Konstantinos Kanellis
12 papers 107 citations h-index 2 2017–2026
TUNA: Tuning Unstable and Noisy Cloud Applications
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 cloud arises...
LlamaTune: Sample-Efficient DBMS Configuration Tuning
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 approaches to...
Beyond Correctness: Resolving Underspecification in Agentic Text-to-SQL
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 agent has...
MANTA: Machine Learning Augmented Tiering Advisor
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 depends on keeping...
Too Many Knobs to Tune? Towards Faster Database Tuning by Pre-selecting Important Knobs
Reproducibility Report for ACM SIGMOD 2024 Paper: 'FACET: Robust Counterfactual Explanation Analytics'
ARMS: Adaptive and Robust Memory Tiering System
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 host in faster...
From FASTER to F2: Evolving Concurrent Key-Value Store Designs for Large Skewed Workloads
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 services are...
From Good to Great: Improving Memory Tiering Performance Through Parameter Tuning
Memory tiering systems achieve memory scaling by adding multiple tiers of memory wherein different tiers have different access latencies and bandwidth. For maximum performance, frequently accessed...