Key-Value Stores under Disaggregation
What breaks when an LSM engine's storage moves away from its compute, and can it stay fast, tunable, and encrypted once it does?
- 3× SIGMOD
- CaaS-LSM · SHIELD · O3-LSM
- Encryption & offloading
Ph.D. Student · Arizona State University
I teach databases new tricks and poke at storage engines until they spill their secrets.
Hi, 👋 I’m a perpetually-curious CS Ph.D. student who gets way too excited about LSM-based key-value stores, disaggregated infrastructure, and storage systems for AI/ML. My favorite thing is taking a big, intimidating system apart just to see what makes it tick (sometimes literally), and then, on a good day, putting it back together a little bit better than I found it.
What breaks when an LSM engine's storage moves away from its compute, and can it stay fast, tunable, and encrypted once it does?
How do you hold a globally distributed, client-side cache coherent, and what is the system underneath actually costing you?
Can an LLM tune a storage engine end to end, and where exactly does that capability stop?
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Our paper “TellyTune: Exploring the Limits of LLM-Driven Tuning for LSM-Based Key-Value Stores” was accepted to SYSTOR 2026! 📄
Honored to be ASU’s nominee for the Google PhD Fellowship Award! 🎉
Presented “SHIELD: Encrypting Persistent Data of LSM-KVS from Monolithic to Disaggregated Storage” at ACM SIGMOD 2025! 🔐