https://dasya.itu.dk/supervisor/jens-birk-andersen/ Recent content in Jens Birk Andersen on DASYA Hugo -- gohugo.io en-us © DASYA, IT University of Copenhagen Wed, 05 Aug 2026 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/pito-llm-agents/ Wed, 05 Aug 2026 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/pito-llm-agents/ This project would investigate the storage access patterns of LLM agents using open-source state-of-the-art inference frameworks such as vLLM and SGLang and workload traces such as the ones released by Mooncake, Alibaba, Swiss AI Initiative, etc. Depending on the size and duration of the project, it can also propose or implement optimizations for the storage access. This project would be suitable as a standalone project, a research project with an MSc thesis followup, or BSc thesis. https://dasya.itu.dk/for-students/proposals/current/pito-llm-kvcache/ Wed, 05 Aug 2026 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/pito-llm-kvcache/ This project would investigate key-value(KV)-cache offloading in large language model (LLM) inference engines, with particular emphasis on data placement and storage efficiency. First, the project would use an emulator such as FEMU to characterize workload access patterns, including hot-key distributions, write intensity, and write amplification factor (WAF). Then, we would evaluate the potential of Flexible Data Placement (FDP) for improving data placement decisions on SSDs for KV-cache accesses. The project will use open source state-of-the-art KV-cache types such as LMCache or the ones implemented by the inference frameworks such as vLLM and SGLang.