A VIRTUAL LABORATORY FOR

WEB AND GRID ENABLED SCIENTIFIC EXPERIMENTS

Francesco Amigoni, Mariagrazia Fugini

Dipartimento di Elettronica e Informazione, Politecnico di Milano, Milano, Italy

Diego Liberati

Istituto di Elettronica e Ingegneria dell’Informazione e delle Telecomunicazioni, Consiglio Nazionale delle Ricerche

Dipartimento di Elettronica e Informazione, Politecnico di Milano, Milano, Italy

Keywords: Virtual Organizations, Cooperative Information Systems, e-Science, Bioinformatics, e-Collaboration, Web

Services, Multiagent Systems.

Abstract: Interactions and organization models tend to be more and more oriented to flexibility, heterogeneity and

collaboration in the style of Virtual Organizations. This paper is framed in the context of e-science and

presents an approach to the definition and execution of distributed scientific experiments as a pool of

services executed on collaborating sites at different heterogeneous organizations. The focus is on flexibility,

reuse, orchestration and interoperability of services within a cooperation process. It is discussed how the

workflow of the experiment can be specified by actors with low information technology, but high domain,

knowledge and how an agent-based framework can enhance the flexibility of experiment execution.

Examples are given in the bioinformatics context. A prototype environment is described.

1 INTRODUCTION

The concept of Virtual Organization, nowadays

boosted also thanks to the Grid computing (Foster

and Kesselman, 2004), is a general model, free from

specific technical solutions: a set of individuals

and/or institutions having direct access to computers,

software, data, tools, knowledge, services and other

resources in a dynamic heterogeneous way, share the

aim of achieving a common goal through

collaboration. The basic idea is the virtualization of

resources, consisting in creating and associating to

resources a generic interface to allow services to be

used through remote control. In this paper, we face

such issue linked to orchestration of services in the

context of e-Science (Hey and Trefethen, 2004), (De

Roure et al., 2004), where the concept is known as

Collaborative Environment, to denote a

Collaborative Laboratory (Bosin et al., 2005). The

aim is to enable a set of scientific (but possibly also

commercial) partners to design the workflow of

distributed experiments, and to support the execution

of the experiment on distributed cooperative nodes,

each providing and using a set of services.

Virtualization of resources is achieved using Web

Service technologies (Alonso et al., 2004

Orchestration is achieved by taking each virtualized

resource as a component of a distributed cooperative

process, modelled in terms of workflows (van der

Alst and van Hee, 2002). The general process of

problem solving strategy in a Virtual Organization

can be seen as a whole of single collaborative

procedures or services (Travica, 2005), each

composed of input data, a computing core, and

output data. The suitable orchestration (Peltz, 2003)

of services is the aggregate workflow related to a

business activity, which is globally a service whose

resources (services) are dynamically found through a

discovery mechanism, and executed, through

dynamic binding, on the network (Figure 1). In the

resulting scientific experiment, modelled as a Web

Service, the resources are: functions, seen in terms

of services according to the Service Oriented

Architecture paradigm (Comm. of the ACM, 2003);

data; computational power; ICT components; or

humans. Global-scale experimental networking

initiatives, developed in the last years, aim to

227

Amigoni F., Fugini M. and Liberati D. (2007).

A VIRTUAL LABORATORY FOR WEB AND GRID ENABLED SCIENTIFIC EXPERIMENTS.

In Proceedings of the Ninth International Conference on Enterprise Information Systems - SAIC, pages 227-230

DOI: 10.5220/0002369902270230

 SciTePress

provide advanced infrastructures for e-scientists

through the collaborative development of

networking tools, advanced grid services, and data-

intensive applications (Newman et al., 2003). Web

Services and the Grid are then merged in the often

called Semantic Grid, in order to design Virtual

Laboratories, that is, Virtual Organizations where

cooperation to execute a scientific experiment is

supported. The paradigmatic distributed experiment

used in the paper refers to a process of DNA-

microarrays clustering, based on a technique

illustrated in (Liberati et al., 2005).

Figure 1: Plan of problem solving as a whole of connected

collaborative procedures.

2 SETTING THE VIRTUAL

LABORATORY

The functional architecture of our proposed Virtual

Laboratory is represented in Figure 2.

“Organization” identifies each member of a Virtual

Laboratory who cooperates to a specific experiment

run. The “clouds” denote distinct physical sites. We

mainly distinguish, besides sites where experiments

are invoked and visualised, the following sites:

Site where the experiment is defined. The

Workflow Designer associated to Organization A

creates the workflow model that characterizes the

distributed experiment by selecting the needed

resources and specifying their choreography in the

experiment workflow. The tool used to support this

actor in specifying the experiment workflow is a

workflow editor, in particular the Taverna tool

(http://taverna.sourceforge.net/), allowing scientists

to define and execute their workflows, and to

analyse the deriving outputs, through operations of

Web Service discovery, selection, and link, which

can be executed through a graphical support. Once

the definition of the experiment has been completed,

an instance of a workflow model is created and

produces results (e.g., usually in the Scufl format

XML file). Scufl (Simple Conceptual Unified Flow

Language) (Oinn, 2004) is a workflow description

language similar to BPEL (Business Process

Execution Language), used in the commercial and

software engineering environments (Andrews et al.,

2003).

Site/sites where data are placed. We assume

that the site of Organization D stores (and has

published as available, according to the Web Service

style (Booth et el., 2004)) all the data necessary for

the experiment. The data resources can also be

drawn from various sites (possibly belonging to

different organizations); this means that several

instances of the experiment have to be launched at

the same time, in order to achieve the best

parallelism and resource allocation policy. Data can

be found in any location on the Web, identified

through an URL, and published in a registry in the

UDDI style.

Site/sites where the computation takes place.

In our example, processing uses the Matlab tool

(http://www.mathworks.com), itself modelled as a

Web Service performing the clustering process

described in (Liberati et al., 2005). The computation

can be partitioned in functional sub-modules. The

granularity and the terms of the contract set up in the

Virtual Laboratory before starting an experiment and

regarding collaboration are negotiable upon specific

demands and according to the specification of

complexity, performance, and costs of the

experiment. Additional modular potentialities are

available through foreseen interfaces, that can be

linked and referred to existing services, using simply

the Web Service Definition Language (WSDL)

interfaces, with possibly additional information

regarding the modalities of execution of the

experiment (e.g., security, performance, and in

general Quality of Service related parameters).

3 ARCHITECTURAL ISSUES

Figure 3 shows the implementation of some

components of the Virtual Laboratory support

system in our specific experiment, that is, a process

of DNA-microarrays clustering. The Experiment

Engine interfaces the Matlab environment and

executes the code for the clustering procedure. The

core of the elaboration is a Matlab function. This is

made active by Java code through a suitable

JMatLink interface (http://jmatlink.sourceforge.net/).

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The whole Java code is exposed as a Web Service.

Single functions can be isolated as independent Web

Services, and allocated on different sites, allowing

reuse. Web Services are created and deployed via

Apache Axis, installed on the Apache Tomcat

container (http://tomcat.apache.org/). The Data

Extraction Module is used as follows. Nearly the

whole totality of DNA-microrrays data is available

in a few standard formats. Each can be further

distinguished in different variants. A wide choice of

data is available for example on the Cancer Program

Data Set of Broad Institute

(http://www.broad.mit.edu/cgi-

bin/cancer/datasets.cgi). The Data Extraction

Module is a Java module that can express these

different kinds of formats into a unified

representation.

4 FUTURE WORKS

An interesting evolution can promote the flexibility

of the Virtual Laboratory concepts presented in the

paper. In particular, the use of agents (Wooldridge,

2002) allows for dynamic selection, at run time, of

the services that execute the activities of an

experiment. An improved architecture can make use

of two kinds of agents. A Supervisor Agent (SA) is

in charge of supervising the overall execution of the

experiment. For each activity of the experiment, the

SA discovers from a Yellow Pages directory service

the set of agents able to provide the needed

service(s). These agents are called Resource Agents

(RAs) and represent and manage the actual services.

The services are still modelled as Web Services,

described with various properties, among which the

non-functional QoS parameters (availability,

robustness, time, cost, security) that are needed to

select the Web Services. The basic role of the SA is

to assign each activity to the most appropriate RA

and to supervise its execution. For example, the

activity assignment can be done according to the

well-known contract net protocol (Smith, 1980), in

which an SA announces an activity to some RAs and

awards it to the best offering RA. A given

organization of the collaborative network can have

more SAs (i.e., more experiments currently running)

and more RAs (i.e., more resources made available).

In this scenario, the Workflow Designer does not

even need to be aware of the physical structure of

the network that will be used to execute the

experiment in a distributed collaborative way. For

example, the Workflow Designer does not need to

specify, as in Figure 1, that the initial activity of the

workflow will be executed by a service from

Organization A. The Workflow Designer has only to

specify the kind of the initial activity and, then, the

SA in charge of supervising the execution of the

experiment will find the most appropriate service to

execute the activity, according to Quality of Service

parameters. Note that the service from Organization

A will execute the activity if it is the only available

Figure 2: General architecture of the Virtual Laboratory.

A VIRTUAL LABORATORY FOR WEB AND GRID ENABLED SCIENTIFIC EXPERIMENTS

229

service for the activity or if it is considered by the

SA to be the best service (e.g., requiring minimum

time and with high degree of security) to execute the

activity.

Setting up this improved architecture requires

addressing a number of issues, including the

dynamic negotiation of contracts between agents,

and the interface between agents and the workflow

engine and between agents and Web Services.

When the Grid Services (Foster and Kesselman,

2004) will have reached a better stability on the

market, it will be possible to build a prototype of the

proposed system by using also this technology.

The approach can be generalized, with the

opportune tuning, to wider areas of business

interaction. The evolutions in the commercial

domain will be offered by the virtual marketplaces

of services, where the modalities of distribution are

characterised by ways of payment (for subscription

or for amount consumed) for single transactions.

Also privacy and data security are major

concerns in our current research, considering both

methods to select trusted nodes within the

cooperation network and to obscure or encrypt both

the transmitted and stored data and portions of the

experiment workflow, to preserve sensitivity,

according to user security requirements.

Figure 3: Prototype system modules.

ACKNOWLEDGEMENTS

We thank prof. Nicoletta Dessì, dr. Andrea Bosin,

and dr. Barbara Pes, for common ideas and work.

We acknowledge the contribution of Alberto

Trovato in the prototype implementation.

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