research-article

Tao: Improving Resource Utilization while Guaranteeing SLO in Multi-tenant Relational Database-as-a-Service

Authors:

Bo TangAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 2, Issue 4

Article No.: 205, Pages 1 - 26

https://doi.org/10.1145/3677141

Published: 30 September 2024 Publication History

Abstract

It is an open challenge for cloud database service providers to guarantee tenants' service-level objectives (SLOs) and enjoy high resource utilization simultaneously. In this work, we propose a novel system Tao to overcome it. Tao consists of three key components: (i) tasklet-based DAG generator, (ii) tasklet-based DAG executor, and (iii) SLO-guaranteed scheduler. The core concept in Tao is tasklet, a coroutine-based lightweight execution unit of the physical execution plan. In particular, we first convert each SQL operator in the traditional physical execution plan into a set of fine-grained tasklets by the tasklet-based DAG generator. Then, we abstract the tasklet-based DAG execution procedure and implement the tasklet-based DAG executor using C++20 coroutines. Finally, we introduce the SLO-guaranteed scheduler for scheduling tenants' tasklets across CPU cores. This scheduler guarantees tenants' SLOs with a token bucket model and improves resource utilization with an on-demand core adjustment strategy. We build Tao on an open-sourced relational database, Hyrise, and conduct extensive experimental studies to demonstrate its superiority over existing solutions.

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Cited By

Wu XGong QChen JLiu QPodhorszki NLiang XKlasky S(2024)Error-controlled Progressive Retrieval of Scientific Data under Derivable Quantities of InterestProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.1109/SC41406.2024.00092(1-16)Online publication date: 17-Nov-2024
https://dl.acm.org/doi/10.1109/SC41406.2024.00092

Index Terms

Tao: Improving Resource Utilization while Guaranteeing SLO in Multi-tenant Relational Database-as-a-Service
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
      1. Cloud computing
2. Information systems
  1. Data management systems
    1. Database management system engines
      1. DBMS engine architectures

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cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 2, Issue 4

SIGMOD

September 2024

458 pages

EISSN:2836-6573

DOI:10.1145/3698442

Editor:
Divyakant Agrawal
UC Santa Barbara, United States

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Publication History

Published: 30 September 2024

Published in PACMMOD Volume 2, Issue 4

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Cited By

Wu XGong QChen JLiu QPodhorszki NLiang XKlasky S(2024)Error-controlled Progressive Retrieval of Scientific Data under Derivable Quantities of InterestProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.1109/SC41406.2024.00092(1-16)Online publication date: 17-Nov-2024
https://dl.acm.org/doi/10.1109/SC41406.2024.00092

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