CS 607

Hot Topics in Systems

Fall 2025

Course Information

Have you ever wondered what the hot topics in systems are? What research problems interest top system conferences (SOSP, OSDI, NSDI, EuroSys, ATC, VLDB, SIGMOD)? What are the types of papers that are getting published at these conferences?
In this world of AI/ML, every major company (The Big Seven) is still looking for system designers and researchers. What systems do these companies work on? How do they design these systems? Do you want that edge during the interviews?
In this course, we will explore some of the latest research areas in systems, reviewing the most cited works and gathering insights behind those systems.

Time: Tues 2pm - 3:20pm PST
Location: Deschutes 200

Instructor

Name	Office Hours	Location
Suyash Gupta	By Appointment	Deschutes 334

Syllabus

Objectives

This is an upper-level course where we will study and discuss research papers from top systems, database, networking, and security conferences. This course places a heavy emphasis on attendance and paper reading/discussions. Additionally, each student will work towards coming up with a new research idea, which can lead to a future publication. Upon successful completion of this course, a student will have broader understanding in the following topics:

• Replicated State Machines.
• Shared-Disaggregated Memory.
• Vector Databases.
• Fault Tolerance.
• Federated Learning.

Grading Scheme

The final grade for the course will be based approximately on the following weights:

• 60% - Paper Discussions and Summary
• 40% - Research Idea

Papers

Shared-Disaggregated Memory

• Motor: Enabling Multi-Versioning for Distributed Transactions on Disaggregated Memory. OSDI 2024.
• Understanding the Performance Implications of the Design Principles in Storage-Disaggregated Databases. SIGMOD 2024.
• LazyLog: A New Shared Log Abstraction for Low-Latency Applications. SOSP 2024.
• SWARM: Replicating Shared Disaggregated-Memory Data in No Time. SOSP 2024.
• FineMem: Breaking the Allocation Overhead vs. Memory Waste Dilemma in Fine-Grained Disaggregated Memory Management. OSDI 2025.
• Tigon: A Distributed Database for a CXL Pod. OSDI 2025.
• Low End-to-End Latency atop a Speculative Shared Log with Fix-Ante Ordering. OSDI 2025.
• Chimera: Mitigating Ownership Transfers in Multi-Primary Shared-Storage Cloud-Native Databases. VLDB 2025.
• Disaggregated State Management in Apache Flink 2.0. VLDB 2025.
• Cache Coherence Over Disaggregated Memory. VLDB 2025.
• Beehive: A Scalable Disaggregated Memory Runtime Exploiting Asynchrony of Multithreaded Programs. NSDI 2025.

Vector Databases

• Survey of Vector Database Management Systems. VLDB Journal 2024
• GaussDB-Vector: A Large-Scale Persistent Real-Time Vector Database for LLM Applications. VLDB 2025.
• Cost-Effective, Low Latency Vector Search with Azure Cosmos DB. VLDB 2025.
• Practical and Asymptotically Optimal Quantization of High-Dimensional Vectors in Euclidean Space for Approximate Nearest Neighbor Search. SIGMOD 2025.
• DEG: Efficient Hybrid Vector Search Using the Dynamic Edge Navigation Graph. SIGMOD 2025.
• Quake: Adaptive Indexing for Vector Search. OSDI 2025.

Federated Learning

• PS-MI: Accurate, Efficient, and Private Data Valuation in Vertical Federated Learning. VLDB 2025.
• FedVSE: A Privacy-Preserving and Efficient Vector Search Engine for Federated Databases. VLDB 2025.
• PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicality. NSDI 2025.
• Federated Heavy Hitter Analytics with Local Differential Privacy. SIGMOD 2025.
• SecureXGB: A Secure and Efficient Multi-party Protocol for Vertical Federated XGBoost. SIGMOD 2025.
• A Profit-Maximizing Data Marketplace with Differentially Private Federated Learning under Price Competition. SIGMOD 2025.

Schedule

Date	Paper	Presenters
Sep 30	Introduction.	Suyash Gupta
Oct 7	PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicality
Oct 7	LazyLog: A New Shared Log Abstraction for Low-Latency Applications
Oct 14	Quake: Adaptive Indexing for Vector Search
Oct 14	FedVSE: A Privacy-Preserving and Efficient Vector Search Engine for Federated Databases