1. Some Scientific Challenges in the Cloud
Roberto Di Cosmo
University Paris Diderot, and INRIA
May 20, 2010
GTLL
Paris
Roberto Di Cosmo () Scientific challenges in the Cloud May 20 2010 / Paris 1/5

2. Cloud Computing: basic definitions
Three levels:
Layer Main target Abstraction
Software as a Service (SaaS) Final users Application
Platform as a Service (PaaS) App Developers System
Infrastructure as a Service (IaaS) Platform Developers HW
Roberto Di Cosmo () Scientific challenges in the Cloud May 20 2010 / Paris 2/5

3. Cloud Computing: basic definitions
Three levels:
Layer Main target Abstraction
Software as a Service (SaaS) Final users Application
Platform as a Service (PaaS) App Developers System
Infrastructure as a Service (IaaS) Platform Developers HW
Key concepts: virtualization
Roberto Di Cosmo () Scientific challenges in the Cloud May 20 2010 / Paris 2/5

4. Cloud Computing: basic definitions
Three levels:
Layer Main target Abstraction
Software as a Service (SaaS) Final users Application
Platform as a Service (PaaS) App Developers System
Infrastructure as a Service (IaaS) Platform Developers HW
Key concepts: virtualization + flexibility
Roberto Di Cosmo () Scientific challenges in the Cloud May 20 2010 / Paris 2/5

5. Cloud Computing: basic definitions
Three levels:
Layer Main target Abstraction
Software as a Service (SaaS) Final users Application
Platform as a Service (PaaS) App Developers System
Infrastructure as a Service (IaaS) Platform Developers HW
Key concepts: virtualization + flexibility + automation
Roberto Di Cosmo () Scientific challenges in the Cloud May 20 2010 / Paris 2/5

6. Cloud Computing: basic definitions
Three levels:
Layer Main target Abstraction
Software as a Service (SaaS) Final users Application
Platform as a Service (PaaS) App Developers System
Infrastructure as a Service (IaaS) Platform Developers HW
Key concepts: virtualization + flexibility + automation
Erich Clementi, IBM’s Cloud Initiative head says it well:
Many people equate cloud computing to virtualization.
It is not virtualization.
To get the value you need standardization and automation on
top of that virtualization.
Roberto Di Cosmo () Scientific challenges in the Cloud May 20 2010 / Paris 2/5

7. Cloud Computing: basic definitions
Three levels:
Layer Main target Abstraction
Software as a Service (SaaS) Final users Application
Platform as a Service (PaaS) App Developers System
Infrastructure as a Service (IaaS) Platform Developers HW
Key concepts: virtualization + flexibility + automation
Erich Clementi, IBM’s Cloud Initiative head says it well:
Many people equate cloud computing to virtualization.
It is not virtualization.
To get the value you need standardization and automation on
top of that virtualization.
Let’s sketch some scientific challenges at each level.
Roberto Di Cosmo () Scientific challenges in the Cloud May 20 2010 / Paris 2/5

8. Challenges in IaaS
Optimization of resources:
Roberto Di Cosmo () Scientific challenges in the Cloud May 20 2010 / Paris 3/5

9. Challenges in IaaS
Optimization of resources:
share common data in disk and memory (see Decentralized
Deduplication in SAN Cluster File Systems, Clements et al., and
Satori: Enlightened page sharing, Milos et al. USENIX ’09)
optimal VM placement
Roberto Di Cosmo () Scientific challenges in the Cloud May 20 2010 / Paris 3/5

10. Challenges in IaaS
Optimization of resources:
share common data in disk and memory (see Decentralized
Deduplication in SAN Cluster File Systems, Clements et al., and
Satori: Enlightened page sharing, Milos et al. USENIX ’09)
optimal VM placement
Security:
Roberto Di Cosmo () Scientific challenges in the Cloud May 20 2010 / Paris 3/5

11. Challenges in IaaS
Optimization of resources:
share common data in disk and memory (see Decentralized
Deduplication in SAN Cluster File Systems, Clements et al., and
Satori: Enlightened page sharing, Milos et al. USENIX ’09)
optimal VM placement
Security:
avoiding escapes
Roberto Di Cosmo () Scientific challenges in the Cloud May 20 2010 / Paris 3/5

12. Challenges in IaaS
Optimization of resources:
share common data in disk and memory (see Decentralized
Deduplication in SAN Cluster File Systems, Clements et al., and
Satori: Enlightened page sharing, Milos et al. USENIX ’09)
optimal VM placement
Security:
avoiding escapes
avoiding mapping of the physical infrastructure, and its exploitation
http://cseweb.ucsd.edu/~hovav/dist/cloudsec.pdf
Roberto Di Cosmo () Scientific challenges in the Cloud May 20 2010 / Paris 3/5

13. Challenges in PaaS
Ensuring flexibility, avoiding vendor lock-in, is not easy.
Roberto Di Cosmo () Scientific challenges in the Cloud May 20 2010 / Paris 4/5

14. Challenges in PaaS
Ensuring flexibility, avoiding vendor lock-in, is not easy.
Roberto Di Cosmo () Scientific challenges in the Cloud May 20 2010 / Paris 4/5

15. Challenges in PaaS
Ensuring flexibility, avoiding vendor lock-in, is not easy.
N.B.: (re)writing applications for a platform/middleware is not neutral.
Roberto Di Cosmo () Scientific challenges in the Cloud May 20 2010 / Paris 4/5

16. Transversal Challenges
Several challenges are transversal:
security:
who can break (into) the system?
Roberto Di Cosmo () Scientific challenges in the Cloud May 20 2010 / Paris 5/5

17. Transversal Challenges
Several challenges are transversal:
security:
who can break (into) the system?
data ownership:
who can access my data?
Roberto Di Cosmo () Scientific challenges in the Cloud May 20 2010 / Paris 5/5

18. Transversal Challenges
Several challenges are transversal:
security:
who can break (into) the system?
data ownership:
who can access my data? please do not simply rely on contracts!
Roberto Di Cosmo () Scientific challenges in the Cloud May 20 2010 / Paris 5/5

19. Transversal Challenges
Several challenges are transversal:
security:
who can break (into) the system?
data ownership:
who can access my data? please do not simply rely on contracts!
efficient implementation:
abstraction is nice, but can it accomodate optimal implementation?
Roberto Di Cosmo () Scientific challenges in the Cloud May 20 2010 / Paris 5/5

20. Transversal Challenges
Several challenges are transversal:
security:
who can break (into) the system?
data ownership:
who can access my data? please do not simply rely on contracts!
efficient implementation:
abstraction is nice, but can it accomodate optimal implementation?
interoperability:
SaaS and PaaS try to hide the complexity of the lower layer, but at
the price of vendor or technology lock-in
Roberto Di Cosmo () Scientific challenges in the Cloud May 20 2010 / Paris 5/5

21. Transversal Challenges
Several challenges are transversal:
security:
who can break (into) the system?
data ownership:
who can access my data? please do not simply rely on contracts!
efficient implementation:
abstraction is nice, but can it accomodate optimal implementation?
interoperability:
SaaS and PaaS try to hide the complexity of the lower layer, but at
the price of vendor or technology lock-in
Bottomline: there is a lot of work ahead.
Roberto Di Cosmo () Scientific challenges in the Cloud May 20 2010 / Paris 5/5