Recent PDL Publications

The PDL Packet - Fall 2024 Newsletter

 

The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions

William Zhang, Wan Shen Lim, Matthew Butrovich, Andrew Pavlo

Proceedings of the VLDB Endowment, 17(11): 3373-3387, 2024. July 2024.

Existing machine learning (ML) approaches to automatically optimize database management systems (DBMSs) only target a single configuration space at a time (e.g., knobs, query hints, indexes). Simultaneously tuning multiple configuration spaces is challenging due to the combined space’s complexity. Previous tuning methods work around this by sequentially tuning individual spaces with a pool of tuners. However, these approaches struggle to coordinate their tuners and get stuck in local optima. This paper presents the Proto-X framework that holistically tunes multiple configuration spaces. [...more]

 

Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems

Wan Shen Lim, Lin Ma, William Zhang, Matthew Butrovich, Samuel Arch, Andrew Pavlo

Proceedings of the VLDB Endowment, Vol. 17, No. 11, ISSN 2150-8097. July 2024.

Autonomous database management systems (DBMSs) aim to optimize themselves automatically without human guidance. They rely on machine learning (ML) models that predict their run-time behavior to evaluate whether a candidate configuration is beneficial without the expensive execution of queries. However, the high cost of collecting the training data to build these models makes them impractical for real-world deployments. Furthermore, these models are instance-specific and thus require retraining whenever the DBMS’s environment changes. State-of-the-art methods spend over 93% of their time running queries for training versus tuning. To mitigate this problem, we present the Boot framework for automatically accelerating training data collection in DBMSs. [...more]

 

A Call for Research on Storage Emissions

Sara McAllister, Fiodar Kazhamiaka, Daniel S. Berger, Rodrigo Fonseca, Kali Frost, Aaron Ogus, Maneesh Sah, Ricardo Bianchini, George Amvrosiadis, Nathan Beckmann, Gregory R. Ganger

HotCarbon’24, July 9, 2024, Santa Cruz, CA.

A major contributor to datacenter emissions has not received enough attention: storage. Storage — everything from file storage to inter-application messaging in datacenters — causes 33% of operational emissions and 61% of embodied emissions in Azure’s general-purpose cloud, based on a recent study. This paper identifies key sources of both operational and embodied emissions within distributed storage in datacenters. [...more]

 

FairyWREN: A Sustainable Cache for Emerging Write-Read-Erase Flash Interfaces

Sara McAllister, Yucong Sherry Wang, Benjamin Berg, Daniel S. Berger, George Amvrosiadis, Nathan Beckmann, Gregory R. Ganger

18th USENIX Symposium on Operating Systems Design and Implementation (OSDI '24), July 10–12, 2024. Santa Clara, CA, USA.

Datacenters need to reduce embodied carbon emissions, particularly for flash, which accounts for 40% of embodied carbon in servers. However, decreasing flash’s embodied emissions is challenging due to flash’s limited write endurance, which more than halves with each generation of denser flash. Reducing embodied emissions requires extending flash lifetime, stressing its limited write endurance even further. The legacy Logical Block-Addressable Device (LBAD) interface exacerbates the problem by forcing devices to perform garbage collection, leading to even more writes.[...more]