Prof. Dr. Cesare Pautasso

Autonomic Computing for Virtual Laboratories

Cesare Pautasso, Win Bausch, Gustavo Alonso

Jürg Kohlas, Bertrand Meyer, André Schiper (eds.)

Dependable Systems: Software, Computing, Networks, Springer, pp. 211-230

2006

Abstract

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).

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ISBN: 3-540-36821-3

DOI: 10.1007/11808107_10

PDF: ▼jopera-hasler2006.pdf (2MB)

Citation

Bibtex

@inbook{116,
	author = {Cesare Pautasso and Win Bausch and Gustavo Alonso},
	title = {Autonomic Computing for Virtual Laboratories},
	editor = {J\"urg Kohlas and Bertrand Meyer and Andr\'e Schiper},
	booktitle = {Dependable Systems: Software, Computing, Networks},
	volume = {4028},
	series = {LNCS},
	year = {2006},
	pages = {211-230},
	publisher = {Springer},
	abstract = {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).},
	keywords = {autonomic computing, JOpera, scientific workflow management},
	isbn = {3-540-36821-3},
	doi = {10.1007/11808107_10}
}