scientist, SINTEF, Oslo

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Cloud computing

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Cloud computing explained

http://www.youtube.com/watch?v=QJncFirhjPg

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Cloud computing

A computing model enabling ubiquitous network access to a shared and virtualised pool of

computing capabilities (e.g., network, storage, processing, and memory) that can be rapidly provisioned with minimal management effort

source: NIST

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•  source: Graviant

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Applica'ons  

Data  

Run-­‐'me  

OS  

Virtualisa'on  

Servers  

Storage  

Network  

Applica'ons  

Data  

Run-­‐'me  

OS  

Virtualisa'on  

Servers  

Storage  

Network  

Applica'ons  

Data  

Run-­‐'me  

OS  

Virtualisa'on  

Servers  

Storage  

Network  

Consum

er  

Provider  

Consum

er  

Provider  

Provider  

IaaS PaaS SaaS

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Cloudify   Cloud  Foundry   Scalr  

jclouds   Simple  Cloud   Deltacloud  

Amazon  AWS   OpenStack   vCloud  

Amazon  AWS   Rackspace  

IBM  SmartCloud   Terramark  

…  

…  

…  

…  

Frameworks  

Libraries  

Stacks  

PaaS  

IaaS  

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Benefits

•  Scalability

•  Performance

•  Availability

•  Cost

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Challenges

•  Heterogeneity

•  Incompatibility

•  Lack of standards

•  Vendor lock-in

•  Self-adaptation

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Self-adaptation

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Wed,  19  Feb  Facebook  acquires  

WhatsApp  

Sat,  22  Feb  1.8  million  users  

register  to  Telegram…  

…and  Telegram  crashes…  

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Self-adaptive cloud-based applications

Cloud-based applications that self-adapt to changes in environment and requirements

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Dynamically adaptive systems

•  Continuous design and adaptation

•  Short-term changes in execution environment

•  Long-term changes in system requirements

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CloudML

•  Domain-specific language (DSL) for modelling the provisioning and deployment of multi-cloud applications at design-time

•  Models@run-time environment for enacting the provisioning and deployment of these applications at run-time

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Design-time

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Flexiant  [loca'on:  UK]  

SINTEF  (OpenStack)  [loca'on:  NO]  

No'fier  

Tomcat:SC  

SL:VM  

Dispatcher  

Tomcat:SC  

LL:VM  

MongoDB  

VM:  Virtual  machine  SC:  Servlet  container  LB:  Load  balancer  SL:  Small  GNU/Linux  ML:  Medium  GNU/Linux  LL:  Large  GNU/Linux  

[elas'city:  1..4  response  'me  <  50ms]  

[elas'city:  1..8]  

[elas'city:  1..8]  

Design-time deployment model

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Models@run-time

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Models@run-time

•  Model causally connected to the running system

•  A change in the model is reflected on-demand in the running system

•  A change in the running system is automatically reflected in the model

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Models@run-­‐.me  

Execu'on  environments  

Current  model  

Adapta'on  engine  

Reasoning  engine  

Target  model  

Diff  

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Flexiant  [loca'on:  UK]  

SINTEF  (OpenStack)  [loca'on:  NO]  

LB  

LB  LB  

1:No'fier  

2:Tomcat  

1:SL  

1:Dispatcher  

1:Tomcat  

1:LL  

1:MongoDB   2:MongoDB  

VM:  Virtual  machine  SC:  Servlet  container  LB:  Load  balancer  SL:  Small  GNU/Linux  ML:  Medium  GNU/Linux  LL:  Large  GNU/Linux  

Run-time deployment model

[response  'me  =  20ms]  

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Flexiant  [loca'on:  UK]  

SINTEF  (OpenStack)  [loca'on:  NO]  

LB  

LB  LB  

1:No'fier  

2:Tomcat  

1:SL  

1:Dispatcher  

1:Tomcat  

1:LL  

1:MongoDB   2:MongoDB  

VM:  Virtual  machine  SC:  Servlet  container  LB:  Load  balancer  SL:  Small  GNU/Linux  ML:  Medium  GNU/Linux  LL:  Large  GNU/Linux  

Run-time deployment model

[response  'me  =  80ms]  

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Flexiant  [loca'on:  UK]  

SINTEF  (OpenStack)  [loca'on:  NO]  

LB  

LB  LB  

1:No'fier  

2:Tomcat  

1:SL  

1:Dispatcher  

1:Tomcat  

2:Dispatcher  

3:Tomcat  

1:LL  

1:MongoDB   2:MongoDB  

VM:  Virtual  machine  SC:  Servlet  container  LB:  Load  balancer  SL:  Small  GNU/Linux  ML:  Medium  GNU/Linux  LL:  Large  GNU/Linux  

Run-time deployment model

[response  'me  =  80ms]  

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Flexiant  [loca'on:  UK]  

SINTEF  (OpenStack)  [loca'on:  NO]  

LB  

LB  LB  

1:No'fier  

2:Tomcat  

2:No'fier  

4:Tomcat  

1:SL  

1:Dispatcher  

1:Tomcat  

2:Dispatcher  

3:Tomcat  

1:LL  

1:MongoDB   2:MongoDB  

VM:  Virtual  machine  SC:  Servlet  container  LB:  Load  balancer  SL:  Small  GNU/Linux  ML:  Medium  GNU/Linux  LL:  Large  GNU/Linux  

Run-time deployment model

[response  'me  =  20ms]  

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Metamodel

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Metamodel (type portion)

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EU Projects

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Resources

•  cloudml.org

•  github.com/SINTEF-9012/cloudml/

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Thank you!

More info:

cloudml.org

Get in touch:

@alerossini

alessandrorossini.org