Performance measuring framework for grid market middleware

Current implementations of Grid infrastructures provide frameworks which aim at achieve on-demand computing. In such a scenario, contribution and use of resources will be governed by business models. The challenge is to provide multi-level performance information which enables the participation of the different actors in such a system. In this paper we describe the performance measuring framework developed for Grid Market Middleware, a middleware which supports economic-model based selection of service-oriented Grid applications. This middleware is a distributed infrastructure, which we have implemented for providing a market of services and resources to be assigned to Grid applications. The objectives of the performance measuring framework is first to assess the behaviour of the middleware and the used economic models in a deployed system, and secondly allow the provision of metrics for the components of the middleware itself. We describe the design of the performance measuring framework, its implementation and show its capability and usefulness for our objectives by experiments.Peer Reviewe

A study of publish/subscribe systems for real-time grid monitoring

Monitoring and controlling a large number of geographically distributed scientific instruments is a challenging task. Some operations on these instruments require real-time (or quasi real-time) response which make it even more difficult. In this paper, we describe the requirements of distributed monitoring for a possible future electrical power grid based on real-time extensions to grid computing. We examine several standards and publish/subscribe middleware candidates, some of which were specially designed and developed for grid monitoring. We analyze their architecture and functionality, and discuss the advantages and disadvantages. We report on a series of tests to measure their real-time performance and scalability

Speeding-up the execution of credit risk simulations using desktop grid computing: A case study

This paper describes a case study that was undertaken at a leading European Investment bank in which desktop grid computing was used to speed-up the execution of Monte Carlo credit risk simulations. The credit risk simulations were modelled using commercial-off-the-shelf simulation packages (CSPs). The CSPs did not incorporate built-in support for desktop grids, and therefore the authors implemented a middleware for desktop grid computing, called WinGrid, and interfaced it with the CSP. The performance results show that WinGrid can speed-up the execution of CSP-based Monte Carlo simulations. However, since WinGrid was installed on non-dedicated PCs, the speed-up achieved varied according to users’ PC usage. Finally, the paper presents some lessons learnt from this case study. It is expected that this paper will encourage simulation practitioners and CSP vendors to experiment with desktop grid computing technologies with the objective of speeding-up simulation experimentation

Software Provision Process for EGI

he European Grid Initiative (EGI) provides a sustainable pan-European Grid computing infrastructure for e-Science based on a network of regional and national Grids. The middleware driving this production infrastructure is constantly adapted to the changing needs of the EGI Community by deploying new features and phasing out other features and components that are no longer needed. Unlike previous e-Infrastructure projects, EGI does not develop its own middleware solution, but instead sources the required components from Technology Providers and integrates them in the Unified Middleware Distribution (UMD). In order to guarantee a high quality and reliable operation of the infrastructure, all UMD software must undergo a release process that covers the definition of the functional, performance and quality requirements, the verification of those requirements and testing in production environments

MATLAB*G: A Grid-Based Parallel MATLAB

This paper describes the design and implementation of MATLAB*G, a parallel MATLAB on the ALiCE Grid. ALiCE (Adaptive and scaLable internet-based Computing Engine), developed at NUS, is a lightweight grid-computing middleware. Grid applications in ALiCE are written in Java and use the distributed shared memory programming model. Utilizing existing MATLAB functions, MATLAB*G provides distributed matrix computation to the user through a set of simple commands. Currently two forms of parallelism for distributed matrix computation are implemented: task parallelism and job parallelism. Experiments are carried out to investigate the performance of MATLAB*G on each type of parallelism. Results indicate that for large matrix sizes MATLAB*G can be a faster alternative to sequential MATLAB.Singapore-MIT Alliance (SMA

Application Plugins for Distributed Simulations on the Grid

Computing grids are today still underexploited by scientific computing communities. The main reasons for this are, on the one hand, the complexity and variety of tools and services existent in the grid middleware ecosystem, and, on the other hand, the complexity of the development of applications capable to exploit the grids. We address in this work the challenge of developing grid applications that keep pace with the rapid evolution of grid middleware. For that, we propose an approach based on plugins for grid applications that encapsulate a set of commonly used type of grid operations. We further propose more complex high-level functionalities, such as the plugins for remote exploration of simulation scenarios and for monitoring of the behavior of end-user applications in grids. We provide an example of a grid application constructed with these software components and evaluate based on it the performance of our approach in the context of the simulation of biological neurons. The results obtained on test and production grids demonstrate the usefulness of the proposed plugins, with a small performance overhead compared to traditional grid tools

Combining Grid and Cloud Resources by Use of Middleware for SPMD Application

International audienceDistributed computing environments have evolved from in-house clusters to Grids and now Cloud platforms. We, as others, provide HPC benchmarks results over Amazon EC2 that show a lower performance of Cloud resources compared to private resources. So, it is not yet clear how much of impact Clouds will have in high performance computing (HPC). But hybrid Grid/Cloud computing may offer opportunities to increase overall applications performance, while benefiting from in-house computational resources extending them by Cloud ones only whenever needed. In this paper, we advocate the usage of ProActive, a well established middleware in the grid community, for mixed Grid/Cloud computing, extended with features to address Grid/Cloud issues with little or no effort for application developers. We also introduce a framework, developed in the context of the DiscoGrid project, based upon the ProActive middleware to couple HPC domain-decomposition SPMD applications in heterogeneous multi-domain environments. Performance results coupling Grid and Cloud resources for the execution of such kind of highly communicating and processing intensive applications have shown an overhead of about 15%, which is a non-negligible value, but lower enough to consider using such environments to achieve a better cost-performance trade-off than using exclusively Cloud resources

Global Grids and Software Toolkits: A Study of Four Grid Middleware Technologies

Grid is an infrastructure that involves the integrated and collaborative use of computers, networks, databases and scientific instruments owned and managed by multiple organizations. Grid applications often involve large amounts of data and/or computing resources that require secure resource sharing across organizational boundaries. This makes Grid application management and deployment a complex undertaking. Grid middlewares provide users with seamless computing ability and uniform access to resources in the heterogeneous Grid environment. Several software toolkits and systems have been developed, most of which are results of academic research projects, all over the world. This chapter will focus on four of these middlewares--UNICORE, Globus, Legion and Gridbus. It also presents our implementation of a resource broker for UNICORE as this functionality was not supported in it. A comparison of these systems on the basis of the architecture, implementation model and several other features is included.Comment: 19 pages, 10 figure

Encryption Key Search using Java-based ALiCE Grid

Encryption Key Search is a compute-intensive operation that consists of a brute-force search of a particular key in a given key space. Sequential execution time for a 56-bit encryption key search is approximately 200,000 years and therefore it is ideal to execute such operation in a grid environment. ALiCE (Adaptive and scaLable internet-based Computing Engine) is a grid middleware that offers a portable software technology for developing and deploying grid applications and systems. This paper discusses the development of the Encryption Key Search application on ALiCE and also presents the performance evaluation of ALiCE using this application.Singapore-MIT Alliance (SMA

Supporting simulation in industry through the application of grid computing

An increased need for collaborative research, together with continuing advances in communication technology and computer hardware, has facilitated the development of distributed systems that can provide users access to geographically dispersed computing resources that are administered in multiple computer domains. The term grid computing, or grids, is popularly used to refer to such distributed systems. Simulation is characterized by the need to run multiple sets of computationally intensive experiments. Large scale scientific simulations have traditionally been the primary benefactor of grid computing. The application of this technology to simulation in industry has, however, been negligible. This research investigates how grid technology can be effectively exploited by users to model simulations in industry. It introduces our desktop grid, WinGrid, and presents a case study conducted at a leading European investment bank. Results indicate that grid computing does indeed hold promise for simulation in industry