Towards Building Autonomous Data Services on Azure

TL;DR

This paper discusses the development of autonomous data services on Azure, leveraging data-driven and machine learning techniques to improve configuration, optimization, and management of cloud data services.

Contribution

It presents a perspective on creating autonomous data services on Azure, highlighting the use of ML and data science to automate service management and address existing challenges.

Findings

Utilizing workload traces and telemetry for automation

Applying ML techniques to optimize data service configurations

Identifying unresolved issues for future research

Abstract

Modern cloud has turned data services into easily accessible commodities. With just a few clicks, users are now able to access a catalog of data processing systems for a wide range of tasks. However, the cloud brings in both complexity and opportunity. While cloud users can quickly start an application by using various data services, it can be difficult to configure and optimize these services to gain the most value from them. For cloud providers, managing every aspect of an ever-increasing set of data services, while meeting customer SLAs and minimizing operational cost is becoming more challenging. Cloud technology enables the collection of significant amounts of workload traces and system telemetry. With the progress in data science (DS) and machine learning (ML), it is feasible and desirable to utilize a data-driven, ML-based approach to automate various aspects of data services, resulting in the creation of autonomous data services. This paper presents our perspectives and insights on creating autonomous data services on Azure. It also covers the future endeavors we plan to undertake and unresolved issues that still need attention.