Auto-tuning Distributed Stream Processing Systems using Reinforcement Learning

Fine tuning distributed systems is considered to be a craftsmanship, relying on intuition and experience. This becomes even more challenging when the systems need to react in near real time, as streaming engines have to do to maintain pre-agreed service quality metrics. In this article, we present an automated approach that builds on a combination of supervised and reinforcement learning methods to recommend the most appropriate lever configurations based on previous load. With this, streaming engines can be automatically tuned without requiring a human to determine the right way and proper time to deploy them. This opens the door to new configurations that are not being applied today since the complexity of managing these systems has surpassed the abilities of human experts. We show how reinforcement learning systems can find substantially better configurations in less time than their human counterparts and adapt to changing workloads

Query-Driven Learning for Next Generation Predictive Modeling & Analytics

As data-size is increasing exponentially, new paradigm shifts have to emerge allowing fast exploitation of data by every- body. Large-scale predictive analytics is restricted to wealthy organizations as small-scale enterprises (SMEs) struggle to compete and are inundated by the sheer monetary cost of either procuring data infrastructures or analyzing datasets over the Cloud. The aim of this work is to study mechanisms which can democratize analytics, in the sense of making them affordable, while at the same time ensuring high efficiency, scalability, and accuracy. The crux of this proposal lies in developing query-driven solutions that can be used off the Cloud thus minimizing costs. Our query-driven approach will learn and adapt on-the-fly machine learning models, based solely on query-answer interactions, which can be used for answering analytical queries. In this abstract we describe the methodology followed for the implementation and evaluation of the system designed

Learning a Partitioning Advisor with Deep Reinforcement Learning

Commercial data analytics products such as Microsoft Azure SQL Data Warehouse or Amazon Redshift provide ready-to-use scale-out database solutions for OLAP-style workloads in the cloud. While the provisioning of a database cluster is usually fully automated by cloud providers, customers typically still have to make important design decisions which were traditionally made by the database administrator such as selecting the partitioning schemes. In this paper we introduce a learned partitioning advisor for analytical OLAP-style workloads based on Deep Reinforcement Learning (DRL). The main idea is that a DRL agent learns its decisions based on experience by monitoring the rewards for different workloads and partitioning schemes. We evaluate our learned partitioning advisor in an experimental evaluation with different databases schemata and workloads of varying complexity. In the evaluation, we show that our advisor is not only able to find partitionings that outperform existing approaches for automated partitioning design but that it also can easily adjust to different deployments. This is especially important in cloud setups where customers can easily migrate their cluster to a new set of (virtual) machines