Performance Prediction for Concurrent Database Workloads
Current trends in data management systems, such as cloud and multi-tenant databases are leading to data processing environments that concurrently execute heterogeneous query workloads. At the same time, these systems need to satisfy diverse performance expectations. In these newly-emerging settings, avoiding potential Quality-of-Service (QoS) violations heavily relies on performance predictability, i.e., the ability to estimate the impact of concurrent query execution on the performance of individual queries in a continuously evolving workload. This paper presents a modeling approach to estimate the impact of concurrency on query performance for analytical workloads. The authors' solution relies on the analysis of query behavior in isolation, pair-wise query interactions and sampling techniques to predict resource contention under various query mixes and concurrency levels.