PUMA publications for /user/rss/PerformanceThu Feb 05 17:18:36 CET 2026Softwaretechnik-Trends Band 45, Heft 1The Slingshot Simulator: An Architectural Overview2025myown simulator performance architecture Thu Feb 05 17:13:18 CET 2026Softwaretechnik-Trends Band 45, Heft 1The Slingshot Simulator: An Architectural Overview2025myown slingshot simulation performance Thu Feb 05 17:07:02 CET 2026Journal of Systems and Software112432An architectural view type for elasticity modeling and simulation—The Slingshot approach2282025myown Performance Analysis, Elasticity Simulation, Policies, rss Modeling, view, architectural The cloud computing model enables the on-demand provisioning of computing resources, reducing manual management, increasing efficiency, and improving environmental impact. Software architects now play a strategic role in designing and deploying elasticity policies for automated resource management. However, creating policies that meet performance and cost objectives is complex. Existing approaches, often relying on formal models like Queueing Theory, require advanced skills and lack specific methods for representing elasticity within architectural models. This paper introduces an architectural view type for modeling and simulating elasticity, supported by the Scaling Policy Definition (SPD) modeling language, a visual notation, and precise simulation semantics. The view type is integrated into the Palladio ecosystem, providing both conceptual and tool-based support. We evaluate the approach through two single-case experiments and a user study. In the first experiment, simulations of elasticity policies demonstrate sufficient accuracy when compared to load tests, showing the utility of simulations for evaluating elasticity. The second experiment confirms feasibility for larger applications, though with increased simulation times. The user study shows that participants completed 90% of tasks, rated the usability at 71%, and achieved an average score of 76% in nearly half the allocated time. However, the empirical evidence suggests that modeling with this architectural view requires more time than modeling control flow, resource environments, or usage profiles, despite its benefits for elasticity policy design and evaluation.Thu Feb 05 17:02:20 CET 2026ChamSoftware Architecture. ECSA 2025 Tracks and Workshops141--154LLM-Based Explainability at Design Time: Detecting Elasticity Antipatterns in Software Architectures2025rss myown models explainability language large performance threshold-based As software architecture grows in complexity, understanding the implications of design decisions becomes increasingly challenging. Large Language Models (LLMs) offer new opportunities for enhancing explainability during architecture modeling and evaluation by generating natural language explanations that support comprehension, learning, and decision-making. This potential is particularly valuable in domains with increased technical complexity---such as elasticity in cloud-based systems. In this work, we integrate and evaluate LLM-based explanations in supporting design-time evaluation of software architectures, focusing on the detection of elasticity antipatterns. Elasticity antipatterns are flawed autoscaling policy configurations that potentially lead to inefficient or unreliable system behavior. We extend an existing modeling and simulation approach with a novel feature that generates contextualized, textual explanations derived from simulation data. These explanations aim to guide architects in understanding scaling behaviors, identifying design issues, and refining their models. Our contribution includes the conceptualization of explanation types relevant to elasticity modeling, the design of prompt templates to elicit effective responses from LLMs, and an evaluation of the generated explanations' usefulness and quality. Results indicate that LLM-assisted feedback enhances the interpretability of elasticity models and supports the early identification of antipatterns, albeit with some limitations in precision and conciseness with only a slight agreement between expert evaluations ({\$}{\$}{\backslash}kappa {\$}{\$}$\kappa$ = 0.202). The explanation quality across types of explanations differs. Even though most explanations contain factual information, a large portion was deemed as imprecise especially in explaining problem and solution, the policy and target and service level objectives.Thu Jun 29 07:06:15 CEST 2023Softwaretechnik-Trends325--27An Architectural Template for Parallel Loops and Sections392019myown from:alirezahakamian pcm prediction performance Thu Jun 29 07:04:17 CEST 2023{IEEE} International Conference on Software Architecture Companion, {ICSA} Companion 2019, Hamburg, Germany, March 25-26, 201927--30A Process Model for Elastic and Resilient IoT Applications with Emergent Behaviors2019myown processes from:alirezahakamian elasticity pcm prediction resilience performance Mon Sep 19 11:56:26 CEST 2022New York, NY, USACompanion of the 2022 ACM/SPEC International Conference on Performance Engineering753–60ICPE '22Beauty and the Beast: A Case Study on Performance Prototyping of Data-Intensive Containerized Cloud Applications2022myown from:klinaku cloud-computing performance threshold-based 14Data-intensive container-based cloud applications have become popular with the increased use cases in the Internet of Things domain. Challenges arise when engineering such applications to meet quality requirements, both classical ones like performance and emerging ones like resilience. There is a lack of reference use cases, applications, and experiences when prototyping such applications that could benefit the research community. Moreover, it is hard to generate realistic and reliable workloads that exercise the resources according to a specification. Hence, designing reference applications that would exhibit similar performance behavior in such environments is hard. In this paper, we present a work in progress towards a reference use case and application for data-intensive containerized cloud applications having an industrial motivation. Moreover, to generate reliable CPU workloads we make use of ProtoCom, a well-known library for the generation of resource demands, and report the performance under various quality requirements in a Kubernetes cluster of moderate size. Finally, we present the scalability of the current solution assuming a particular autoscaling policy. Results of the calibration show high variability of the ProtoCom library when executed in a cloud environment. We observe a moderate association between the occupancy of node and the relative variability of execution time.