Software-defined environments for science and engineering. (January 2018)

• Record Type: Journal Article
• Authors: AbdelBaky, Moustafa; Diaz-Montes, Javier; Parashar, Manish
• Other Names: Dongarra Jack guest-editor.; Tourancheau Bernard guest-editor.
• Is Part Of: International journal of high performance computing applications. Volume 32:Number 1(2018)
• Page Start: 104
• Page End: 122
• Publication Date: 2018-01
• Subjects: Software-defined environments -- software-defined service composition -- software-defined federation -- programmable infrastructure -- programmable service composition -- programmable federation; High performance computing -- Periodicals; Supercomputers -- Periodicals; 004.1105
• DOI: 10.1177/1094342017710706
• Languages: English
• ISSNs: 1094-3420

Abstract

Service-based access models coupled with recent advances in application deployment technologies are enabling opportunities for realizing highly customized software-defined environments that can achieve new levels of efficiencies and can support emerging dynamic and data-driven applications. However, achieving this vision requires new models that can support dynamic (and opportunistic) compositions of infrastructure services, which can adapt to evolving application needs and the state of resources. In this article, we present a programmable dynamic infrastructure service composition approach that uses software-defined environment concepts to control the composition process. The resulting software-defined infrastructure service composition adapts to meet objectives and constraints set by the users, applications, and/or resource providers. We present and compare two different approaches for programming resources and controlling the service composition, one that is based on a rule engine and another that leverages a constraint programming model for resource description. We present the design and prototype implementation of such software-defined service composition and demonstrate its operation through a use case where multiple views of heterogeneous, geographically distributed services are aggregated on demand based on user and resource provider specifications. The resulting compositions are used to run different bioinformatics workloads, which are encapsulated inside Docker containers. Each view independently adapts to various constraints and events that are imposed on the system while minimizing the workload completion time.