English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Two established approaches to engineer adaptive systems arearchitecture-based adaptation that uses a Monitor-Analysis-Planning-Executing(MAPE) loop that reasons over architectural models (aka Knowledge) to makeadaptation decisions, and control-based adaptation that relies on principles ofcontrol theory (CT) to realize adaptation. Recently, we also observe a rapidlygrowing interest in applying machine learning (ML) to support differentadaptation mechanisms. While MAPE and CT have particular characteristics andstrengths to be applied independently, in this paper, we are concerned with thequestion of how these approaches are related with one another and whethercombining them and supporting them with ML can produce better adaptive systems.We motivate the combined use of different adaptation approaches using ascenario of a cloud-based enterprise system and illustrate the analysis whencombining the different approaches. To conclude, we offer a set of openquestions for further research in this interesting area.

Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning