ClassyTune: A Performance Auto-Tuner for Systems in the Cloud
This addresses the challenge of optimizing cloud system performance for users and providers, offering a novel solution that improves over existing methods, though it builds incrementally on data-driven approaches.
The paper tackles the problem of automating performance tuning for complex cloud systems by introducing ClassyTune, a data-driven tool that uses classification models to handle high-dimensional configuration spaces and sample scarcity, resulting in up to 7x higher performance and enabling a 33% reduction in computing resources.
Performance tuning can improve the system performance and thus enable the reduction of cloud computing resources needed to support an application. Due to the ever increasing number of parameters and complexity of systems, there is a necessity to automate performance tuning for the complicated systems in the cloud. The state-of-the-art tuning methods are adopting either the experience-driven tuning approach or the data-driven one. Data-driven tuning is attracting increasing attentions, as it has wider applicability. But existing data-driven methods cannot fully address the challenges of sample scarcity and high dimensionality simultaneously. We present ClassyTune, a data-driven automatic configuration tuning tool for cloud systems. ClassyTune exploits the machine learning model of classification for auto-tuning. This exploitation enables the induction of more training samples without increasing the input dimension. Experiments on seven popular systems in the cloud show that ClassyTune can effectively tune system performance to seven times higher for high-dimensional configuration space, outperforming expert tuning and the state-of-the-art auto-tuning solutions. We also describe a use case in which performance tuning enables the reduction of 33% computing resources needed to run an online stateless service.