A formal architecture-centric and model driven approach for the engineering of science gateways

From n-Tier client/server applications, to more complex academic Grids, or even the most recent and promising industrial Clouds, the last decade has witnessed significant developments in distributed computing. In spite of this conceptual heterogeneity, Service-Oriented Architecture (SOA) seems to have emerged as the common and underlying abstraction paradigm, even though different standards and technologies are applied across application domains. Suitable access to data and algorithms resident in SOAs via so-called ‘Science Gateways’ has thus become a pressing need in order to realize the benefits of distributed computing infrastructures.In an attempt to inform service-oriented systems design and developments in Grid-based biomedical research infrastructures, the applicant has consolidated work from three complementary experiences in European projects, which have developed and deployed large-scale production quality infrastructures and more recently Science Gateways to support research in breast cancer, pediatric diseases and neurodegenerative pathologies respectively. In analyzing the requirements from these biomedical applications the applicant was able to elaborate on commonly faced issues in Grid development and deployment, while proposing an adapted and extensible engineering framework. Grids implement a number of protocols, applications, standards and attempt to virtualize and harmonize accesses to them. Most Grid implementations therefore are instantiated as superposed software layers, often resulting in a low quality of services and quality of applications, thus making design and development increasingly complex, and rendering classical software engineering approaches unsuitable for Grid developments.The applicant proposes the application of a formal Model-Driven Engineering (MDE) approach to service-oriented developments, making it possible to define Grid-based architectures and Science Gateways that satisfy quality of service requirements, execution platform and distribution criteria at design time. An novel investigation is thus presented on the applicability of the resulting grid MDE (gMDE) to specific examples and conclusions are drawn on the benefits of this approach and its possible application to other areas, in particular that of Distributed Computing Infrastructures (DCI) interoperability, Science Gateways and Cloud architectures developments

Impact of the execution context on Grid job performances

In this paper, we examine how the execution context of grid jobs can help to refine submission strategies on a production grid. On this kind of infrastructure, the latency highly impacts performances. We present experiments that quantify the dependencies between the grid latency and both internal and external context parameters on the EGEE grid infrastructure. We show how job submission managers, job execution sites and the submission date can be statistically correlated to grid performances. 1. Objectives Grids are increasingly used as support infrastructures for different scientific application areas [11, 9, 5]. Several grids, such as the European EGEE grid infrastructure 1 [10] or the Open Science Grid (OSG) 2, have reached a production level quality of service. These large scale and multiusers systems are characterized by a non-stationary load, their heterogeneity and their large geographic expansion. As a consequence, non-negligible jobs submission latencies, measured as the time between the jobs submission and the start of their execution, are observed. They are mainly due to queuing systems, network delays and system faults. This system variability is known to highly impact application performances and thus has to be taken into account [14]. Several initiatives aim at modeling grid infrastructure Workload Management Systems (WMS). In [12], correlations between job execution characteristics (job size or number of processors requested, job runtime and memory used) are studied on a multi-cluster supercomputer in order to build models of workloads, enabling comparative study on system design and scheduling strategies. Feitelson [4