11 citations · 15 across the 5 of their papers we have counts for
7 papers
Beyond Correctness: Resolving Underspecification in Agentic Text-to-SQL
Wen-Zhi Li, Yue Gong, Konstantinos Kanellis +1
Agentic Text-to-SQL systems can interact with users to clarify underspecified queries before generating SQL. However, a correct execution result does not necessarily imply that the…
MANTA: Machine Learning Augmented Tiering Advisor
Johannes Freischuetz, Kiet Pham, Sujay Yadalam +3
Memory tiering has been used to expand memory capacity, particularly in datacenters, by combining fast DRAM with slower tiers, including CXL-attached memory. Its effectiveness depe…
ARMS: Adaptive and Robust Memory Tiering System
Sujay Yadalam, Konstantinos Kanellis, Michael Swift +1
Memory tiering systems seek cost-effective memory scaling by adding multiple tiers of memory. For maximum performance, frequently accessed (hot) data must be placed close to the ho…
From Good to Great: Improving Memory Tiering Performance Through Parameter Tuning
Konstantinos Kanellis, Sujay Yadalam, Fanchao Chen +2
Memory tiering systems achieve memory scaling by adding multiple tiers of memory wherein different tiers have different access latencies and bandwidth. For maximum performance, fre…
TUNA: Tuning Unstable and Noisy Cloud Applications
Johannes Freischuetz, Konstantinos Kanellis, Brian Kroth +1
Autotuning plays a pivotal role in optimizing the performance of systems, particularly in large-scale cloud deployments. One of the main challenges in performing autotuning in the…
From FASTER to F2: Evolving Concurrent Key-Value Store Designs for Large Skewed Workloads
Konstantinos Kanellis, Badrish Chandramouli, Ted Hart +1
Modern large-scale services such as search engines, messaging platforms, and serverless functions, rely on key-value (KV) stores to maintain high performance at scale. When such se…
LlamaTune: Sample-Efficient DBMS Configuration Tuning
Konstantinos Kanellis, Cong Ding, Brian Kroth +3
Tuning a database system to achieve optimal performance on a given workload is a long-standing problem in the database community. A number of recent works have leveraged ML-based a…