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Software architects plan the realization of software systems by assessing design decisions on the basis of architectural models. Using these models as input, architectural analyses assess the impact of architects' decisions on quality-of-service properties. While the creation of suitable architectural models requires software architects to apply complex architectural knowledge, for example, in the form of established architectural styles and patterns, current architectural analyses lack support for directly reusing such knowledge. This lack points to an unused potential to make the work of software architects more effective and efficient. To use this potential, we introduce the architectural template (AT) method, an engineering method that makes design-time analyses of quality-of-service properties of software systems more efficient. The AT method allows to quantify quality-of-service properties on the basis of reusable modeling templates that capture recurring architectural knowledge. Architects just need to customize such templates with system-specific parts. In a case study, we illustrate the applicability of the AT method in the domains of distributed and cloud computing and its suitability for the quality-of-service properties performance, scalability, elasticity, and cost-efficiency. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Today's system developers and operators face the challenge of creating software systems that make efficient use of dynamically allocated resources under highly variable and dynamic load profiles, while at the same time delivering reliable performance. Autonomic controllers, for example, an advanced autoscaling mechanism in a cloud computing context, can benefit from an abstracted load model as knowledge to reconfigure on time and precisely. Existing workload characterization approaches have limited support to capture variations in the interarrival times of incoming work units over time (i.e., a variable load profile). For example, industrial and scientific benchmarks support constant or stepwise increasing load, or interarrival times defined by statistical distributions or recorded traces. These options show shortcomings either in representative character of load variation patterns or in abstraction and flexibility of their format. In this article, we present the Descartes Load Intensity Model (DLIM) approach addressing these issues. DLIM provides a modeling formalism for describing load intensity variations over time. A DLIM instance is a compact formal description of a load intensity trace. DLIM-based tools provide features for benchmarking, performance, and recorded load intensity trace analysis. As manually obtaining and maintaining DLIM instances becomes time consuming, we contribute three automated extraction methods and devised metrics for comparison and method selection. We discuss how these features are used to enhance systemmanagement approaches for adaptations during runtime, and how they are integrated into simulation contexts and enable benchmarking of elastic or adaptive behavior. We show that automatically extracted DLIM instances exhibit an average modeling error of 15.2% over 10 different real-world traces that cover between 2 weeks and 7 months. These results underline DLIM model expressiveness. In terms of accuracy and processing speed, our proposed extraction methods for the descriptive models are comparable to existing time series decomposition methods. Additionally, we illustrate DLIM applicability by outlining approaches of workload modeling in systems engineering that employ or rely on our proposed load intensity modeling formalism. [ABSTRACT FROM AUTHOR]