Journal Article on Descartes Meta-Model (DMM)

New article on "DMM Application Architecture Meta-Model", published in the Elsevier Science of Computer Programming Journal (SciCo), is now available.

A new article by Fabian Brosig, Nikolaus Huber, and Samuel Kounev on "Architecture-Level Software Performance Abstractions for Online Performance Prediction", published in the Elsevier Science of Computer Programming Journal (SciCo), is now available.

Furthermore, a second article by Nikolaus Huber, André van Hoorn, Anne Koziolek, Fabian Brosig, and Samuel Kounev on "Modeling Run-Time Adaptation at the System Architecture Level in Dynamic Service-Oriented Environments" has been accepted for publication and will appear in the Springer Journal on Service Oriented Computing and Applications (SOCA).

Fabian Brosig, Nikolaus Huber, and Samuel Kounev. Architecture-Level Software Performance Abstractions for Online Performance Prediction. Elsevier Science of Computer Programming Journal (SciCo), 2013. [ bib | DOI | http | .pdf ]

Abstract:

Modern service-oriented enterprise systems have increasingly complex and dynamic loosely-coupled architectures that often exhibit poor performance and resource efficiency and have high operating costs. This is due to the inability to predict at run-time the effect of workload changes on performance-relevant application-level dependencies and adapt the system configuration accordingly. Architecture-level performance models provide a powerful tool for performance prediction, however, current approaches to modeling the context of software components are not suitable for use at run-time. In this paper, we analyze typical online performance prediction scenarios and propose a performance meta-model for i) expressing and resolving parameter and context dependencies, ii) modeling service abstractions at different levels of granularity and iii) modeling the deployment of software components in complex resource landscapes. The presented meta-model is a subset of the Descartes Meta-Model (DMM) for online performance prediction, specifically designed for use in online scenarios. We motivate and validate our approach in the context of realistic and representative online performance prediction scenarios based on the SPECjEnterprise2010 standard benchmark.

Nikolaus Huber, André van Hoorn, Anne Koziolek, Fabian Brosig, and Samuel Kounev. Modeling Run-Time Adaptation at the System Architecture Level in Dynamic Service-Oriented Environments. Service Oriented Computing and Applications, 2013, Springer-Verlag. In print. [ bib | .pdf ]

Abstract:

Today, software systems are increasingly operated in dynamic, virtualized environments. Such environments host diverse applications of different parties sharing the underlying resources. The goal of this resource sharing is to utilize resources efficiently while ensuring that quality-of-service requirements are continuously satisfied. In such scenarios, complex adaptations to changes in the system environment are still largely performed manually by humans. Over the past decade, autonomic self-adaptation techniques aiming to minimize human intervention have become increasingly popular. However, given that adaptation processes are usually highly system-specific, it is a challenge to abstract from system details enabling the reuse of adaptation strategies. In this paper, we present S/T/A, a modeling language to describe system adaptation processes at the system architecture level in a generic, human-understandable and reusable way. We apply our approach to multiple different realistic contexts (dynamic resource allocation, run-time adaptation planning, etc.). The results show how a holistic model-based approach can close the gap between complex manual adaptations and their autonomous execution.