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Researcher profile

Sebastian Werner

Sebastian Werner contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Distributed, Parallel, and Cluster ComputingSoftware EngineeringArtificial Intelligence

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Published work

4 published item(s)

preprint2026arXiv

Architectural Constraints Alignment in AI-assisted, Platform-based Service Development

AI-assisted development tools enable rapid prototyping of services but often lack awareness of architectural constraints, infrastructure dependencies, and organizational standards required in production environments. Consequently, generated artifacts may exhibit brittle behavior and limited deployability. We propose a retrieval-augmented scaffolding approach that combines platform-based code generation with agentic clarification loops to expose and resolve architectural constraint ambiguities. By combining template retrieval with structured interaction, the method embeds production-relevant considerations during service scaffolding. Evaluation indicates improved architectural consistency and deployability compared to general-purpose AI code generation workflows, suggesting that constraint-aware retrieval is essential for aligning AI-assisted service development with production software engineering practices.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Service-Level Energy Modeling and Experimentation for Cloud-Native Microservices

Microservice architectures have become the dominant paradigm for cloud-native systems, offering flexibility and scalability. However, this shift has also led to increased demand for cloud resources, contributing to higher energy consumption and carbon emissions. While existing research has focused on measuring fine-grained energy usage of CPU and memory at the container level, or on system-wide assessments, these approaches often overlook the energy impact of cross-container service interactions, especially those involving network and storage for auxiliary services such as observability and system monitoring. To address this gap, we introduce a service-level energy model that captures the distributed nature of microservice execution across containers. Our model is supported by an experimentation tool that accounts for energy consumption not just in CPU and memory, but also in network and storage components. We validate our approach through extensive experimentation with diverse experiment configurations of auxiliary services for a popular open-source cloud-native microservice application. Results show that omitting network and storage can lead to an underestimation of auxiliary service energy use by up to 63%, highlighting the need for more comprehensive energy assessments in the design of energy-efficient microservice architectures.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Hardless: A Generalized Serverless Compute Architecture for Hardware Processing Accelerators

The increasing use of hardware processing accelerators tailored for specific applications, such as the Vision Processing Unit (VPU) for image recognition, further increases developers' configuration, development, and management overhead. Developers have successfully used fully automated elastic cloud services such as serverless computing to counter these additional efforts and shorten development cycles for applications running on CPUs. Unfortunately, current cloud solutions do not yet provide these simplifications for applications that require hardware acceleration. However, as the development of specialized hardware acceleration continues to provide performance and cost improvements, it will become increasingly important to enable ease of use in the cloud. In this paper, we present an initial design and implementation of Hardless, an extensible and generalized serverless computing architecture that can support workloads for arbitrary hardware accelerators. We show how Hardless can scale across different commodity hardware accelerators and support a variety of workloads using the same execution and programming model common in serverless computing today.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Synthesizing Configuration Tactics for Exercising Hidden Options in Serverless Systems

A proper configuration of an information system can ensure accuracy and efficiency, among other system objectives. Conversely, a poor configuration can have a significant negative impact on the system's performance, reliability, and cost. Serverless systems, which are comprised of many functions and managed services, especially risk exposure to misconfigurations, with many provider- and platform-specific, often intransparent and 'hidden' settings. In this paper, we argue to pay close attention to the configuration of serverless systems to exercise options with known accuracy, cost and time. Based on a literature study and long-term serverless systems development experience, we present nine tactics to unlock potentially neglected and unknown options in serverless systems.

0 citations0 reviewsNo code linkedOpen access