Cloud architecture continuity: Change models and change rules for sustainable cloud software architectures. Issue 2 (7th February 2017)

• Record Type: Journal Article
• Authors: Pahl, Claus; Jamshidi, Pooyan; Weyns, Danny
• Other Names: Lago Patricia guestEditor.; Penzenstadler Birgit guestEditor.
• Is Part Of: Journal of software. Volume 29:Issue 2(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-02-07
• Subjects: adaptation -- change -- change models -- cloud systems -- evolution -- software architecture; Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.1849
• Languages: English
• ISSNs: 2047-7473

Abstract

Abstract: Cloud systems provide elastic execution environments of resources that link application and infrastructure/platform components, which are both exposed to uncertainties and change. Change appears in 2 forms: the evolution of architectural components under changing requirements and the adaptation of the infrastructure running applications. Cloud architecture continuity refers to the ability of a cloud system to change its architecture and maintain the validity of the goals that determine the architecture. Goal validity implies the satisfaction of goals in adapting or evolving systems. Architecture continuity aids technical sustainability, that is, the longevity of information, systems, and infrastructure and their adequate evolution with changing conditions. In a cloud setting that requires both steady alignment with technological evolution and availability, architecture continuity directly impacts economic sustainability. We investigate change models and change rules for managing change to support cloud architecture continuity. These models and rules define transformations of architectures to maintain system goals: Evolution is about unanticipated change of structural aspects of architectures, and adaptation is about anticipated change of architecture configurations. Both are driven by quality and cost, and both represent multidimensional decision problems under uncertainty. We have applied the models and rules for adaptation and evolution in research and industry consultancy projects.