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.
Language
English
Keywords
Stochastic Programming
Web Service Composition
Quality of Service
Average Value-at-Risk
HSG Classification
contribution to scientific community
Refereed
Yes
Book title
8th IEEE International Symposium on Cluster Computing and the Grid
Publisher
IEEE Computer Society
Publisher place
Los Alamitos, Calif.
Start page
226
End page
233
Pages
8
Event Title
8th IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2008)