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International audienceEnterprise distributed real-time and embedded (DRE)publish/subscribe (pub/sub) systems manage resources and data that arevital to users. Cloud computing--where computing resources areprovisioned elastically and leased as a service--is an increasinglypopular deployment paradigm. Enterprise DRE pub/sub systems can leveragecloud computing provisioning services to execute needed functionalitywhen on-site computing resources are not available. Although cloudcomputing provides flexible on-demand computing and networkingresources, enterprise DRE pub/sub systems often cannot accuratelycharacterize their behavior a priori for the variety of resourceconfigurations cloud computing supplies (e.g., CPU and networkbandwidth), which makes it hard for DRE systems to leverage conventionalcloud computing platforms. This paper provides two contributions to thestudy of how autonomic configuration of DRE pub/sub middleware canprovision and use on-demand cloud resources effectively. We firstdescribe how supervised machine learning can configure DRE pub/submiddleware services and transport protocols autonomically to supportend-to-end quality-of-service (QoS) requirements based on cloudcomputing resources. We then present results that empirically validatehow computing and networking resources affect enterprise DRE pub/subsystem QoS. These results show how supervised machine learning canconfigure DRE pub/sub middleware adaptively in < 10 μsec with boundedtime complexity to support key QoS reliability and latencyrequirements

Adapting Distributed Real-Time and Embedded Pub/Sub Middleware for Cloud Computing Environments