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20242026
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cs.DC2026

Duet instrumentation: An Agentic Approach to Improving Sensitivity in Cloud Service Benchmarking

Sebastian Koch, Nils Japke, David Bermbach

Continuous cloud service performance benchmarking is essential for detecting performance bugs early before deploying them to production. However, detecting performance regressions…

cs.DC2026

FaaSMoE: A Serverless Framework for Multi-Tenant Mixture-of-Experts Serving

Minghe Wang, Trever Schirmer, Mohammadreza Malekabbasi +1

Mixture-of-Experts (MoE) models offer high capacity with efficient inference cost by activating a small subset of expert models per input. However, deploying MoE models requires al…

cs.DC2026

New Kids: An Architecture and Performance Investigation of Second-Generation Serverless Platforms

Trever Schirmer, Aris Wiegand, Lucca di Benedetto +4

With the ever-increasing usage of serverless computing in both industry and academia, it is essential to understand the mechanisms that power the underlying platforms. As serverles…

cs.DC2026

DisCEdge: Distributed Context Management for Large Language Models at the Edge

Mohammadreza Malekabbasi, Minghe Wang, David Bermbach

Deploying Large Language Model (LLM) services at the edge benefits latency-sensitive and privacy-aware applications. However, the stateless nature of LLMs makes managing user conte…

cs.DC2026

Revealing the influence of participant failures on model quality in cross-silo Federated Learning

Fabian Stricker, David Bermbach, Christian Zirpins

Federated Learning (FL) is a paradigm for training machine learning (ML) models in collaborative settings while preserving participants' privacy by keeping raw data local. A key re…

cs.DC2026

Provuse: Platform-Side Function Fusion for Performance and Efficiency in FaaS Environments

Niklas Kowallik, Natalie Carl, Leon Pöllinger +3

Function-as-a-Service (FaaS) platforms provide scalable and cost-efficient execution but suffer from increased latency and resource overheads in complex applications comprising mul…