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Cluster Computing and the Grid, IEEE International Symposium on (2008)
May 19, 2008 to May 22, 2008
ISBN: 978-0-7695-3156-4
pp: 226-233
We formulate the service composition problem as a multi-objective stochastic program which simultaneously optimizes the following quality of service (QoS) parameters: workflow duration, service invocation costs, availability, and reliability. All of these quality measures are modelled as decision-dependent random variables. Our model minimizes the average value-at-risk (AVaR) of the workflow duration and costs while imposing constraints on the workflow availability and reliability. AVaR is a popular risk measure in decision theory which quantifies the expected shortfall below some percentile of a loss distribution. By replacingthe random durations and costs with their expected values, our risk-aware model reduces to the nominal problem formulation prevalent in literature. We argue that this nominal model can lead to overly risky decisions. Finally, we report on the scalability properties of our model.
Stochastic Programming, Web Service Composition, Quality of Service, Average Value-at-Risk
Ronald Hochreiter, Daniel Kuhn, Wolfram Wiesemann, "A Stochastic Programming Approach for QoS-Aware Service Composition", Cluster Computing and the Grid, IEEE International Symposium on, vol. 00, no. , pp. 226-233, 2008, doi:10.1109/CCGRID.2008.40
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