Elastic Processes on Clouds of Hybrid Services: Principles, Enabling Techniques and Approaches

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Elastic Processes on Clouds of Hybrid Services: Principles, Enabling Techniques

and Approaches

Hong-Linh Truong Distributed Systems Group,

Vienna University of Technology

truong@infosys.tuwien.ac.athttp://www.infosys.tuwien.ac.at/staff/truong

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Acknowledgement: some results from joint works with: Kamal Bhattacharya, Schahram Dustdar, Muhammad Zuhri Catur Candra, Aditya Ghose, Lam-Son Le, Frank Leymann, Vitaliy Liptchinsky, Tran-Vu Pham, Michael Reiter, Mirela Riveni, Ognjen Scekic, Quang-Hieu Vu, and Rostyslav Zaboloznyi

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An example of elastic processes

Recap: Schahram‘s presentation yesterday

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Hybrid services in the cloud

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How to realize elasticity in hybrid systems?

Attention: ongoing work not everything is well tested !

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Outline

Vienna Elastic Computing Model Incorporating humans into programming

paradigms Programming hybrid services in the cloud Modeling elasticity constraints Reasoning elasticity constraints Conclusions and future work

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VIENNA ELASTIC COMPUTING MODEL

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Multi-dimensional Elasticity

Units can be networks, middleware, SaaS, PaaS

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Vienna Elastic Computing Modelmulti-dimensional elasticity, service computing models and cloud provisioning models

Schahram Dustdar, Hong-Linh Truong, "Virtualizing Software and Humans for Elastic Processes in Multiple Clouds - a Service Management Perspective" International Journal of Next-Generation Computing, To appear, 2012

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INCORPORATING HUMANS INTO PROGRAMMING PARADIGM

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Incorporate humans into a programming paradigm

• Abstracting human compute units as program elements• Extending programming languages to support human

compute units• Data/control flows via extensible APIs

Programming languages

• Shared memory (e.g., human –software – human), message passing (human-to-human), artifact-centric, etc., via APIs working atop the compute unit abstraction layer

Multiple programming

models

• Computing capability /profile management: human computing power, reputation and incentive models

• Monitoring: monitoring and enforcing incentives/rewards, quality of results, availability

• Communication: between human-middleware, among ICU/SCU , within SCU for exchanging artifacts and comprehensing social tasks

Execution environment

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Incorporate humans into a programming paradigm

Volunteers ProfessionalsTeamIndividual

Service-based Middleware

CommunicationMonitoringCapability/Profile

Management

Provisioning/Negotiation/Execution API

Abstraction of Human Compute Units

SCU

ICU

ICU ICU SCU

Program languages and programming models

Program elements

Software Compute

Units

program human actions and dependenciesprogram incentive condition and rewarding actionprogram result evaluation method

Human-to-middleware interfaces:• visualization of collective

tasks• embedding of common

forms• mobile app

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Existing technologies are not adequate

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PROGRAMMING HYBRID SERVICES IN THE CLOUD

Hong-Linh Truong, Schahram Dustdar, Kamal Bhattacharya, Programming Hybrid Services in the Cloud, Working paper,2012

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HBS Communication Interface

Based on well known technologies for integrating human requests/responses into SBS Emails, web interfaces, Web services, etc.

Some special characteristics The same protocol can be used for multiple layers

(middleware, application, OS) -- unlike in SBS Only asyschronous communication and unknown

upper-bound delay

Can support message passing and shared memory models hbsi hbsj or hbsi sbs hbsj

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Human Power Unit (HPU)

Humans are determined via skills/skill levels A particular HBS cloud can define its own sets of

skills (CS) and skill levels (SK) Similar to Amazon defines its own EC unit Different ways to make sure skill and skill levels

declared in a cloud consistent (e.g., via testing and monitoring).

HPU Definition: HPU is a value describing the computing power of an HBS measured in an abstract unit. A cloud of HBS has a pre-defined basic power unit, hpuθ, corresponding to the baseline skill bsθ of the cloud.”

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Human Power Unit (HPU) – our starting point

Given an HBS, its theoretical HPU can be larger or smaller than its real HPU – unlike SBS

HPU for a particular (skill, skill level)

HPU for a set of (skill, skill level), no weighted factors

Decomposition/composition of HPU for a shared HBS

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HBS Instances Provisioning

• Individual Compute Unit (ICU)• Social Compute Unit (SCU

Types of services

• iICU(CS, HPU, price, utilization,location, APIs)

• iSCU(CS,HPU, price, utilization,connectedness, location, APIs)

• Other (traditional) NFPs

Instances Description

s

• utilization• offering communication APIs• connectedness

Pricing factors

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Cloud APIs for Provisioning Hybrid Services

APIs hide low-level platforms and utilize low level HBS communication interfaces

APIs for HBS information and management

•listSkills();listSkillLevels();•listICU();listSCU()•negotiateHBS()•startHBS()•suspendHBS ()•resumeHBS ()•stopHBS()•reduceHBS()•expandHBS()

APIs for HBS execution and

communication •runRequestOnHBS ()•receiveResultFromHBS()

•sendMessageToHBS() •receiveMessageFromHBS()

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Prototype (simulated environment)

Combined with Jcloud/boto for real SBS

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Framework for Utilizing Hybrid Services

IT ManagementSmart City Management

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Modeling HPU-aware Task Dependency Graphs

Link management skills and compliance constraints to tasks required HBS

Can be specified in TOSCA!Modeled in dependency graphs

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Programming a combination of HBS and SBS

e.g., preparing/managing inputs/outputs for HBS using SBS

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Forming iSCUs Done by consumers or cloud providers

SCU Formation Algorithms

Business As Usual

Corrective Action

HBS Constraints

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Configuring iSCU

Establish „connectedness“ based on compliance constraints and network topology Addional cost might occur!

Program SBS and HBS for the iSCU to have a complete working environment.

Different connectedness E.g., Ring-based, star-based, and master-slave

topologies

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Selecting HBS: Some algorithms

Several algorithms can be built based on existing team formation algorithms which do not consider dependency graphs

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Forming iSCU by minimizing cost and considering no direction

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Example of star-based iSCU using Dropbox as a communication hub

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Change Model for Task Graph’s Human Power Unit

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MODELING ELASTICITY CONSTRAINT

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Elasticitiy modeling

• Beyond traditional QoS metrics and for only services

• Modeling elasticity constraints• Attaching elasticity constraints to processes at

the design time for composition and runtime elasticity

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Beyond QoS metrics (1)

And many more metrics!

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Beyond QoS metrics (2)

Data marketplaces and DaaSElastic dynamic properties associated with:Service APIs, data APIs and data assets

Quang Hieu Vu, Tran Vu Pham, Hong-Linh Truong, Schahram Dustdar, Rasool Asal, DEMODS: A Description Model for Data-as-a-Service, The 26th IEEE International Conference on Advanced Information Networking and Applications (AINA-2012)

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Elastic Profile Using Rules

process

process fragment

activity

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Declarative constraints based first-order logic and set theory

Lam-Son Le, Hong-Linh Truong, Adytia Ghose, Schahram Dustdar, On Elasticity and Constrainedness of Business Services Provisioning, 9th International Conference on Services Computing (SCC 2012)

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REASON THE ELASTICITY CONSTRAINTS

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Runtime quality, cost and benefits evaluation and reasoning principles

Part A Part B Part C

model i model j model k

Runtime: Elastic processes

Quality, cost and benefits

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Composable cost evaluation

Hong Linh Truong, Schahram Dustdar: Composable cost estimation and monitoring for computational applications in cloud computing environments. Procedia CS 1(1): 2175-2184 (2010)

Tran Vu Pham, Hong-Linh Truong, Schahram Dustdar "Elastic High Performance Applications - A Composition Framework", The 2011 Asia-Pacific Services Computing Conference (IEEE APSCC 2011), (c) IEEE Computer Society, December 12 - 15, 2011, Jeju, Korea

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Runtime evaluation of quality of data in workflows

Michael Reiter, Uwe Breitenbuecher, Schahram Dustdar, Dimka Karastoyanova, Frank Leymann, Hong-Linh Truong, A Novel Framework for Monitoring and Analyzing Quality of Data in Simulation Workflows, (c)IEEE Computer Society, The 7th IEEE International Conference on e-Science, 5-8 December, 2011, Stockholm, SwedenMichael Reiter, Hong-Linh Truong, Schahram Dustdar, Dimka Karastoyanova, Robert Krause, Frank Leymann, Dieter Pahr, On Analyzing Quality of Data Infuences on Performance of Finite Elements driven Computational Simulations, EuroPar 2012

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Incentive Monitoring

Ognjen Scekic, Hong-Linh Truong, Schahram Dustdar, "Modeling Rewards and Incentive Mechanisms for Social BPM",, Short paper, 10th International Conference on Business Process Management (BPM2012), (c)Springer-Verlag, September 3-6, 2012, Tallinn, Estonia

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Put things together -- IDE for elastic processes

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Put things together – from high-level languages to programmable units

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Statelet: a declarative coordination language for collaboration processes

Statelet statements can be mapped to APIs for programmable units

Vitaliy Liptchinsky, Roman Khazankin, Hong Linh Truong, Schahram Dustdar: Statelets: Coordination of Social Collaboration Processes. COORDINATION 2012

Provisioning/Negotiation/Execution API

Abstraction of Human Compute Units

SCUICUICU ICU SCU

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Conclusions and Future Work

Novel models and APIs are needed for integrating humans into program paradigms

We need to address the whole stack Elasticity modeling, monitoring and reasoning Mapping high level languages to APIs of hybrid services

Future works Several issues mentioned here are ongoing work Declarative languages for specifying elasticity Methods for evaluation and experiments

Some open questions will be addressed in FP7 CELAR

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Thanks for your attention

Hong-Linh TruongDistributed Systems GroupVienna University of Technologytruong@infosys.tuwien.ac.athttp://www.infosys.tuwien.ac.at/staff/truong

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