Talks and Poster Presentations (with Proceedings-Entry):

B. Satzger, W. Hummer, Ph. Leitner, S. Dustdar:
"Esc: Towards an Elastic Stream Computing Platform for the Cloud";
Talk: IEEE 4th International Conference on Cloud Computing (CLOUD 2011), Washington, DC, USA; 2011-07-04 - 2011-07-09; in: "Proceedings of the IEEE 4th International Conference on Cloud Computing (CLOUD 2011)", L. Liu, M. Parashar (ed.); IEEE Computer Society, (2011), ISBN: 978-0-7695-4460-1; 348 - 355.

English abstract:
Today, most tools for processing big data are
batch-oriented. However, many scenarios require continuous,
online processing of data streams and events. We present ESC,
a new stream computing engine. It is designed for computations
with real-time demands, such as online data mining. It offers
a simple programming model in which programs are specified
by directed acyclic graphs (DAGs). The DAG defines the data
flow of a program, vertices represent operations applied to
the data. The data which are streaming through the graph
are expressed as key/value pairs. ESC allows programmers to
focus on the problem at hand and deals with distribution and
fault tolerance. Furthermore, it is able to adapt to changing
computational demands. In the cloud, ESC can dynamically
attach and release machines to adjust the computational
capacities to the current needs. This is crucial for stream
computing since the amount of data fed into the system is
not under the platform´s control. We substantiate the concepts
we propose in this paper with an evaluation based on a highfrequency
trading scenario.

stream computing; event processing; adaptability

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Related Projects:
Project Head Schahram Dustdar:

Created from the Publication Database of the Vienna University of Technology.