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KARBON PRESENTATION 8 • 24LEVEL 13, 82 SPENCER ST, MELBOURNE
IMMERSE YOURSELF
a Peer-assisted cloud
storage System
A. DAvoli
Sapienza University of Rome
davoli@di.uniroma1.it
Principles and practice on Evenutal consistency
(PAPEC) @ EuROSYS 2014
04/13/2014
TRITON
Cloud Storage DrawBacks (1):
REliability AND Availability
Antonio Davoli Triton@PaPEC 2014 • 2
Cloud Storage DrawBacks (2):
Security AND Privacy
Antonio Davoli Triton@PaPEC 2014 • 3
Cloud Storage DrawBacks (3):
CAPacity AND Concurrency
Antonio Davoli Triton@PaPEC 2014 • 4
Triton: a Peer-Assisted Cloud storage System
• Triton clients are interconnected through a P2P Network and they can
achieve fast interactions by exchanging data directly among
themselves.
• In addition, they share a Cloud Storage Resource as rendevouz point
for update operations.
Antonio Davoli
Power oF cloud + benefits of peer-to-peer (p2p) networks
Triton@PaPEC 2014 • 5
Hybrid Cloud
Delivering
Static COntents
This hybrid architecture is often employed only
in static content distribution systems, such as
television and music streaming.
Antonio Davoli Triton@PaPEC 2014 • 6
Hybrid Cloud
Dynamic COntents
‣ What happens when users need to
modify their shared data?
‣ How can we exploit this new Way of
thinking about Cloud infrastructure?
Antonio Davoli Triton@PaPEC 2014 • 7
Triton GOALS
• Increase Access PErformance to Shared Information,
• Provide a Mechanism to Synchronize access to contents,
• Decrease Inconsistency window while waiting For cloud updates,
• Decrease trust on cloud Storage pRoviders.
Antonio Davoli Triton@PaPEC 2014 • 8
Triton Design (1)
• Physical Separation BETWEEN Files
information and files Data Blocks
Files are shared among the users. However,
in the cloud storage are uploaded only the
meta-information about file. (e.g. list of
peers, hashes of blocks composing the file).
Antonio Davoli Triton@PaPEC 2014 • 9
Triton design (2)
• Files are split in data blocks
Update Result
H1 B1 B2
H1
B2
B1
B2
B1
Antonio Davoli Triton@PaPEC 2014 • 10
After a write/update operation, the peer uploads
on the cloud the information (hashes) of modified
blocks and pushes toward the other peers the
changes that must be applied (binary-diffs).
Request Proxy Req Commit
Node 1
Node 2
Node 3
Node 4
Node 5
Triton Write Operations
Antonio Davoli
• agreement Protocol to
Serialize Accesses to
shared REsources.
Considering that often peers are
online only for a limited amount
of time, it is essential to achieve
a convergence soon.
Triton@PaPEC 2014 • 11
Triton Write Operations
• Three-pHases agreement Protocol for Update Operations
1. An user who wants to update a file creates a request message and broadcasts it to
other users who share the file.
2. Then, the request is spread among the users where each one confirm to the others
that has recevied and agree on that request (proxy-request),
3. Finally, when the user receives a quorum of messages starts the commit phase.
Antonio Davoli Triton@PaPEC 2014 • 12
Request Proxy Req Commit
Node 1
Node 2
Node 3
Node 4
Node 5
Triton Read Operations
• Read Operations Can be executed In parallel (if they do not conflict
with ongoing writes)
Peers are aware of ongoing operations, thus it is possible to provide an read-only
access to all the file blocks that are not involved in some update rounds.
Antonio Davoli Triton@PaPEC 2014 • 13
Triton Consistency
By splitting file in smaller blocks and by working
with rolling hashes, Triton reduces the amount of
data exchanged by peers after a file update.
• Smaller Blocks
• Pushing Updates
By pushing information to other peers, Triton
reduces the amount of time to wait for the
information to be updated on the cloud.
Antonio Davoli Triton@PaPEC 2014 • 14
Experimental Results
Antonio Davoli Triton@PaPEC 2014 • 15
Experimental Results
We built Triton as a client library and created a file system (in user space) based
on the protocol’s access policies. In this way, we guarantee an interoperability
with several cloud storage providers.
Many other systems (e.g. Depot [1]) require to run part of the code on a
centralized server, Triton instead code runs only on the clients.
Antonio Davoli
[1] Mahajan, P. et al. - Cloud Storage with minimal trust, OSDI’10
Triton@PaPEC 2014 • 16
Antonio Davoli Triton@PaPEC 2014 • 17
Triton Latency for agreement
• Up to 8 Replicas
on a Intel Xeon
Machine
• Up to 8k Payload
Antonio Davoli Triton@PaPEC 2014 • 18
Triton Performance Results
• 6 Clients,
• 5 Amazon Ec2 (Oregon)
• 1 Sapienza Reg Elena
CS Dept. (Rome)
What’s Next?
Antonio Davoli Triton@PaPEC • 19
Conclusions & Future Work
Triton is a peer-assisted cloud storage solution that merges the benifits of a
P2P in the design of a cloud storage system. It contributes to improve
performance and security concerns of classical storage systems.
• Improve Experimental Results by employing a larger set of peers,
• Enhance Triton model by applying a Game Theoric Approach for PEers
BehaviorS (Rational and Selfish Peers),
• Release the Library Prototype + Integration with p2p Social Nets.
Antonio Davoli Triton@PaPEC 2014 • 19
Antonio Davoli On a New Generation of Cloud Storage Systems• 28
Thanks for your attention.
davoli@di.uniroma1.it
On a New Generation of Cloud Storage System • 37Antonio Davoli
Triton Backup Slides
Practical Byzantine Fault Tolerance (BFT)
SPECULATIVE BFT: Zyzzyva
Triton : Request
< REQUEST, t, o, n, H, timestamp@c, c >c
• t, topic related to the file
• o, operations to apply,
• n, sequence nubmer,
• H, is hash of history,
• timestamp@c is the timestamp for the op,
• c, user ID.
Triton: A peer-assisted cloud storage systems
Triton : Proxy-Request
< PROXY − REQ, t, o, n, H, timestamp@c, c, p >p
• t, topic related to the file
• o, operations to apply,
• n, sequence nubmer,
• H, is hash of history,
• timestamp@c is the timestamp for the op,
• p, the request to forward (collection),
• c, user ID.
Triton : Commit
< COMMIT, t, o, n, H, R, timestamp@c, c, p >p
• t, topic related to the file
• o, operations to apply,
• n, sequence nubmer,
• H, is hash of history,
• timestamp@c is the timestamp for the op,
• p, collection of proxy-requests ,
• c, user ID.
KARBON PRESENTATION 7 • 24LEVEL 13, 82 SPENCER ST, MELBOURNE
AWESOME PRESENTATION TEMPLATE
CAP Theorem
CAP Theorem: In a network subject to communication failures, it is impossible
for any web service to implement an atomic read/write shared memory that
guarantees a response to every request.
KARBON PRESENTATION 7 • 24LEVEL 13, 82 SPENCER ST, MELBOURNE
AWESOME PRESENTATION TEMPLATE
CAP Theorem (2)
CAP Theorem: Any networked shared-data system can have at most two
of three desirable properties:
• COncistency (C),
• HIgh Availability (A),
• tolerance to network partitions (P)

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Triton: A peer-assisted cloud storage systems

  • 1. KARBON PRESENTATION 8 • 24LEVEL 13, 82 SPENCER ST, MELBOURNE IMMERSE YOURSELF a Peer-assisted cloud storage System A. DAvoli Sapienza University of Rome davoli@di.uniroma1.it Principles and practice on Evenutal consistency (PAPEC) @ EuROSYS 2014 04/13/2014 TRITON
  • 2. Cloud Storage DrawBacks (1): REliability AND Availability Antonio Davoli Triton@PaPEC 2014 • 2
  • 3. Cloud Storage DrawBacks (2): Security AND Privacy Antonio Davoli Triton@PaPEC 2014 • 3
  • 4. Cloud Storage DrawBacks (3): CAPacity AND Concurrency Antonio Davoli Triton@PaPEC 2014 • 4
  • 5. Triton: a Peer-Assisted Cloud storage System • Triton clients are interconnected through a P2P Network and they can achieve fast interactions by exchanging data directly among themselves. • In addition, they share a Cloud Storage Resource as rendevouz point for update operations. Antonio Davoli Power oF cloud + benefits of peer-to-peer (p2p) networks Triton@PaPEC 2014 • 5
  • 6. Hybrid Cloud Delivering Static COntents This hybrid architecture is often employed only in static content distribution systems, such as television and music streaming. Antonio Davoli Triton@PaPEC 2014 • 6
  • 7. Hybrid Cloud Dynamic COntents ‣ What happens when users need to modify their shared data? ‣ How can we exploit this new Way of thinking about Cloud infrastructure? Antonio Davoli Triton@PaPEC 2014 • 7
  • 8. Triton GOALS • Increase Access PErformance to Shared Information, • Provide a Mechanism to Synchronize access to contents, • Decrease Inconsistency window while waiting For cloud updates, • Decrease trust on cloud Storage pRoviders. Antonio Davoli Triton@PaPEC 2014 • 8
  • 9. Triton Design (1) • Physical Separation BETWEEN Files information and files Data Blocks Files are shared among the users. However, in the cloud storage are uploaded only the meta-information about file. (e.g. list of peers, hashes of blocks composing the file). Antonio Davoli Triton@PaPEC 2014 • 9
  • 10. Triton design (2) • Files are split in data blocks Update Result H1 B1 B2 H1 B2 B1 B2 B1 Antonio Davoli Triton@PaPEC 2014 • 10 After a write/update operation, the peer uploads on the cloud the information (hashes) of modified blocks and pushes toward the other peers the changes that must be applied (binary-diffs).
  • 11. Request Proxy Req Commit Node 1 Node 2 Node 3 Node 4 Node 5 Triton Write Operations Antonio Davoli • agreement Protocol to Serialize Accesses to shared REsources. Considering that often peers are online only for a limited amount of time, it is essential to achieve a convergence soon. Triton@PaPEC 2014 • 11
  • 12. Triton Write Operations • Three-pHases agreement Protocol for Update Operations 1. An user who wants to update a file creates a request message and broadcasts it to other users who share the file. 2. Then, the request is spread among the users where each one confirm to the others that has recevied and agree on that request (proxy-request), 3. Finally, when the user receives a quorum of messages starts the commit phase. Antonio Davoli Triton@PaPEC 2014 • 12 Request Proxy Req Commit Node 1 Node 2 Node 3 Node 4 Node 5
  • 13. Triton Read Operations • Read Operations Can be executed In parallel (if they do not conflict with ongoing writes) Peers are aware of ongoing operations, thus it is possible to provide an read-only access to all the file blocks that are not involved in some update rounds. Antonio Davoli Triton@PaPEC 2014 • 13
  • 14. Triton Consistency By splitting file in smaller blocks and by working with rolling hashes, Triton reduces the amount of data exchanged by peers after a file update. • Smaller Blocks • Pushing Updates By pushing information to other peers, Triton reduces the amount of time to wait for the information to be updated on the cloud. Antonio Davoli Triton@PaPEC 2014 • 14
  • 15. Experimental Results Antonio Davoli Triton@PaPEC 2014 • 15
  • 16. Experimental Results We built Triton as a client library and created a file system (in user space) based on the protocol’s access policies. In this way, we guarantee an interoperability with several cloud storage providers. Many other systems (e.g. Depot [1]) require to run part of the code on a centralized server, Triton instead code runs only on the clients. Antonio Davoli [1] Mahajan, P. et al. - Cloud Storage with minimal trust, OSDI’10 Triton@PaPEC 2014 • 16
  • 17. Antonio Davoli Triton@PaPEC 2014 • 17 Triton Latency for agreement • Up to 8 Replicas on a Intel Xeon Machine • Up to 8k Payload
  • 18. Antonio Davoli Triton@PaPEC 2014 • 18 Triton Performance Results • 6 Clients, • 5 Amazon Ec2 (Oregon) • 1 Sapienza Reg Elena CS Dept. (Rome)
  • 19. What’s Next? Antonio Davoli Triton@PaPEC • 19
  • 20. Conclusions & Future Work Triton is a peer-assisted cloud storage solution that merges the benifits of a P2P in the design of a cloud storage system. It contributes to improve performance and security concerns of classical storage systems. • Improve Experimental Results by employing a larger set of peers, • Enhance Triton model by applying a Game Theoric Approach for PEers BehaviorS (Rational and Selfish Peers), • Release the Library Prototype + Integration with p2p Social Nets. Antonio Davoli Triton@PaPEC 2014 • 19
  • 21. Antonio Davoli On a New Generation of Cloud Storage Systems• 28 Thanks for your attention. davoli@di.uniroma1.it
  • 22. On a New Generation of Cloud Storage System • 37Antonio Davoli Triton Backup Slides
  • 23. Practical Byzantine Fault Tolerance (BFT)
  • 25. Triton : Request < REQUEST, t, o, n, H, timestamp@c, c >c • t, topic related to the file • o, operations to apply, • n, sequence nubmer, • H, is hash of history, • timestamp@c is the timestamp for the op, • c, user ID.
  • 27. Triton : Proxy-Request < PROXY − REQ, t, o, n, H, timestamp@c, c, p >p • t, topic related to the file • o, operations to apply, • n, sequence nubmer, • H, is hash of history, • timestamp@c is the timestamp for the op, • p, the request to forward (collection), • c, user ID.
  • 28. Triton : Commit < COMMIT, t, o, n, H, R, timestamp@c, c, p >p • t, topic related to the file • o, operations to apply, • n, sequence nubmer, • H, is hash of history, • timestamp@c is the timestamp for the op, • p, collection of proxy-requests , • c, user ID.
  • 29. KARBON PRESENTATION 7 • 24LEVEL 13, 82 SPENCER ST, MELBOURNE AWESOME PRESENTATION TEMPLATE CAP Theorem CAP Theorem: In a network subject to communication failures, it is impossible for any web service to implement an atomic read/write shared memory that guarantees a response to every request.
  • 30. KARBON PRESENTATION 7 • 24LEVEL 13, 82 SPENCER ST, MELBOURNE AWESOME PRESENTATION TEMPLATE CAP Theorem (2) CAP Theorem: Any networked shared-data system can have at most two of three desirable properties: • COncistency (C), • HIgh Availability (A), • tolerance to network partitions (P)