Architecting applications for the Cloud is challenging due to significant differences between traditional hosting and Cloud infrastructure setup, unknown and unproven Cloud performance and scalability characteristics, as well as variable quota limitations. Building workable cloud applications therefore requires in-depth insight into the architectural and performance characteristics of each cloud offering, and the ability to reason about tradeoffs and alternatives of application designs and deployments. NICTA has developed a Service Oriented Performance Modeling technology for modeling the performance and scalability of Service Oriented applications architected for a variety of platforms. Using a suite of cloud testing applications we conducted in-depth empirical evaluations of a variety of real cloud infrastructures, including Google App Engine, Amazon EC2, and Microsoft Azure. The insights from these experimental evaluations, and other public/published data, were combined with the modeling technology to predict the resource requirements in terms of cost, application performance, and limitations of a realistic application for different deployment scenarios.
Since 2006 NICTA has been developing and trialing Service-Oriented Performance Modeling (SOPM), a method and tool support for performance modeling of large-scale heterogeneous Service Oriented Architectures (SOAs). This technology enables software architects to rapidly build performance models of SOAs directly in terms of service compositions. Enterprise Service Buses (ESBs) are an increasingly common style of SOA infrastructure and implementation technology that we have encountered and modeled in eGovernment SOA projects. In this paper we show the application of our SOPM approach to the MULE Enterprise Service Bus Loan Broker application in a laboratory context. We give a high-level outline of the SOPM method, and introduce the MULE ESB and Loan Broker application. We describe how a SOPM of the Loan Broker application is built in terms of application business-logic services and MULE infrastructure service components, and parameterized with measurements from an experimental test-bed. We demonstrate the validity of the approach in an initial scenario, and then explore the modeling of alternative deployment and application scenarios.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.