Balancing Security and Performance Properties During System Architectural Design
Notice bibliographique
Résumé
Developers of critical systems need to address several quality properties, such as security and performance, in the early stages of the development cycle to ensure that the system under construction meets its requirements. Sometimes quality properties conflict with each other and/or with the system’s functionalities, so the developers need to make trade-off decisions. Unreasonable costs, added developer resources and tight project schedules may be other reasons for having to trade-off between alternative solutions. In the context of Model-Driven Development, the analysis of quality properties is done by transforming software design models into different analysis models based on various formalisms, which are then analyzed with existing tools. A major challenge is to integrate different models, transformations and tools into a consistent and coherent process. In this chapter the authors present a methodology called Aspect-Oriented Risk Driven Development (AORDD), which integrates the analysis of two quality properties, namely security and performance, into the development process of critical systems. Each quality property is analyzed separately, and then all results are input to a trade-off analysis that identifies conflicts between the properties. Trade-off analysis aims at supporting designers and developers in choosing the security and performance solutions that best fit their needs, without introducing unacceptable development delays or costs. The security analysis consists of identifying the assets (critical components, such as sensitive information) of an application and the attacks that can compromise these assets, and formally analyzing whether these attacks are actually possible using the tools UML2Alloy and Alloy Analyzer. If the system is vulnerable to the attack, some security solution, modeled as an aspect according to Aspect Oriented Modeling (AOM), is added to the system. The analysis must be repeated to ensure that the resulting system is secure. Performance analysis is accomplished using Layered Queuing Network (LQN) models. Annotated system models are transformed into LQN models and performance experiments are executed on them. If the performance results are unacceptable, the system design has to be changed and the analysis repeated. Finally, the results of the security and performance analysis are input to the system quality property trade-off analysis, which is implemented as a Bayesian Belief Network (BBN) topology, and which also takes as input external parameters, such as time to market and budget constraints. The results of the trade-off analysis help identify how well a particular design meets performance, security and other project goals, which, in turn, can guide the developer in making informed design decisions. The approach is illustrated using a transactional web e-commerce benchmark (TPC-W) originally developed by the Transaction Processing Performance Council.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».