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
Artikel, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | König, Arnd Christian ; Shan, Yi ; Ziegler, Tobias ; Kakaraparthy, Aarati ; Lang, Willis ; Moeller, Justin ; Kalhan, Ajay ; Narasayya, Vivek |
| Art des Eintrags: | Bibliographie |
| Titel: | Tenant Placement in Over-subscribed Database-as-a-Service Clusters |
| Sprache: | Englisch |
| Publikationsjahr: | Juli 2022 |
| Verlag: | ACM |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Proceedings of the VLDB Endowment |
| Jahrgang/Volume einer Zeitschrift: | 15 |
| (Heft-)Nummer: | 11 |
| DOI: | 10.14778/3551793.3551814 |
| Kurzbeschreibung (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. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Data and AI Systems |
| Hinterlegungsdatum: | 02 Feb 2023 08:17 |
| Letzte Änderung: | 13 Jun 2023 15:34 |
| PPN: | 508529247 |
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| Suche nach Titel in: | TUfind oder in Google |
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