Architectural Design Decisions Research Articles

Impact of Urban Street Network on BIPV Generation Capacity of Buildings

Climate change necessitates a critical reconsideration of the built environment since buildings are among the top fossil fuel consumers. Solar energy generation through building integrated photovoltaic (BIPV) systems is one of the most common onsite energy generation methods. However, many factors regarding urban morphology can negatively affect BIPV generation. Urban block typologies and spatial patterns are commonly studied descriptive metrics of an urban morphology that affect the solar energy potential. Similarly, the street network pattern is a measure of the spatial quality of an urban environment. Although various urban morphology indicators have been extensively studied in relation to solar energy potential, a comparative analysis of urban fabric focusing on street network patterns is also needed. In this study, four representative urban areas with different morphological characteristics are studied. The selected morphologies are parametrically modelled and compared with different building height configurations. A comparative analysis of BIPV generation capacity per square meter façade or roof area is presented. Urban areas without a dominating street network pattern have resulted in greater PV generation on facades, whereas the impact of urban morphology was found negligible for roof PV potential. The findings of this research have the potential to aid in urban planning and architectural design decisions, as well as the efficient use of BIPV systems in diverse urban morphologies.

Developing a Microservices Integration Layer for Next-Generation Rail Operations Centers

This experience report provides insights into architectural challenges, design decisions, and lessons learned developing a microservices-based integration layer for Rail Operations Centre (ROC) systems. The discussed project is part of a Hitachi Rail R&D initiative to develop a modern solution that standardizes the integration of Hitachi Rail signaling and traffic management applications. We describe the system architecture, discuss the microservice API considerations, and share the lessons learned and the experiences operating a simplified implementation of the integration layer microservice API in the autonomous freight train project AutoHaul.

Conformance Assessment of Architectural Design Decisions on API Endpoint Designs Derived from Domain Models

Conformance Assessment of Architectural Design Decisions on API Endpoint Designs Derived from Domain Models

Conformance assessment of Architectural Design Decisions on API endpoint designs derived from domain models

Context:Domain-driven design (DDD) is commonly used to design microservices. A crucial aspect of microservice design is API design, which includes the design of API endpoints. Objective:Our objective is to automate the assessment of conformance to Architectural Design Decisions (ADDs) on the interrelation of DDD and APIs. In particular, we studied link mapping, API operation design, and resource segregation as API endpoint design issues that are linked to domain model design. We particularly aim to address conformance checking in the context of frequent release practices, as frequent manual conformance checking is difficult or infeasible. Methods:We suggest a new approach for the automated assessment of conformance to ADD options. The approach suggests automated detectors to detect ADD options selected in a given API endpoint design, as well as an assessment scoring scheme based on empirical results. For the evaluation of our approach, we first manually created a ground truth for 12 cases in a multi-case study, and then compared the results of our automated detectors to the ground truth for each of those cases. Results:With our approach, all ADD options in our multi-case study possibly can be automatically detected. Without further improvements, our approach identifies 83% of the decision points in the multi-case study correctly. A statistical analysis of our data shows only a negligible effect size for differences to the ground truth. Conclusion:Our new approach provides a pragmatic method for automated detection of conformance to ADDs on the interrelation of DDD and APIs. The approach can support the continuous analysis of API endpoint designs.

The Impacts of Early Architectural Design Decisions on Building Performance

The early architectural design involves the most salient decisions. However, because of the large amount of variance, the decision-making is highly arduous. This article presents a methodology to enable the most effective design variables to be selected within the most effective value range by presenting a method that allows the measurement of output uncertainty depending on the impact of design decisions on outputs. The methodology was tested with different building functions and climate regions using two-phase sensitivity analysis. The values of design variables were generated with quasi-random sampling. They were sorted with factor prioritization. Ineffective variables were eliminated with factor fixing. Advanced global sensitivity analyses were performed for the total effect. Factor mapping was applied with the output weighting. The results were presented with Parallel Coordinate Plot (PCP). The designers can make selections from alternatives with PCP. Finally, the study demonstrated how climate and building functions should be considered for building performance.

A Study on Blockchain Architecture Design Decisions and Their Security Attacks and Threats

Blockchain is a disruptive technology intended to implement secure decentralised distributed systems, in which transactional data can be shared, stored, and verified by participants of the system without needing a central authentication/verification authority. Blockchain-based systems have several architectural components and variants, which architects can leverage to build secure software systems. However, there is a lack of studies to assist architects in making architecture design and configuration decisions for blockchain-based systems. This knowledge gap may increase the chance of making unsuitable design decisions and producing configurations prone to potential security risks. To address this limitation, we report our comprehensive systematic literature review to derive a taxonomy of commonly used architecture design decisions in blockchain-based systems. We map each of these decisions to potential security attacks and their posed threats. MITRE’s attack tactic categories and Microsoft STRIDE threat modeling are used to systematically classify threats and their associated attacks to identify potential attacks and threats in blockchain-based systems. Our mapping approach aims to guide architects to make justifiable design decisions that will result in more secure implementations.

Architectural Design Decisions for the Machine Learning Workflow

We conducted a qualitative investigation of architectural decisions faced by practitioners and modeled current practices in machine learning. We describe a subset of the architectural decisions that were modeled, discuss uses for the model, and outline areas in which further research may be pursued.

On the Accuracy of Analog Neural Network Inference Accelerators

Specialized accelerators have recently garnered attention as a method to reduce the power consumption of neural network inference. A promising category of accelerators utilizes nonvolatile memory arrays to both store weights and perform $\textit{in situ}$ analog computation inside the array. While prior work has explored the design space of analog accelerators to optimize performance and energy efficiency, there is seldom a rigorous evaluation of the accuracy of these accelerators. This work shows how architectural design decisions, particularly in mapping neural network parameters to analog memory cells, influence inference accuracy. When evaluated using ResNet50 on ImageNet, the resilience of the system to analog non-idealities - cell programming errors, analog-to-digital converter resolution, and array parasitic resistances - all improve when analog quantities in the hardware are made proportional to the weights in the network. Moreover, contrary to the assumptions of prior work, nearly equivalent resilience to cell imprecision can be achieved by fully storing weights as analog quantities, rather than spreading weight bits across multiple devices, often referred to as bit slicing. By exploiting proportionality, analog system designers have the freedom to match the precision of the hardware to the needs of the algorithm, rather than attempting to guarantee the same level of precision in the intermediate results as an equivalent digital accelerator. This ultimately results in an analog accelerator that is more accurate, more robust to analog errors, and more energy-efficient.

On Memristors for Enabling Energy Efficient and Enhanced Cognitive Network Functions

The high performance requirements of nowadays computer networks are limiting their ability to support important requirements of the future. Two important properties essential in assuring cost-efficient computer networks and supporting new challenging network scenarios are operating energy efficient and supporting cognitive computational models. These requirements are hard to fulfill without challenging the current architecture behind network packet processing elements such as routers and switches. Notably, these are currently dominated by the use of traditional transistor-based components. In this article, we contribute with an in-depth analysis of alternative architectural design decisions to improve the energy footprint and computational capabilities of future network packet processors by shifting from transistor-based components to a novel component named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Memristor</i> . A memristor is a computational component characterized by non-volatile operations on a physical state, mostly represented in form of (electrical) resistance. Its state can be read or altered by input signals, e.g. electrical pulses, where the future state always depends on the past state. Unlike in traditional von Neumann architectures, the principles behind memristors impose that memory operations and computations are inherently colocated. In combination with the non-volatility, this allows to build memristors at nanoscale size and significantly reduce the energy consumption. At the same time, memristors appear to be highly suitable to model cognitive functionality due to the state dependence transitions in the memristor. In cognitive architectures, our survey contributes to the study of memristor-based Ternary Content Addressable Memory (TCAM) used for storage of cognitive rules inside packet processors. Moreover, we analyze the memristor-based novel cognitive computational architectures built upon self-learning capabilities by harnessing from non-volatility and state-based response of memristors (including reconfigurable architectures, reservoir computation architectures, neural network architectures and neuromorphic computing architectures).

Participatory design in refugee camps: Ethnographic case studies from Greece

PurposeArchitectural management and construction practices have not been widely explored in refugee camps which have been growing exponentially around the world. Previous research largely focused on the negativity surrounding living in refugee camps and mostly ignored the input of refugees. This study explores the possibilities of involving refugees in architectural design and construction decisions in the camps.Design/methodology/approachThis research adopts a qualitative interpretive research approach and employs exploratory ethnographic methods. Participatory design (PD) principles are applied to design and construct community place projects in two refugee camps in Greece.FindingsThe findings demonstrate that, despite the technical and institutional challenges of employing PD approaches in refugee camps, there are observed positive impacts on the wellbeing of refugees and impacts on the hosting communities.Practical implicationsThis paper contests the negativity surrounding refugee camps and has implications on research, practice and society as well as a positive impact on NGO organisations, policy-makers and other stakeholders involved in the governance of refugee camps.Originality/valueThis paper addresses a critical issue concerning how to include refugees in the design, implementation and maintenance of refugee camps to improve their well-being and fight the feeling of “otherness” for both refugees and host communities. This study extends research on refugee camps by collaborating with refugees to improve their lives within the camps. This research contributes to architectural management and construction studies by providing practical recommendations related to PD methods in new contexts.

FEATURES OF URBAN PLANNING SOLUTIONS AND THE EVOLUTION OF SETTLEMENTS FOR DISPLACED PERSONS FROM THE CHERNOBYL ZONE

In the article, the author presents the results of her full-scale studies of the evolution of settlements for migrants from the Chernobyl zone, built in 1986-1987 of the twentieth century. Strategies and consequences of their placement, integration into the structure of existing villages, features of planning blocks, rural estates, residential and outbuildings are considered. The article analyzes the consequences of architectural and urban planning design decisions made 30 years after their implementation. Nowadays in Ukraine, there is a situation where villages and urban-type settlements urgently need to develop or update new general plans that would correspond to the present. It is known that currently there is a certain proportion of villages in Ukraine that do not have general plans at all or have them outdated. Taking into account all aspects of this problem, the results of the design, construction and evolution of villages for displaced persons from the Chernobyl zone are interesting and useful. Taking into account the tragic circumstances that led to their appearance, these villages represent a large-scale urban planning experiment in the field of rural housing construction, which represents the embodiment of the latest knowledge in the theory of village architecture in the mid-80s of the twentieth century. It was 30 years after the construction and settlement of these villages that it became possible to check the compliance of design standards with their compliance with the real needs of the village, from the standpoint of the current design standards, which are fundamentally no different from the design standards of 1986. Comparing the results of research (the study of general plans of villages of displaced persons and general plans that are being developed at the present stage), it turned out that the basis of those villages for Chernobyl victims was the main goal of socialist ideology, namely, erasing the differences between the city and the village. The author sees in modern design a similar problem, which directly leads to further degradation of the Ukrainian countryside. And nowadays, just against the background of this phenomenon, it is time for urbanists to understand the difference between a block of manor development for citizens and a rural street of a Ukrainian village.

Architectural design decisions that incur technical debt — An industrial case study

During software development, some architectural design decisions incur technical debt, either deliberately or inadvertently. These have serious impact on the quality of a software system, and can cost significant time and effort to be changed. While current research efforts have explored general concepts of architectural design decisions and technical debt separately, debt-incurring architectural design decisions have not been specifically explored in practice.

Augmented Markov Model for Enhancing Hazard Rate Result of Safety Critical Systems Possessing Diverse Redundant Architecture

Safety-critical systems in various industries such as transportation or nuclear energy have been paid more attention with the development of societies due to increased attachment of importance to the life of human, their property, and nature. While developing such systems, detailed availability and safety characteristics are to be taken into account in parallel with architectural design decisions such as synchronization between different computing units or real-time task management. For fulfilling top-level requirements in international standards, ambitious quantitative targets like 0.012 FIT for HW units are to be reached where the industry has difficulties to achieve it. In this paper, this problem is handled by proposing an augmented Markov model for diverse architectures that is superior to the formulas provided in the main safety standard IEC 61508 and previous studies. With the proposed method it is possible to cover all safety-relevant states, which leads to more accuracy and lower hazard rates helping to reach these ambitious quantitative targets. Besides, the reliability parameters are investigated and optimized to increase safety performance. Consequently, the proposed novel model including enhanced reliability parameters is used for an industry application, namely safety-critical computer used for unmanned metro and high-speed rail transportation. The result obtained by the proposed model is compared with the results obtained using state of art models in literature and using the formulas in IEC 61508. As domain independent references IEC 61508 and Markovian approach are used in the paper, this study is applicable to other safety critical areas such as automotive or avionic industry.

Dynamic Evaluation of Microservice Granularity Adaptation

Microservices have gained acceptance in software industries as an emerging architectural style for autonomic, scalable, and more reliable computing. Among the critical microservice architecture design decisions is when to adapt the granularity of a microservice architecture by merging/decomposing microservices. No existing work investigates the following question: How can we reason about the trade-off between predicted benefits and cost of pursuing microservice granularity adaptation under uncertainty? To address this question, we provide a novel formulation of the decision problem to pursue granularity adaptation as a real options problem. We propose a novel evaluation process for dynamically evaluating granularity adaptation design decisions under uncertainty. Our process is based on a novel combination of real options and the concept of Bayesian surprises. We show the benefits of our evaluation process by comparing it to four representative industrial microservice runtime monitoring tools, which can be used for retrospective evaluation for granularity adaptation decisions. Our comparison shows that our process can supersede and/or complement these tools. We implement a microservice application—Filmflix—using Amazon Web Service Lambda and use this implementation as a case study to show the unique benefit of our process compared to traditional application of real options analysis.

Computerized DSS for evaluating design performance of residential buildings using additive weighting approach

ABSTRACT Early architectural decisions have an enormous impact on the long-term performance of buildings. Evaluating architectural designs remains a subjective and biased process that varies from one person to another. Based on three questionnaires with 37 expert architects, seven Architectural Design Variables (ADVs) were identified, with a total of 40 design options that suit residential buildings. The experts defined the degree by which each ADV decision positively or negatively affect multiple life-cycle performance criteria: space functionality; construction time and cost; operational performance; and aesthetics. Based on that, this paper presents a novel Architectural Design Decision Support System (AD-DSS) to automatically evaluate and rank any combination of early design, using the Multi-Criteria Decision Analysis (MCDA) Simple Additive Weighting (SAW) method. The AD-DSS can automatically evaluate any combination of architectural design in terms of overall performance or a specific aspect without subjective inputs. Besides, a simple-user program developed to facilitate multiple system functions. A case study of a residential building with five different design alternatives demonstrated the powerful ability of the AD-DSS to provide early decision advice in a systematic and informed way to project stakeholders.

Evolution of the Unix System Architecture: An Exploratory Case Study

Unix has evolved for almost five decades, shaping modern operating systems, key software technologies, and development practices. Studying the evolution of this remarkable system from an architectural perspective can provide insights on how to manage the growth of large, complex, and long-lived software systems. Along main Unix releases leading to the FreeBSD lineage we examine core architectural design decisions, the number of features, and code complexity, based on the analysis of source code, reference documentation, and related publications. We report that the growth in size has been uniform, with some notable outliers, while cyclomatic complexity has been religiously safeguarded. A large number of Unix-defining design decisions were implemented right from the very early beginning, with most of them still playing a major role. Unix continues to evolve from an architectural perspective, but the rate of architectural innovation has slowed down over the system's lifetime. Architectural technical debt has accrued in the forms of functionality duplication and unused facilities, but in terms of cyclomatic complexity it is systematically being paid back through what appears to be a self-correcting process. Some unsung architectural forces that shaped Unix are the emphasis on conventions over rigid enforcement, the drive for portability, a sophisticated ecosystem of other operating systems and development organizations, and the emergence of a federated architecture, often through the adoption of third-party subsystems. These findings have led us to form an initial theory on the architecture evolution of large, complex operating system software.

ARINC653 Channel Robustness Verification Using LeonViP-MC, a LEON4 Multicore Virtual Platform

Multicore hardware platforms are being incorporated into spacecraft on-board systems to achieve faster and more efficient data processing. However, such systems lead to increased complexity in software development and represent a considerable challenge, especially concerning the runtime verification of fault-tolerance requirements. To address the ever-challenging verification of this kind of requirement, we introduce a LEON4 multicore virtual platform called LeonViP-MC. LeonViP-MC is an evolution of a previous development called Leon2ViP, carried out by the Space Research Group of the University of Alcalá (SRG-UAH), which has been successfully used in the development and testing of the flight software of the instrument control unit (ICU) of the energetic particle detector (EPD) on board the Solar Orbiter. This paper describes the LeonViP-MC architectural design decisions oriented towards fault-injection campaigns to verify software fault-tolerance mechanisms. To validate the simulator, we developed an ARINC653 communications channel that incorporates fault-tolerance mechanisms and is currently being used to develop a hypervisor level for the GR740 platform.

A Layered Reference Architecture for Metamodels to Tailor Quality Modeling and Analysis

Nearly all facets of our everyday life strongly depend on software-intensive systems. Besides correctness, highly relevant quality properties of these systems include performance, as directly perceived by the user, and maintainability, as an important decision factor for evolution. These quality properties strongly depend on architectural design decisions. Hence, to ensure high quality, research and practice is interested in approaches to analyze the system architecture for quality properties. Therefore, models of the system architecture are created and used for analysis. Many different languages (often defined by metamodels) exist to model the systems and reason on their quality. Such languages are mostly specific to quality properties, tools or development paradigms. Unfortunately, the creation of a specific model for any quality property of interest and any different tool used is simply infeasible. Current metamodels for quality modeling and analysis are often not designed to be extensible and reusable. Experience from generalizing and extending metamodels result in hard to evolve and overly complex metamodels. A systematic way of creating, extending and reusing metamodels for quality modeling and analysis, or parts of them, does not exist yet. When comparing metamodels for different quality properties, however, substantial parts show quite similar language features. This leads to our approach to define the first reference architecture for metamodels for quality modeling and analysis. A reference architecture in software engineering provides a general architecture for a given application domain. In this paper, we investigate the applicability of modularization concepts from object-oriented design and the idea of a reference architecture to metamodels for quality modeling and analysis to systematically create, extend and reuse metamodel parts. Thus, the reference architecture allows to tailor metamodels. Requirements on the reference architecture are gathered from a historically grown metamodel. We specify modularization concepts as a foundation of the reference architecture. Detailed application guidelines are described. We argue the reference architecture supports instance compatibility and non-intrusive, independent extension of metamodels. In four case studies, we refactor historically grown metamodels and compare them to the original metamodels. The study results show the reference architecture significantly improves evolvability as well as need-specific use and reuse of metamodels.

Mining software architecture knowledge: Classifying stack overflow posts using machine learning

AbstractSoftware Architectural Process (SAP) is a core and excessively knowledge intensive phase of software development life cycle, as it consumes and produces knowledge artifacts, simultaneously. SAP is about making design decisions, and the changes in these verdicts may pose adverse effects on software projects. The performance and properties of software components are fundamentally influenced by the design decisions. The implementation of immature and abrupt design decisions seriously threatens the development process of SAP. Moreover, software architectural knowledge management (AKM) approaches offer systematic ways to support SAP through versatile architectural solutions and design decisions. However, the majority of software organizations have limited access to data and still depend upon manually created and maintained AKM process. In this paper, we have utilized the one of the most prominent online community for software development (i.e., Stack Overflow) as a source of SAP knowledge to support AKM. In order to support AKM, we have proposed a supervised machine learning‐based approach to classify the architectural knowledge into predefined categories, that is, analysis, synthesis, evaluation, and implementation. We have employed different combinations of feature selection technique to achieve the optimal classification results of the used classifiers (Support Vector Machine [SVM], K‐Nearest Neighbor, Random Forest, and Naive Bayes [NB]). Among these classifiers, SVM with Uni‐gram feature set provides best classification results and attains 85.80% accuracy. For evaluating the proposed approach's effectiveness, we have also computed the suitability of the classifiers, that is, the cost of computation along with its accuracy, and NB with Uni‐gram feature set proved to be the most suitable.

Static power model for CMOS and FPGA circuits

In Ultra-Low-Power (ULP) applications, power consumption is a key parameter for process independent architectural level design decisions. Traditionally, time-consuming Spice simulations are used to measure the static power consumption. Herein, a technology-independent static power estimation model is presented, which can estimate static power with reasonable accuracy in much less time. It is shown that active area only is not a good indicator for static power consumption, hence in this model, the effects of transistor sizing, transistor stacking, gate boosting and voltage change are considered. The procedure to apply this model to processors and FPGAs is demonstrated. Across different process technologies, compared to traditional spice simulation, this model can estimate the static power consumption of processor with an error of 1%–4%, while static power consumption of an FPGA system with an error of 1%–15%.