Middleware for Grid Computing 

Abstract: Among existing grid middleware approaches, one simple, powerful, and flexible approach consists of using servers available in different administrative domains through the classic client–server or remote procedure call paradigm. Network Enabled Servers (NES) implement this model, also called GridRPC. Clients submit computation requests to a scheduler, whose goal is to find a server available on the grid using some performance metric. The aim of this paper is to give an overview of a NES middleware developed in the GRAAL team called distributed interactive engineering toolbox (DIET) and to describe recent developments around plug-in schedulers, workflow management, and tools. DIET is a hierarchical set of components used for the development of applications based on computational servers on the grid. Copyright © 2007 John Wiley & Sons, Ltd.

Abstract: Efficient discovery of grid services is essential for the success of grid computing. The standardization of grids based on web services has resulted in the need for scalable web service discovery mechanisms to be deployed in grids Even though UDDI has been the de facto industry standard for web-services discovery, imposed requirements of tight-replication among registries and lack of autonomous control has severely hindered its widespread deployment and usage. With the advent of grid computing the scalability issue of UDDI will become a roadblock that will prevent its deployment in grids. In this paper we present our distributed web-service discovery architecture, called DUDE (Distributed UDDI Deployment Engine). DUDE leverages DHT (Distributed Hash Tables) as a rendezvous mechanism between multiple UDDI registries. DUDE enables consumers to query multiple registries, still at the same time allowing organizations to have autonomous control over their registries.. Based on preliminary prototype on PlanetLab, we believe that DUDE architecture can support effective distribution of UDDI registries thereby making UDDI more robust and also addressing its scaling issues. Furthermore, The DUDE architecture for scalable distribution can be applied beyond UDDI to any Grid Service Discovery mechanism.

Abstract: This article is a review of the importance of identifying the conceptual components participating in the design of Grid infrastructure, the interfaces presented to other elements and the semantics involved. We will show that the middleware layer still exposes too much detail of the underlying implementation, thus making the application development more complex, difficulting interoperability and scaling. We also discuss Cloud Computing, an emerging technology that has been so far successful in the IT market, also show how Grids and Clouds are related, and to what extent these technologies may provide features that will help accomplish the Grid vision for e-Science applications.

Abstract: A scheduler must consider the heterogeneity and communication delays when scheduling dependent tasks on a grid. The task-scheduling problem is NP-Complete in general, which led us to the development of a heuristic for the associated optimization problem. In this work we present a dynamic adaptive approach to schedule dependent tasks onto a grid based on the Xavantes grid middleware. The developed dynamic approach is applied to the Path Clustering Heuristic, and introduces the concept of rounds, which take turns sending tasks to execution and evaluating the performance of the resources. The adaptive extension changes the size of rounds during the process execution, taking task attributes and resources performance as parameters, and it can be adopted in other task schedulers. The experiments show that the dynamic round-based and adaptive schedule can minimize the effects of performance losses while executing processes on the grid. Copyright © 2007 John Wiley & Sons, Ltd. This is an extended version of the paper ‘A dynamic approach for scheduling dependent tasks on the Xavantes grid middleware,’ presented at the 4th International Workshop on Middleware for Grid Computing (MGC'06).