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Data storage in Cloud computing
1. Overview of Cloud Computing and Workflow
Research in NGSP Group
Dr. Dong YUAN
Research Fellow
Swinburne University of Technology
Melbourne, Australia
2. Outline
> SUCCESS Centre and NGSP Group
> Background: Big Data, Cloud Computing and Workflow
> Research Topics
– Data Management in Cloud Computing
– Performance Management in Scientific Workflows
– Security and Privacy Protection in the Cloud
– SwinDeW-C Cloud Workflow System
3. The Centre of SUCCESS
> SUCCESS: Swinburne University Centre for Computing
and Engineering Software Systems
– SUCCESS is the “NO.1” Software Engineering Centre in
Australia
– SUCCESS is one of the 7 Tire 1 Centres at Swinburne
University of Technology (Times World Ranking: 351- 400,
Academic Ranking of World Universities: 301- 400)
> The ambition of the Centre is to become the top centre
for software research in the Southern Hemisphere
within the next five years.
3
4. SUCCESS
> Research Focus Areas
– Knowledge and Data Intensive Systems
– Nature of Software
– Next Generation Software Platforms
– SE Education and IBL/RBL
– Software Analysis and Testing
– Software R&D Group
> http://www.swinburne.edu.au/ict/success/research-
expertise/
4
5. NGSP (Small) Group Overview
> We conduct research into cloud computing and workflow
technologies for complex software systems and services.
> Members:
Leader:
Prof Yun Yang
(PC Member for
ICSE 07/08, FSE09
ICSE 10/11/12)
Researchers:
Dr Xiao Liu (Postdoc, China)
Dr Dong Yuan (Postdoc)
Gaofeng Zhang
Wenhao Li
Dahai Cao
Jofry Hadi SUTANTO
Antonio Giardina
Others:
Prof John Grundy
Prof Chengfei Liu
5
Visitors:
Prof Lee Osterweil
Prof Lori Clarke
Prof Ivan Stojmenovic
Prof Paola Inverardi
Prof Amit Sheth
Prof Wil van der Aalst
Prof Hai Jin
Prof Hai Zhuge
6. > Primary projects:
– (Cloud) workflow technology: Scheduling and temporal analysis in cloud
workflows
• ARC LP0990393 (Y Yang, R Kotagiri, J Chen, C Liu)
– Cloud computing: Intermediate data management in cloud computing
• ARC DP110101340 (Y Yang, J Chen, J Grundy)
> Secondary project:
– Management control systems for effective information sharing and
security in government organisations
• ARC LP110100228 (S Cugenasen, Y Yang)
R&D Projects – Grants
6
7. > SwinDeW workflow family including SwinDeW-C
– Architectures / Models (D Cao)
– Scheduling / Data and service management (D Yuan, X Liu)
– Verification / Exception handling (X Liu)
> Cloud computing:
– Data management (D Yuan, X Liu, W Li)
– Privacy and Security (G Zhang, X Zhang, C Liu)
R&D Projects – Overview
7
8. > J. Chen and Y. Yang, Temporal Dependency based Checkpoint Selection for Dynamic
Verification of Temporal Constraints in Scientific Workflow Systems. ACM Transactions on
Software Engineering and Methodology, 20(3), 2011
> X. Liu, Y. Yang, Y. Jiang and J. Chen, Preventing Temporal Violations in Scientific
Workflows: Where and How. IEEE Transactions on Software Engineering, 37(6):805-
825, Nov./Dec. 2011.
> D. Yuan, Y. Yang, X. Liu and J. Chen, On demand Minimum Cost Benchmarking for‑
Intermediate Datasets Storage in Scientific Cloud Workflow Systems. Journal of Parallel
and Distributed Computing, 71:(316-332), 2011
> J. Chen and Y. Yang, Localising Temporal Constraints in Scientific Workflows. Journal of
Computer and System Sciences, Elsevier, 76(6):464-474, Sept. 2010
> G. Zhang, Y. Yang and J. Chen, A Historical Probability based Noise Generation Strategy for
Privacy Protection in Cloud Computing. Journal of Computer and System Sciences,
Elsevier, published online, Dec. 2011.
> Another 8 A* papers are currently under review…
Some Recent ERA A* Ranked Publications
8
9. Part 1: Outline
> SUCCESS Centre and NGSP Group
> Background: Big Data, Cloud Computing and Workflow
> Research Topics
– Data Management in Cloud Computing
– Performance Management in Scientific Workflows
– Security and Privacy Protection in the Cloud
– SwinDeW-C Cloud Workflow System
10. Big Data
> Data explosion
– TB (1012
), PB(1015
), exabyte (EB, 1018
), zettabyte (ZB, 1021
), yottabyte (YB,1024
)
– The total amount of global data in 2010:
– Google processes ？ data everyday in 2009:
– Every day, Facebook 10T, Twitter 7T, Youtube 4.5T
> Moore's law vs. data explosion speed
– Application data double every year over the next decade and further -
[Szalay et al. Nature, 2006]
> Buzzwords: data storage, data processing, parallel, distributed,
virtualisation, commodity machines, energy consumption, data
centres, utility computing, software (everything) as a service
10
1.2 ZB
24 PB
11. 11
Example: Pulsar Searching
> Astrophysics: pulsar searching
> Pulsars: the collapsed cores of stars that were once more massive than 6-10 times
the mass of the Sun
> http://astronomy.swin.edu.au/cosmos/P/Pulsar
> Parkes Radio Telescope (http://www.parkes.atnf.csiro.au/)
> Swinburne Astrophysics group (http://astronomy.swinburne.edu.au/) has been
conducting pulsar searching surveys (http://astronomy.swin.edu.au/pulsar/) based
on the observation data from Parkes Radio Telescope.
> Typical scientific workflow which involves a large number of data and computation
intensive activities. For a single searching process, the average data volume (not
including the raw stream data from the telescope) is over 4 terabytes and the
average execution time is about 23 hours on Swinburne high performance
supercomputing facility (http://astronomy.swinburne.edu.au/supercomputing/).
left: Image of the Crab Nebula taken with
the Palomar telescope
right: A close up of the Crab Pulsar from
the Hubble Space Telescope
Credit: Jeff Hester and Paul Scowen
(Arizona State University) and NASA
13. Benefits of Clouds
> No upfront infrastructure investment
– No procuring hardware, setup, hosting, power, etc..
> On demand access
– Lease what you need and when you need..
> Efficient Resource Allocation
– Globally shared infrastructure …
> Nice Pricing
– Based on Usage, QoS, Supply and Demand, Loyalty, …
> Application Acceleration
– Parallelism for large-scale data analysis…
> Highly Availability, Scalable, and Energy Efficient
> Supports Creation of 3rd Party Services & Seamless offering
– Builds on infrastructure and follows similar Business model as Cloud
13
14. SwinDeW Workflow Series
SwinDeW – Swinburne Decentralised Workflow
- foundation prototype based on p2p
– SwinDeW – past
– SwinDeW-S (for Services) – past
– SwinDeW-B (for BPEL4WS) – past
– SwinDeW-G (for Grid) – past
– SwinDeW-A (for Agents) – past
– SwinDeW-V (for Verification) – current
– SwinDeW-C (for Cloud) – current
15. Part 1: Outline
> SUCCESS Centre and NGSP Group
> Background: Big Data, Cloud Computing and Workflow
> Research Topics
– Data Management in Cloud Computing
– Performance Management in Scientific Workflows
– Security and Privacy Protection in the Cloud
– SwinDeW-C Cloud Workflow System
17. Data Management in Cloud Computing
> Scientific applications in cloud computing
– Computation and data intensive applications
– Excessive computation and storage resources
– Pay-as-you-go model
> Three aspects of data management in the cloud
– Data storage
– Data placement
– Data replication
18. Data Storage
> Developing smart data storage strategies for reducing
the cost of storing big data in the cloud
– Data regeneration (computation and storage
trade-off)
– Data de-duplication
– Data compression
> Researcher: Dong Yuan
19. Publications
> D. Yuan, Y. Yang, X. Liu, J. Chen, On demand Minimum Cost Benchmarking for‑
Intermediate Datasets Storage in Scientific Cloud Workflow Systems, Journal of
Parallel and Distributed Computing, Elsevier, vol. 71(2), pp. 316-332, 2011.
> D. Yuan, Y. Yang, X. Liu, G. Zhang, J. Chen, A Data Dependency Based Strategy
for Intermediate Data Storage in Scientific Cloud Workflow Systems, Concurrency
and Computation: Practice and Experience, Wiley, 24(9), pp. 956-976, Jun. 2012.
> D. Yuan, Y. Yang, X. Liu, J. Chen, A Cost-Effective Strategy for Intermediate Data
Storage in Scientific Cloud Workflow Systems, Proc. of 24th IEEE International
Parallel & Distributed Processing Symposium (IPDPS10), Atlanta, USA, Apr. 2010.
> D. Yuan, Y. Yang, X. Liu and J. Chen, A Local-Optimisation based Strategy for
Cost-Effective Datasets Storage of Scientific Applications in the Cloud, Proc. of 4th
IEEE International Conference on Cloud Computing (Cloud2011), Washington DC,
USA, July 4-9, 2011.
20. Data Placement
> Smart data placement strategies to reduce
application cost
– Data correlation based strategy to reduce
bandwidth cost
– Data usage based strategy to reduce storage cost
> Researchers: Dong Yuan, Jofry Hadi SUTANTO,
Antonio Giardina
21. Publications
> D. Yuan, Y. Yang, X. Liu, J. Chen, A Data Placement Strategy in
Scientific Cloud Workflows, Future Generation Computer Systems,
Elsevier, vol. 26(8), pp. 1200-1214, 2010.
22. Data Replication
> To cost-effectively assure data reliability in the cloud
– Dynamic replication strategy
– Proactively checking based replication strategy
> Researchers: Wenhao Li, Dong Yuan
23. Publications
> W. Li, Y. Yang and D. Yuan, A Novel Cost-effective Dynamic Data
Replication Strategy for Reliability in Cloud Data Centres. Proc. of
International Conference on Cloud and Green Computing (CGC2011),
pages 496-502, Sydney, Australia, Dec. 2011.
> W. Li, Y. Yang, J. Chen and D. Yuan, A Cost-Effective Mechanism for
Cloud Data Reliability Management based on Proactive Replica
Checking. Proc. of 12th IEEE/ACM International Symposium on Cluster,
Cloud and Grid Computing (CCGrid2012), pages 564-571, Ottawa,
Canada, May 2012.
25. 25
Workflow QoS
> QoS dimensions
– time, cost, fidelity, reliability, security …
> QoS of Cloud Services
> Workflow QoS
– the overall QoS for a collection of cloud services
– but not simply add up!
26. 26
Temporal QoS
> System performance
– Response time
– Throughput
> Temporal constraints
– Global constraints: deadlines
– Local constraints: milestones, individual activity durations
> Satisfactory temporal QoS
– High performance: fast response, high throughput
– On-time completion: low temporal violation rate
27. 27
Problem Analysis
> Setting temporal constraints
– Prerequisite: effective forecasting of activity durations
> Monitoring temporal consistency state
– Monitor workflow execution state
– Detect potential temporal violations
> Temporal violation handling
– Where to conduct violation handling
– What strategies to be used
29. Forecasting Activity Durations
> Statistical time-series pattern based forecasting strategies
> Selected Publications:
– X. Liu, Z. Ni, D. Yuan, Y. Jiang, Z. Wu, J. Chen, Y. Yang, A Novel
Statistical Time-Series Pattern based Interval Forecasting Strategy
for Activity Durations in Workflow Systems, Journal of Systems and
Software (JSS), vol. 84, no. 3, Pages 354-376, March 2011.
– X. Liu, J. Chen, K. Liu and Y. Yang, Forecasting Duration Intervals of
Scientific Workflow Activities based on Time-Series Patterns, Proc.
of 4th IEEE International Conference on e-Science (e-Science08),
pages 23-30, Indianapolis, USA, Dec. 2008.
29
30. Setting Temporal Constraints
> Probability based temporal consistency model
> Time analysis based on Stochastic Petri Nets
> Selected Publications:
– X. Liu, Z. Ni, J. Chen, Y. Yang, A Probabilistic Strategy for Temporal
Constraint Management in Scientific Workflow Systems,
Concurrency and Computation: Practice and Experience (CCPE),
Wiley, 23(16):1893-1919, Nov. 2011 .
– X. Liu, J. Chen and Y. Yang, A Probabilistic Strategy for Setting
Temporal Constraints in Scientific Workflows, Proc. 6th International
Conference on Business Process Management (BPM2008), Lecture
Notes in Computer Science, Vol. 5240, pages 180-195, Milan, Italy,
Sept. 2008.
30
31. Temporal Consistency Monitoring
> Minimum (Probability) Time Redundancy based Checkpoint Selection
Strategy
> Temporal Dependency based Checkpoint Selection Strategy
> Selected Publications:
– X. Liu, Y. Yang, Y. Jiang and J. Chen, Preventing Temporal
Violations in Scientific Workflows: Where and How. IEEE
Transactions on Software Engineering, 37(6):805-825, Nov./Dec.
2011.
– J. Chen and Y. Yang, Temporal Dependency based Checkpoint
Selection for Dynamic Verification of Temporal Constraints in
Scientific Workflow Systems. ACM Transactions on Software
Engineering and Methodology, 20(3), 2011
32. Violation Handling
> Violation Handling Point Selection
> (Probability) Time deficit allocation
> Workflow local rescheduling strategy – ACO, GA, PSO
> Selected Publications:
– X. Liu, Z. Ni, Z. Wu, D. Yuan, J. Chen and Y. Yang, A Novel General Framework
for Automatic and Cost-Effective Handling of Recoverable Temporal Violations in
Scientific Workflow Systems, Journal of Systems and Software, vol. 84, no. 3, pp.
492-509, 2011
32
34. Background
> Data Security vs. Data Privacy
> Privacy in cloud computing
– Massive data store and compute in open cloud environment
– Customers cannot control inside cloud
The severity of privacy risk in cloud computing
One specific privacy risk in cloud computing
– Indirectly private information (collectively information)
– Normal service processes and functions (not disruption)
The approach: noise obfuscation for privacy protection
35. Privacy Protection in Cloud
> Roles in the view of privacy in regular IT system
– Privacy owner, Privacy user and Privacy theft
Privacy owner
Privacy theft
Privacy user
Keep safe
between Privacy
owner and
Privacy
user!
36. Privacy Protection in Cloud
> Roles in the view of privacy in Cloud
– Privacy owner, privacy user and privacy theft
Privacy owner
Privacy theft
Privacy user
Virtualisation
disable the
“keeping safe
between Privacy
owner and Privacy
user!”
37. Noise Obfuscation(1)
> Background
– Massive data stores and computes in open cloud environments.
– Customers cannot control inside cloud.
> Main idea: “Dilute” real private information with noise information
– Not noise signal!
38. Noise Obfuscation(2)
> A Motivating example:
– One customer, who often travels to one city in Australia, like ‘Sydney’, checks the
weather report regularly from a weather service in cloud environments before
departure. The frequent appearance of service requests about the weather report for
‘Sydney’ can reveal the privacy that the customer usually goes to ‘Sydney’. But if a
system aids the customer to inject other requests like ‘Perth’ or ‘Darwin’ into the
‘Sydney’ queue, the service provider cannot distinguish which ones are real and
which ones are ‘noise’ as it just sees a similar style of service request. These
requests should be responded and cannot reveal the location privacy of the
customer. In such cases, the privacy can be protected by noise obfuscation in
general.
From ‘data’ privacy to ‘process’ privacy!
39. > Noise Generation
– Historical probability based noise generation strategy
– Time-series pattern based noise generation strategy
– Association probability based noise generation strategy
– ……
> Noise Utilisation
– Trust model and injection strategy for noise obfuscation
– ……
> Noise Cooperation Mechanism
– Privacy protection framework under noise obfuscation
Research Topics
40. Publications
> G. Zhang, Y. Yang and J. Chen, A Historical Probability based Noise Generation
Strategy for Privacy Protection in Cloud Computing. Journal of Computer and
System Sciences, Elsevier, 78(5):1374-1381, Sept. 2012.
> G. Zhang, Y. Yang, D. Yuan and J. Chen, A Trust-based Noise Injection
Strategy for Privacy Protection in Cloud Computing. Software: Practice and
Experience , Wiley, 42(4):431-445, Apr. 2012.
> G. Zhang, Y. Yang, X. Liu and J. Chen, A Time-series Pattern based Noise
Generation Strategy for Privacy Protection in Cloud Computing. Proc. of 12th
IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing
(CCGrid2012), pages 458-465, Ottawa, Canada, May 2012.
> G. Zhang, X. Zhang, Y. Yang, C. Liu and J. Chen, An Association Probability
based Noise Generation Strategy for Privacy Protection in Cloud Computing.
Proc. 10th International Conference on Service Oriented Computing
(ICSoC2012), pages 639-647, Shanghai, China, Nov. 2012. (accepted on
13/7/2012)
