Distributed quality-attribute optimization of software architectures

Rago, Alejandro; Vidal, Santiago; Díaz-Pace, J. Andrés; Frank, Sebastian; Hoorn, André van

doi:10.1145/3132498.3132509

Cited by 17 publications

(10 citation statements)

References 18 publications

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“…Architecture-based MCO [2] builds on top of these prediction approaches and the application of architectural tactics to search for (pareto) optimal architectural candidates. Example MCO approaches supporting reliability and performance are ArcheOpterix [1], PerOpteryx [11], and SQuAT [28]. Like our study, those works focus on supporting architectural design or decision making.…”

Section: Related Workmentioning

confidence: 86%

Automatic Adaptation of Reliability and Performance Trade-Offs in Service- and Cloud-Based Dynamic Routing Architectures

Amiri

Zdun

Hoorn

et al. 2021

2021 IEEE 21st International Conference on Software Quality, Reliability and Security (QRS)

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Many different dynamic routing architectures are available, including sidecar-based routing, routing through a central entity such as an event store or gateway, or architectures with multiple routers. These architectures are currently based on vastly different implementation concepts, such as API Gateways, Message Brokers, or Service Proxies. We propose a new approach that abstracts all these architecture patterns using one Adaptive Dynamic Routers architecture. We hypothesize that a dynamic self-adaptation of the routing architecture is beneficial over any fixed architecture selections for reliability and performance trade-offs. That is, if encountered with traffic and load changes, our approach dynamically self-adapts between more central or distributed routing to optimize system reliability and performance. We evaluate our approach by analyzing our previously-measured data during an experiment of 1200 hours of runtime. Our extensive systematic evaluation with 1089 cases confirms that our hypothesis holds and our approach is beneficial in terms of reliability and performance. Moreover, we empirically validate our results on Google Cloud Platform infrastructure.

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Section: Related Workmentioning

confidence: 86%

Automatic Adaptation of Reliability and Performance Trade-Offs in Service- and Cloud-Based Dynamic Routing Architectures

Amiri

Zdun

Hoorn

et al. 2021

2021 IEEE 21st International Conference on Software Quality, Reliability and Security (QRS)

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“…(author?) have presented SQuAT [62], which is an extensible platform aimed at including flexibility in the definition of an architecture optimization problem. SQuAT supports models conforming to Palladio Component Model language, exploits LQN for performance evaluation, and PerOpteryx tactics for architectural changes.…”

Section: Layered Queueing Network Approachesmentioning

confidence: 99%

Many-Objective Optimization of Non-Functional Attributes based on Refactoring of Software Models

Cortellessa¹,

Pompeo²,

Stoico³

et al. 2023

Preprint

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“…Rango et al have presented SQuAT [47], an extensible platform aimed at including flexibility in the definition of an architecture optimization problem. SQuAT supports models conforming to Palladio Component Model language, exploits LQN for performance evaluation, and PerOpteryx tactics for architecture.…”

Section: B Layered Queueing Network Approachesmentioning

confidence: 99%

On the impact of Performance Antipatterns in multi-objective software model refactoring optimization

Cortellessa¹,

Pompeo²,

Stoico³

et al. 2021

Preprint

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Software quality estimation is a challenging and time-consuming activity, and models are crucial to face the complexity of such activity on modern software applications.One main challenge is that the improvement of distinctive quality attributes may require contrasting refactoring actions on an application, as for trade-off between performance and reliability. In such cases, multi-objective optimization can provide the designer with a wider view on these trade-offs and, consequently, can lead to identify suitable actions that take into account independent or even competing objectives.In this paper, we present an approach that exploits the NSGA − II multi-objective evolutionary algorithm to search optimal Pareto solution frontiers for software refactoring while considering as objectives: i) performance variation, ii) reliability, iii) amount of performance antipatterns, and iv) architectural distance. The algorithm combines randomly generated refactoring actions into solutions (i.e., sequences of actions) and compares them according to the objectives.We have applied our approach on a train ticket booking service case study, and we have focused the analysis on the impact of performance antipatterns on the quality of solutions. Indeed, we observe that the approach finds better solutions when antipatterns enter the multi-objective optimization. In particular, performance antipatterns objective leads to solutions improving the performance by up to 15% with respect to the case where antipatterns are not considered, without affecting the solution quality on other objectives.

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Distributed quality-attribute optimization of software architectures

Cited by 17 publications

References 18 publications

Automatic Adaptation of Reliability and Performance Trade-Offs in Service- and Cloud-Based Dynamic Routing Architectures

Automatic Adaptation of Reliability and Performance Trade-Offs in Service- and Cloud-Based Dynamic Routing Architectures

Many-Objective Optimization of Non-Functional Attributes based on Refactoring of Software Models

On the impact of Performance Antipatterns in multi-objective software model refactoring optimization

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