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Found 8 papers, 0 papers with code
Sibyl: Forecasting Time-Evolving Query Workloads
Drawing insights from real-workloads, we propose template-based featurization techniques and develop a stacked-LSTM with an encoder-decoder architecture for accurate forecasting of query workloads.
GEqO: ML-Accelerated Semantic Equivalence Detection
Detecting common computation is the first and key step for reducing this computational redundancy.
Deploying a Steered Query Optimizer in Production at Microsoft
Modern analytical workloads are highly heterogeneous and massively complex, making generic query optimizers untenable for many customers and scenarios.
Predictive Price-Performance Optimization for Serverless Query Processing
We present an efficient, parametric modeling framework for predictive resource allocations, focusing on the amount of computational resources, that can optimize for a range of price-performance objectives for data analytics in serverless query processing settings.
Phoebe: A Learning-based Checkpoint Optimizer
To address these issues, we propose Phoebe, an efficient learning-based checkpoint optimizer.
Optimal Resource Allocation for Serverless Queries
We introduce a system for optimal resource allocation that can predict performance with aggressive trade-offs, for both new and past observed queries.
Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation
Microsoft Azure is dedicated to guarantee high quality of service to its customers, in particular, during periods of high customer activity, while controlling cost.
Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML
Consequently, rigorous data management has emerged as a key requirement in enterprise settings.
