Anne Koziolek

Software Architecture Optimization Methods: A Systematic Literature Review

Due to significant industrial demands toward software systems with increasing complexity and challenging quality requirements, software architecture design has become an important development activity and the research domain is rapidly evolving. In the last decades, software architecture optimization methods, which aim to automate the search for an optimal architecture design with respect to a (set of) quality attribute(s), have proliferated. However, the reported results are fragmented over different research communities, multiple system domains, and multiple quality attributes. To integrate the existing research results, we have performed a systematic literature review and analyzed the results of 188 research papers from the different research communities. Based on this survey, a taxonomy has been created which is used to classify the existing research. Furthermore, the systematic analysis of the research literature provided in this review aims to help the research community in consolidating the existing research efforts and deriving a research agenda for future developments.

PerOpteryx: automated application of tactics in multi-objective software architecture optimization

Designing software architectures that exhibit a good trade-off between multiple quality attributes is hard. Even with a given functional design, many degrees of freedom in the software architecture (e.g. component deployment or server configuration) span a large design space. In current practice, software architects try to find good solutions manually, which is time-consuming, can be error-prone and can lead to suboptimal designs. We propose an automated approach guided by architectural tactics to search the design space for good solutions. Our approach applies multi-objective evolutionary optimization to software architectures modelled with the Palladio Component Model. Software architects can then make well-informed trade-off decisions and choose the best architecture for their situation. To validate our approach, we applied it to the architecture models of two systems, a business reporting system and an industrial control system from ABB. The approach was able to find meaningful trade-offs leading to significant performance improvements or costs savings. The novel use of tactics decreased the time needed to find good solutions by up to 80%.

Quantitative Evaluation of Model-Driven Performance Analysis and Simulation of Component-Based Architectures

During the last decade, researchers have proposed a number of model transformations enabling performance predictions. These transformations map performance-annotated software architecture models into stochastic models solved by analytical means or by simulation. However, so far, a detailed quantitative evaluation of the accuracy and efficiency of different transformations is missing, making it hard to select an adequate transformation for a given context. This paper provides an in-depth comparison and quantitative evaluation of representative model transformations to, e.g., queueing petri nets and layered queueing networks. The semantic gaps between typical source model abstractions and the different analysis techniques are revealed. The accuracy and efficiency of each transformation are evaluated by considering four case studies representing systems of different size and complexity. The presented results and insights gained from the evaluation help software architects and performance engineers to select the appropriate transformation for a given context, thus significantly improving the usability of model transformations for performance prediction.

Modeling run-time adaptation at the system architecture level in dynamic service-oriented environments

Today, software systems are more and more executed in dynamic, virtualized environments. These 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.

An industrial case study of performance and cost design space exploration

Determining the trade-off between performance and costs of a distributed software system is important as it enables fulfilling performance requirements in a cost-efficient way. The large amount of design alternatives for such systems often leads software architects to select a suboptimal solution, which may either waste resources or cannot cope with future workloads. Recently, several approaches have appeared to assist software architects with this design task. In this paper, we present a case study applying one of these approaches, i.e. PerOpteryx, to explore the design space of an existing industrial distributed software system from ABB. To facilitate the design exploration, we created a highly detailed performance and cost model, which was instrumental in determining a cost-efficient architecture solution using an evolutionary algorithm. The case study demonstrates the capabilities of various modern performance modeling tools and a design space exploration tool in an industrial setting,provides lessons learned, and helps other software architects in solving similar problems.

Towards a generic quality optimisation framework for component-based system models

Designing component-based systems (CBS) that exhibit a good trade-off between multiple quality criteria is hard. Even after functional design, many remaining degrees of freedom of different types (e.g. component deployment, component selection, server configuration) in the CBS span a large, discontinuous design space. Automated approaches have been proposed to optimise CBS models, but they only consider a limited set of degrees of freedom, e.g. they only optimise the selection of components without considering the deployment, or vice versa. We propose a flexible and extensible formulation of the design space for optimising any CBS model for a number of quality properties and an arbitrary number of degrees of freedom. With this design space formulation, a generic quality optimisation framework that is independent of the used CBS metamodel can apply multi-objective metaheuristic optimisation such as evolutionary algorithms.

Automated Improvement of Software Architecture Models for Performance and Other Quality Attributes

Quality attributes, such as performance or reliability, are crucial for the success of a software system and largely influenced by the software architecture. Their quantitative prediction supports systematic, goal-oriented software design and forms a base of an engineering approach to software design. This thesis proposes a method and tool to automatically improve component-based software architecture (CBA) models based on such quantitative quality prediction techniques.

Identifying outdated requirements based on source code changes

Keeping requirements specifications up-to-date when systems evolve is a manual and expensive task. Software engineers have to go through the whole requirements document and look for the requirements that are affected by a change. Consequently, engineers usually apply changes to the implementation directly and leave requirements unchanged. In this paper, we propose an approach for automatically detecting outdated requirements based on changes in the code. Our approach first identifies the changes in the code that are likely to affect requirements. Then it extracts a set of keywords describing the changes. These keywords are traced to the requirements specification, using an existing automated traceability tool, to identify affected requirements. Automatically identifying outdated requirements reduces the effort and time needed for the maintenance of requirements specifications significantly and thus helps preserve the knowledge contained in them. We evaluated our approach in a case study where we analyzed two consecutive source code versions and were able to detect 12 requirements-related changes out of 14 with a precision of 79%. Then we traced a set of keywords we extracted from these changes to the requirements specification. In comparison to simply tracing changed classes to requirements, we got better results in most cases.

Survivability models for the assessment of smart grid distribution automation network designs

Smart grids are fostering a paradigm shift in the realm of power distribution systems. Whereas traditionally different components of the power distribution system have been provided and analyzed by different teams through different lenses, smart grids require a unified and holistic approach that takes into consideration the interplay of communication reliability, energy backup, distribution automation topology, energy storage and intelligent features such as automated failure detection, isolation and restoration (FDIR) and demand response. In this paper, we present an analytical model and metrics for the survivability assessment of the distribution power grid network. The proposed metrics extend the system average interruption duration index (SAIDI), accounting for the fact that after a failure the energy demand and supply will vary over time during a multi-step recovery process. The analytical model used to compute the proposed metrics is built on top of three design principles: state space factorization, state aggregation and initial state conditioning. Using these principles, we reduce a Markov chain model with large state space cardinality to a set of much simpler models that are amenable to analytical treatment and efficient numerical solution. In the special case where demand response is not integrated with FDIR, we provide closed form solutions to the metrics of interest, such as the mean time to repair a given set of sections. We have evaluated the presented model using data from a real power distribution grid and we have found that survivability of distribution power grids can be improved by the integration of the demand response feature with automated FDIR approaches. Our empirical results indicate the importance of quantifying survivability to support investment decisions at different parts of the power grid distribution network.

Survivability analysis of power distribution in smart grids with active and reactive power modeling

A paradigm shift is taking place in the realm of power distribution networks. Power distribution networks that have been traditionally built to meet peak demand are now being automated to offer reliability on demand, i.e., smart distribution power grids can be automatically reconfigured after events such as power failures. In future distribution automation networks an important design decision will consist of which approach to use to avoid voltage drops. A standard approach is to add static capacitors to the distribution circuit. Novel techniques include the automatic reduction of active or reactive load through demand response, or the addition of distributed generators that can tradeoff active load for reactive load. In this paper, we introduce a new modeling approach to assist in such design decisions. The survivability of a system is its ability to function during and after a failure. In survivability analysis, the initial state of the system is set to a failure state, so survivability is “conditional performability” [9, 11]. The main contribution of this paper is the development of a model to study the power distribution in smart grids during the (transient) period that starts after a failure till the system fully recovers. The proposed model bridges power flow modeling of reactive power compensation [8, 14] with performability/survivability modeling of automation distribution networks [1]. We use a Markov chain to characterize the phased recovery of the system after a failure [5]. Then, we associate to each state of the Markov chain a set of corresponding rewards to characterize the active and reactive power supplied and demanded in that state.