research-article

Architectural Run-time Models for Performance and Privacy Analysis in Dynamic Cloud Applications

Author:

Robert HeinrichAuthors Info & Claims

ACM SIGMETRICS Performance Evaluation Review, Volume 43, Issue 4

Pages 13 - 22

https://doi.org/10.1145/2897356.2897359

Published: 25 February 2016 Publication History

Abstract

Building software systems by composing third-party cloud services promises many benefits such as flexibility and scalability. Yet at the same time, it leads to major challenges like limited control of third party infrastructures and runtime changes which mostly cannot be foreseen during development. While previous research focused on automated adaptation, increased complexity and heterogeneity of cloud services as well as their limited observability, makes evident that we need to allow operators (humans) to engage in the adaptation process. Models are useful for involving humans and conducting analysis, e.g. for performance and privacy. During operation the systems often drifts away from its design-time models. Run-time models are kept insync with the underlying system. However, typical run-time models are close to an implementation level of abstraction which impedes understandability for humans. In this vision paper, we present the iObserve approach to target aforementioned challenges while considering operationlevel adaptation and development-level evolution as two mutual interwoven processes. Central to this perception is an architectural run-time model that is usable for automatized adaptation and is simultaneously comprehensible for humans during evolution. The run-time model builds upon a technology-independent monitoring approach. A correspondence model maintains the semantic relationships between monitoring outcomes and architecture models. As an umbrella a megamodel integrates design-time models, code generation, monitoring, and run-time model update. Currently, iObserve covers the monitoring and analysis phases of the MAPE control loop. We come up with a roadmap to include planning and execution activities in iObserve.

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Cited By

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Architectural Run-time Models for Performance and Privacy Analysis in Dynamic Cloud Applications

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Published In

cover image ACM SIGMETRICS Performance Evaluation Review

ACM SIGMETRICS Performance Evaluation Review Volume 43, Issue 4

March 2016

61 pages

ISSN:0163-5999

DOI:10.1145/2897356

Editor:
Nidhi Hegde
Bell Labs France, Alcatel-Lucent Centre de Villarceaux Route de Villejust, Nozay, France

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Published: 25 February 2016

Published in SIGMETRICS Volume 43, Issue 4

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Fang AZhou RTang XHe PBissyandé TKlein JBird CSarro F(2023)RPCover: Recovering gRPC Dependency in Multilingual ProjectsProceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering10.1109/ASE56229.2023.00108(1930-1939)Online publication date: 11-Nov-2023
https://dl.acm.org/doi/10.1109/ASE56229.2023.00108
Li QLu MGu TWu Y(2022)Runtime Software Architecture-Based Reliability Prediction for Self-Adaptive SystemsSymmetry10.3390/sym1403058914:3(589)Online publication date: 16-Mar-2022
https://doi.org/10.3390/sym14030589
Casse CBerthou POwezarski PJosset S(2022)A Tracing Based Model to Identify Bottlenecks in Physically Distributed Applications2022 International Conference on Information Networking (ICOIN)10.1109/ICOIN53446.2022.9687217(226-231)Online publication date: 12-Jan-2022
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Gortney MHarris PCerny TMaruf ABures MTaibi DTisnovsky P(2022)Visualizing Microservice Architecture in the Dynamic Perspective: A Systematic Mapping StudyIEEE Access10.1109/ACCESS.2022.322113010(119999-120012)Online publication date: 2022
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Bouhamed MDíaz GChaoui AKamel ONouara R(2021)Models@Runtime: The Development and Re-Configuration Management of Python Applications Using Formal MethodsApplied Sciences10.3390/app1120974311:20(9743)Online publication date: 19-Oct-2021
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Sobhy DBahsoon RMinku LKazman R(2021)Evaluation of Software Architectures under UncertaintyACM Transactions on Software Engineering and Methodology10.1145/346430530:4(1-50)Online publication date: 3-Aug-2021
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