Nadia Ranaldo - Eugenio Zimeo

Department of Engineering University of Sannio – Benevento – Italy

Grids@Work 2008 ProActive and GCM User Group

Orchestrating Services based on Active Objects and Grid Components

N. Ranaldo & E. Zimeo

Outline

• Research Context Composition-based approaches for Grid applications Service orchestration and choreography

• The SAWE Workflow Enactment System• Orchestration of ProActive/GCM components

Distributed data flow Dynamic binding

• Future directions

N. Ranaldo & E. Zimeo

Grid Applications: Composition-based approaches

•Complex scientific and business applications as composition of reusable, independent and cooperating software units in large-scale distributed systems

Heterogeneity, dynamicity, scalability, security, etc.•Composition in space

Structural relations and interactions among units Code re-use Tightly-coupled systems (closed world, well-defined

knowledge) Favoured by component-based architectures

•Composition in time Units ordered with respect to temporal dependences Efficient scheduling and resource usage Loosely-coupled systems (open world – incremental

knowledge, late binding) Favoured by service-oriented architectures

•Exploit the advantages of both the approaches for Grid applications

N. Ranaldo & E. Zimeo

Composition in time: Orchestration of Services

• Analysis

• Hypothesis

• Related work

• Propose experiments

• Define steps

• Prototype computing systems

• Perform experiments

• Data collection

• Visualization

• Validation

• Adjust experiment

• Refine hypothesis

• Presentation

• Dissemination

Define problems Experiments Data analysis Discovery

Graphical Workflow Editor

Workflow Engine (WE)

Experiment processes

Grid middleware functionalities

• scheduling

• data movement

• monitoring

N. Ranaldo & E. Zimeo

Workflow engines for e-science

Taverna:-Web services based language: Scufl;

-FreeFluo: engine

-Graphical viz of workflow

Kepler:-Actor,director

-MoML

-Execution models

-Ptolemy II

-Web Services

Triana:-Components

-Task graph

-Data/control flow

DAGMan:-Computing tasks

-DAG

Pegasus:-Based on DAGMan

-VDL

-DAG

and many others

N. Ranaldo & E. Zimeo

Towards Service Choreography: Centralized Orchestration Approach

Centralized control and data flow• Completely independent services• High network overhead

•A workflow is managed by a central workflow engine • Late binding• Efficient scheduling and QoS criteria fulfillment performed interacting

with resource management services (matchmaker, broker, etc.) and parallel execution frameworks (skeletons, parallel libraries, etc.)

W orkflowEngine

A1 A2

Client

activateA1

activateA2

An. . .

activateA n

data1 data2

datao ut

datan

data in

data in data1 datan -1

A1 A4A3 ... AnA2

W orkflowEngine

A1 A2

Client

activate A1

activate A2

An. . .

activate An

data1 data2

datao ut

datan -1

datan

data in

data in

Centralized control flow – distributed data flow• Dynamic dependencies among services

N. Ranaldo & E. Zimeo

Towards Service Choreography: Distributed Orchestration Approach

A1 A2Clientactivate A1 activate A2

An. . .

activate An

data1data2

datao u t

datandata in

• P2P network of services for discovery, composition and execution• Each activity described from the individual perspective of its

participating services• Better support to dynamic workflows

Re q u e stHan d le r

Exe cu to r

W E

G oaldescription

En viron men t description(seman tic an n otation s)

Co mp o se r

directlysolve

solve by com posinginner and external services

execu tepartia l w orkflow

Partia l Domainkn ow ledge

resu lt

N. Ranaldo & E. Zimeo

Semantic and Autonomic WE (SAWE)

• Compliant to WfMC specification• XPDL, BPEL• Configurator

Defines process description

• Engine Functional management

of the process

• Manager Monitors engine, running

activites, environment Decides actions to react

to events, environmental changes, etc.

N. Ranaldo & E. Zimeo

Workflows of ProActive/GCM Components

• A task is performed by a ProActive/GCM component (typically a composite component), which exports a well defined functionality

ProActive M iddle w are

Resources

Co

ntr

ol

Bin

din

g

SA

WE

Inte

rac

tio

n

Activ ity Control

. . .

Resource Inte rface

retrievesconcrete resource

executesactivity

schedules activity

Inv oker

X PDL Work flowDescription

Resource M anager

LocalR M

W SR M

H umanPerformer

R I

R M IR I

Jav aR eflection

R I

W ebServ ices

R I

ProActiv e/GC MR I

P rocess Control

Deploym entDescriptor/

ADL Definition

• Grid Component Model (GCM)• Based on Fractal• Target Grid context

• Parallel computation, deployment, dynamicity, autonomous behaviuor

• Lookup of already running components

• Deployment at run-time

N. Ranaldo & E. Zimeo

Future

Future

Engine

B

AA(run)A

Future

B(run)

Value

Value

B(block)B

Early-Start Pattern•Task anticipation exploiting asynchronous invocations and futures

Default future update strategy (data flow follows invocation flow)•Distributed data flow through futures

The lazy message-based update strategy

• No interactions among tasks and the engine for data updating

N. Ranaldo & E. Zimeo

Workflows of ProActive Components: Dynamic Binding

• Dynamic binding (abstract modelling) of ProActive tasks adopting the ProActive Scheduler

Co

ntr

ol

Bin

din

g

SA

WE

Inte

rac

tio

n

Activ ity Control

. . .

Resource Inte rface

ProActiveSche dule r

submits activity

retrievesconcrete resource

executeactivit ies

schedules activity

Inv oker

X P DL Work flowDescription

Resource M anager

LocalR M

W SR M

ProActiv eR M

H umanPerformer

R I

R M IR I

Jav aR eflect ion

R I

W ebServ ices

R I

ProActiv eR I

P rocess Control

retrieves result

Re s ource s

N. Ranaldo & E. Zimeo

Future Directions

• Distributed data flow based on the lazy message-based future update strategy

• Dynamic binding of ProActive/GCM components QoS description through semantic annotations of components

for dynamic binding based on user-defined QoS criteria

• Monitoring of ProActive/GCM components for autonomic behaviour of workflows

Thanks for your attention!

For further contact: Nadia Ranaldo ranaldo@unisannio.it Eugenio Zimeo zimeo@unisannio.it