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Tenant Placement in Over-subscribed Database-as-a-Service Clusters

König, Arnd Christian ; Shan, Yi ; Ziegler, Tobias ; Kakaraparthy, Aarati ; Lang, Willis ; Moeller, Justin ; Kalhan, Ajay ; Narasayya, Vivek (2022)
Tenant Placement in Over-subscribed Database-as-a-Service Clusters.
In: Proceedings of the VLDB Endowment, 15 (11)
doi: 10.14778/3551793.3551814
Article, Bibliographie

Abstract

Relational cloud database-as-a-service offerings run on multi-tenant infrastructure consisting of clusters of nodes, with each node hosting multiple tenant databases. Such clusters may be over-subscribed to increase resource utilization and improve operational efficiency. When resources are over-subscribed, it becomes possible that a node has insufficient resources to satisfy the resource demands of all databases on it, making it necessary to move databases to other nodes in the cluster. Such moves can significantly impact database performance and availability. Therefore, it is important to avoid such resource shortages through judicious placement of databases on the cluster nodes. We propose a novel tenant placement approach that leverages historical traces of tenant resource demands to assess the likelihood of resource shortages. We have prototyped our techniques in the industrial-strength Service Fabric cluster manager. Experiments using production resource usage traces from Azure SQL DB and an evaluation on a real cluster deployment show significant improvements over state-of-the-art tenant placement techniques.

Item Type: Article
Erschienen: 2022
Creators: König, Arnd Christian ; Shan, Yi ; Ziegler, Tobias ; Kakaraparthy, Aarati ; Lang, Willis ; Moeller, Justin ; Kalhan, Ajay ; Narasayya, Vivek
Type of entry: Bibliographie
Title: Tenant Placement in Over-subscribed Database-as-a-Service Clusters
Language: English
Date: July 2022
Publisher: ACM
Journal or Publication Title: Proceedings of the VLDB Endowment
Volume of the journal: 15
Issue Number: 11
DOI: 10.14778/3551793.3551814
Abstract:

Relational cloud database-as-a-service offerings run on multi-tenant infrastructure consisting of clusters of nodes, with each node hosting multiple tenant databases. Such clusters may be over-subscribed to increase resource utilization and improve operational efficiency. When resources are over-subscribed, it becomes possible that a node has insufficient resources to satisfy the resource demands of all databases on it, making it necessary to move databases to other nodes in the cluster. Such moves can significantly impact database performance and availability. Therefore, it is important to avoid such resource shortages through judicious placement of databases on the cluster nodes. We propose a novel tenant placement approach that leverages historical traces of tenant resource demands to assess the likelihood of resource shortages. We have prototyped our techniques in the industrial-strength Service Fabric cluster manager. Experiments using production resource usage traces from Azure SQL DB and an evaluation on a real cluster deployment show significant improvements over state-of-the-art tenant placement techniques.

Divisions: 20 Department of Computer Science
20 Department of Computer Science > Data and AI Systems
Date Deposited: 02 Feb 2023 08:17
Last Modified: 13 Jun 2023 15:34
PPN: 508529247
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