https://dasya.itu.dk/tags/modern-storage/ Recent content in modern storage 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-fdpsim-duckdb/ Wed, 05 Aug 2026 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/pito-fdpsim-duckdb/ Traditionally solid-state drives (SSDs) does not give the users the ability to control the data placement on the SSD. This often leads to suboptimal performance and lowers SSD lifetime, since SSDs internally don’t allow in-place updates. The updated disk pages are written elsewhere and the old versions have to be garbage collected. This poses problems if data with different lifetimes and update frequencies often gets written in the same region. 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. https://dasya.itu.dk/for-students/proposals/archive/pito-xnvme-duckdb/ Tue, 15 Jul 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-xnvme-duckdb/ A recent MSc thesis project at ITU integrated xNVMe to DuckDB. DuckDB, like many popular database systems, rely on a syncronous IO backend and the POSIX filesystem interfaces for its ease of use. xNVMe, in contrast, allows a unified interface for alternative and more performant IO backends such as io-uring and spdk. Thus, integration of xNVMe into DuckDB offers interesting avenues for data analytics workloads run on DuckDB. The existing integration of xNVMe to DuckDB is preliminary. https://dasya.itu.dk/for-students/proposals/archive/pito-gpu-db/ Tue, 15 Jul 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-gpu-db/ This rise of hardware accelerators to meet the demand of AI workloads has also led to a variety of novel methods to leverage GPUs for traditional data analytics workloads. A key concern for any data-intensive system using GPUs is the efficiency of moving the data to the accelerator. In this project, we will investigate ways to improve data movement to GPUs by focusing on the steps of the data path and performance of different data movement options from the network or SSDs to GPUs such as GPUDirect or BaM [1]. https://dasya.itu.dk/for-students/proposals/archive/pito-fdp-duckdb/ Mon, 13 May 2024 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-fdp-duckdb/ Traditionally solid-state drives (SSDs) does not give the users the ability to control the data placement on the SSD. This often leads to suboptimal performance and lowers SSD lifetime, since SSDs internally don’t allow in-place updates. The updated disk pages are written elsewhere and the old versions have to be garbage collected. This poses problems if data with different lifetimes and update frequencies often gets written in the same region. https://dasya.itu.dk/for-students/proposals/archive/diskaccesstracing/ Mon, 01 Aug 2022 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/diskaccesstracing/ In the past decade, data management community has focused on main-memory systems or main-memory-optimized systems. This focus has put the commodity memory hierarchy (DRAM and processor caches) into center when it comes to workload characterization studies. Today, with the evolution of persistent storage technologies such as NVRAM (persistent memory solution of Intel) and NVMe SSDs, data systems have access to orders of magnitude faster persistent storage than traditional hard disks. Recently developed data management systems (e.