Overview of Cloud Computing and Workflow Research in NGSP Group

Dr. Dong YUAN

Research Fellow

Swinburne University of Technology Melbourne, Australia

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

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

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

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 forICSE 07/08, FSE09 ICSE 10/11/12)

Researchers:Dr Xiao Liu (Postdoc, China)Dr Dong Yuan (Postdoc)Gaofeng ZhangWenhao LiDahai CaoJofry Hadi SUTANTOAntonio Giardina

Others:Prof John GrundyProf Chengfei Liu

5

Visitors:Prof Lee OsterweilProf Lori ClarkeProf Ivan StojmenovicProf Paola InverardiProf Amit ShethProf Wil van der Aalst Prof Hai JinProf Hai Zhuge

> 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

> 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

> 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

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

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

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 TelescopeCredit: Jeff Hester and Paul Scowen (Arizona State University) and NASA

Pulsar Searching Workflow

12

AccelerateCollect data

Transfer Data

Pulse Seek

FFA Seek

Get Candidates

Eliminate candidates

Fold to XML

Extract Beam

Get Candidates

U(SW)=24hours

…...

…...

……

…...

Make Decision

U(SW1)=15.25hoursU(SW2)=5.75hours

De-disperse (1200)

De-disperse (3600)

De-disperse (2400)

…...

Extract Beam

1hour

13hours

1.5hours

1hour

20minutes 4hours

20minutes1.5hours

10minutes 20minutes

Transfer Data

…… FFT

Seek

Data Collection

Data Pre-processing Decision Making

Candidate Searching

Dr. Willem van Straten

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

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

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

16

Dr. Dong Yuan

http://www.ict.swin.edu.au/personal/dyuan/

Data Management in Cloud Computing

Research Topics

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

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

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.

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

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.

Data Replication

> To cost-effectively assure data reliability in the cloud

– Dynamic replication strategy

– Proactively checking based replication strategy

> Researchers: Wenhao Li, Dong Yuan

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.

Dr. Xiao Liu

http://www.ict.swin.edu.au/personal/xliu/

Performance Management in Scientific Workflows

Research Topics

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

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

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

Temporal Framework

28

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

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

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

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

33

Gaofeng Zhang

gzhang@swin.edu.au

Security and Privacy Protection in the Cloud

Research Topics

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

Privacy Protection in Cloud

> Roles in the view of privacy in regular IT system

– Privacy owner, Privacy user and Privacy theft

Privacy ownerPrivacy theft

Privacy userKeep safe between Privacy owner and Privacy user!

Privacy Protection in Cloud

> Roles in the view of privacy in Cloud

– Privacy owner, privacy user and privacy theft

Privacy ownerPrivacy theft

Privacy user

Virtualisation disable the “keeping safe between Privacy owner and Privacy user!”

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!

Real Information

Noise Information

Final Information

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!

> 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

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)

41

Dahai Caodcao@swin.edu.au

Cloud Workflow System Design and Development

Research Topics

SwinCloud – Cloud Computing Testbed

> SwinCloud

42

Swinburne Computing Facilities

Astrophysics Supercomputer

VMware

Cloud Simulation Environment

Data Centres with Hadoop

· GT4· SuSE Linux

Swinburne CS3

…...

…...

· GT4· CentOS Linux

Swinburne ESR

…...

…...

· GT4· CentOS Linux

General cloud workflow reference model

ADMINISTRATION & MONITORING

TOOLS

CLIENT APPS

WORKLIST HANDLER

INVOKED APPLICATIONS

WORKFLOW ENACTMENT SERVICE

WORKFLOW ENGINES

OTHER WORKFLOW ENACTMENT SERVICES

WORKFLOW ENGINES

Interface 1

Interface 5

Interface 2

Interface 3

Interface 4 - Interoperability

TYPICALWEB SERVICES

APPLICATION PROVISION SERVICE

TOOL AGENT(S)

WORKFLOW RELATIVE SERVICE

WORKFLOW ACCOMPANIMENT TOOLS

PROCESS DEFINITION

TOOLS

Process Definition Import/Export

The component proposed by WfMC

New component

Form Designing

Tools

Resouce Tools

Tool Agent Tools

Billing Tools

Resouce ServicesData

Service Organisation Service

Version Service

User Service

Security Service

Billing Service

Prototype : SwinDeW-C (Peer-to-Peer)Ⅰ

> SwinDeW-C

44

Activity

Workflow Execution

UKVPAC

HongKong

SwinburneCS3

· SwinDeW-G· GT4· CentOS Linux

BeihangCROWN· SwinDeW-G· CROWN· Linux

SwinburneESR

· SwinDeW-G· GT4· CentOS Linux

AstrophysicsSupercomputer

· SwinDeW-G· GT4· SuSE Linux

PfC

na 1na

2na

3na 4na

5na 6na Na

ma 1ma

2ma

3ma 4ma

5ma 6ma Ma

Amazon Data Centre

Google Data Centre

Microsoft Data Centre

SwinDeW-G Grid Computing Infrastructure

Commercial Cloud

Infrastructure

VMVMVM VM VMVMVM VMVMVMVMVM

……..

……..

……..Application

Layer

Platform Layer

Unified Resource

Layer

Fabric Layer

SwinCloud……..

VM

SwinDeW-C Peer

SwinDeW-C Coordinator Peer

Prototype : SwinFlow-Cloud (Centralised)Ⅱ

Workflow Client

Tools (2)

Workflow Client Tools (1)

Workflow Server EC2 Instance (Linux with Tomcat)

Legacy Application(s)

Application Provision Service on Cloud

Process Definition

Tool

ProcessEngine(s)

Workflow Control

DataWork List

Worklist Handler

Legacy Application(s)

Workflow Enactment Service

instantiated by

Process Definition

defines

maintains

interact via

Cloud Workflow RelevantServices

Workflow Relevant

Data

use

refer to

invokes Tool Agent(s)

update

Cloud Workflow Accompaniment ToolsServer

Configuration Tools

BillingSpecification

Tools

defines

Other Cloud Application(s)

Other Cloud Application(s)

invokes

Configuration

Server Status Checker

Cloud BillingMeterprices

Pricing policies

defines

Tool Agents

Tool AgentDefinition Tools

defines

looks up & callswatchesProcessEngine(s)

ProcessEngine(s)

Administration Tools

Cloud workflow implementation

> Client system

– Process definition tools

– Rule editor

– Organisation modelling tools

– Office calendar management tools

– Authority group tools

– User management tools

– Form designing tools

– Tool agent definition tools

– Simulation tools

New Progress

> Successfully deploy on the Amazon Cloud

> Eucalyptus: the cloud infrastructure platform

A Book

End

> Questions?