apport de recherche

Anomaly detection and diagnosis in grid environments

environment is an autonomous human-machine environment evolving with science, technology, culture, and society. The Knowledge Grid environment is an advanced development of human- machine systems. It focuses not only on machine and network performance but also on knowledge development, sharing

crucial role in developing complex application workflows in Grid environments. Although our work is preliminary, CWG has already been used to map high energy physics applications onto the Grid. In one particular experiment, a set of production runs lasted 7 days and resulted in the generation of 167

An overview of research and development challenges for managing data in Grid environments is provided. We relate issues in data management at various levels of abstraction, from resources storing data sets, to metadata required to manage access to such data sets. A common set of services

, and because independent tasks may share common data files. In this paper, we propose an adaptive scheduling algorithm for parameter sweep applications on the Grid. We modify standard heuristics for task/host assignment in perfectly predictable environments (Max-min, Min-min, Sufferage), and we propose

involve hundreds of processes that must be able to acquire resources dynamically and communicate e#ciently. This paper analyzes the unique security requirements of large-scale distributed (grid) computing and develops a security policy and a corresponding security architecture. An implementation

"Grid" computing has emerged as an important new field, distinguished from conventional distributed computing by its focus on large-scale resource sharing, innovative applications, and, in some cases, high-performance orientation. In this article, we define this new field. First, we

Grid technologies enable large-scale sharing of resources within formal or informal consortia of individuals and/or institutions: what are sometimes called virtual organizations. In these settings, the discovery, characterization, and monitoring of resources, services, and computations

Recent experience in deploying Grid middleware demonstrated the challenges one faces in delivering robust services in distributed and shared environments. Grid applications often must deliver reliable performance despite the use of distributed and shared resources. Although anomaly management