Dependable Computing in Virtual Laboratories
Proceedings of the 17th International Conference on Data Engineering
BioOpera: Cluster-Aware Computing
CLUSTER '02 Proceedings of the IEEE International Conference on Cluster Computing
High Performance Parametric Modeling with Nimrod/G: Killer Application for the Global Grid?
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
Design and Evaluation of an Autonomic Workflow Engine
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Publishing Persistent Grid Computations as WS Resources
E-SCIENCE '05 Proceedings of the First International Conference on e-Science and Grid Computing
The JOpera visual composition language
Journal of Visual Languages and Computing
From web service composition to megaprogramming
TES'04 Proceedings of the 5th international conference on Technologies for E-Services
Flexible binding for reusable composition of web services
SC'05 Proceedings of the 4th international conference on Software Composition
GPC '09 Proceedings of the 4th International Conference on Advances in Grid and Pervasive Computing
Supporting content, context and user awareness in future internet applications
The Future Internet
Hi-index | 0.00 |
Virtual laboratories can be characterized by their long-lasting, large-scale computations, where a collection of heterogeneous tools is integrated into data processing pipelines. Such virtual experiments are typically modeled as scientific workflows in order to guarantee their reproduceability. In this chapter we present JOpera, one of the first autonomic infrastructures for managing virtual laboratories. JOpera provides a sophisticated Eclipse-based graphical environment to design, monitor and debug distributed computations at a high level of abstraction. The chapter describes the architecture of the workflow execution environment, emphasizing its support for the integration of heterogeneous tools and evaluating its autonomic capabilities, both in terms of reliable execution (self-healing) and automatic performance optimization (self-tuning).