Alexander Fuerst
Computer Science · Indiana University
Publications
12
Citations
315
Est. group size
—
Recurring co-author estimate
Active years
15
Publishing since 2012
Alexander Fuerst studies cloud computing systems, with a focus on serverless computing (a model where developers run code without managing servers, and resources are allocated automatically). His work addresses how to schedule, cache, and balance workloads efficiently, including for GPU-accelerated functions, and how to measure and reduce the energy and carbon costs of running these systems. He also explores using idle or temporarily available cloud resources (such as memory-harvesting and transient servers) more effectively.
Publication activity has been steady at roughly one to two papers per year since 2020.
Generated by claude-opus-4-8 from public bibliographic data · Jul 11, 2026
- MQGPU: A Multi-Queue Scheduling Framework For GPU Accelerated Serverless Functions
2026
- MQFQ-Sticky: Fair Queueing For Serverless GPU Functions
arXiv (Cornell University) · 2025
- FaasMeter: Energy-First Serverless Computing
arXiv (Cornell University) · 2024
- Accountable Carbon Footprints and Energy Profiling For Serverless Functions
2024
- Ilúvatar: A Fast Control Plane for Serverless Computing
2023
- Memory-harvesting VMs in cloud platforms
2022
- Locality-aware Load-Balancing For Serverless Clusters
2022
- FaasCache: Keeping Serverless Computing Alive With Greedy-Dual Caching
Zenodo (CERN European Organization for Nuclear Research) · 2021
- Artifacts for 'FaasCache: Keeping Serverless Computing Alive with Greedy-Dual Caching'
Artifact Digital Object Group · 2021
- FaasCache: keeping serverless computing alive with greedy-dual caching
2021
- Cloud-scale VM-deflation for Running Interactive Applications On Transient Servers
2020
- arXiv (Cornell University)×2
- Artifact Digital Object Group×1
- Zenodo (CERN European Organization for Nuclear Research)×1
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 11, 2026.
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