21 citations · 71 across the 6 of their papers we have counts for
6 papers
Towards Building Autonomous Data Services on Azure
Yiwen Zhu, Yuanyuan Tian, Joyce Cahoon +35
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…
Deploying a Steered Query Optimizer in Production at Microsoft
Wangda Zhang, Matteo Interlandi, Paul Mineiro +6
Modern analytical workloads are highly heterogeneous and massively complex, making generic query optimizers untenable for many customers and scenarios. As a result, it is important…
Phoebe: A Learning-based Checkpoint Optimizer
Yiwen Zhu, Matteo Interlandi, Abhishek Roy +5
Easy-to-use programming interfaces paired with cloud-scale processing engines have enabled big data system users to author arbitrarily complex analytical jobs over massive volumes…
Optimal Resource Allocation for Serverless Queries
Anish Pimpley, Shuo Li, Anubha Srivastava +7
Optimizing resource allocation for analytical workloads is vital for reducing costs of cloud-data services. At the same time, it is incredibly hard for users to allocate resources…
Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings
Tarique Siddiqui, Alekh Jindal, Shi Qiao +2
Query processing over big data is ubiquitous in modern clouds, where the system takes care of picking both the physical query execution plans and the resources needed to run those…
Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML
Ashvin Agrawal, Rony Chatterjee, Carlo Curino +19
Machine learning (ML) has proven itself in high-value web applications such as search ranking and is emerging as a powerful tool in a much broader range of enterprise scenarios inc…