Survey of grid resource monitoring and prediction strategies.

This literature focuses on grid resource monitoring and prediction, representative monitoring and prediction systems are analyzed and evaluated, then monitoring and prediction strategies for grid resources are summarized and discussed, recommendations are also given for building monitoring sensors and prediction models. During problem definition, one-step-ahead prediction is extended to multi-step-ahead prediction, which is then modeled with computational intelligence algorithms such as neural network and support vector regression. Numerical simulations are performed on benchmark data sets, while comparative results on accuracy and efficiency indicate that support vector regression models achieve superior performance. Our efforts can be utilized as direction for building online monitoring and prediction system for grid resources

New e-Learning system architecture based on knowledge engineering technology

The paper focuses on the field of research on next generational e-Learning facility, in which knowledge-enhanced systems are the most important candidates. In the paper, a reference architecture based on the technologies of knowledge engineering is proposed, which has following three intrinsic characteristics, first, education ontologies are used to facilitate the integration of static learning resource and dynamic learning resource, second, based on semantic-enriched relationships between Learning Objects (LOs), it provides more advanced features for sharing, reusing and repurposing of LOs, third, with the concept of knowledge object, which is extended from LO, an implementing mechanism for knowledge extraction and knowledge evolution in e-Learning facilities is provided. With this reference architecture, a prototype system called FekLoma (Flexible Extensive Knowledge Learning Object Management Architecture) has been realized, and testing on it is carrying out

Design of plug-in schedulers for a GridRPC environment

International audienceGrid middleware performance is typically determined by the resources that are chosen. Different classes of applications need different metrics to define a meaningful notion of performance. Such metrics include application workload, execution time estimation, disk or memory space availability, etc. In the past, few environments have allowed schedulers to be tuned for specific application scenarios. Within the DIET (Distributed Interactive Engineering Toolbox) project, we developed an API that allows the resource broker to be tuned for specific application classes. In a seamless way, generic or application-dependent performance measures can be used within the hierarchy of resource brokers