research-article

SmartSLA: Cost-Sensitive Management of Virtualized Resources for CPU-Bound Database Services

Authors:

Pengcheng Xiong,

Hakan HacgumusAuthors Info & Claims

IEEE Transactions on Parallel and Distributed Systems, Volume 26, Issue 5

Pages 1441 - 1451

https://doi.org/10.1109/TPDS.2014.2319095

Published: 01 May 2015 Publication History

Abstract

Virtualization-based multi-tenant database consolidation is an important technique for database-as-a-service (DBaaS) providers to minimize their total cost which is composed of SLA penalty cost, infrastructure cost and action cost. Due to the bursty and diverse tenant workloads, over-provisioning for the peak or under-provisioning for the off-peak often results in either infrastructure cost or service level agreement (SLA) penalty cost. Moreover, although the process of scaling out database systems will help DBaaS providers satisfy tenants' service level agreement, its indiscriminate use has performance implications or incurs action cost. In this paper, we propose SmartSLA, a cost-sensitive virtualized resource management system for CPU-bound database services which is composed of two modules. The system modeling module uses machine learning techniques to learn a model for predicting the SLA penalty cost for each tenant under different resource allocations. Based on the learned model, the resource allocating module dynamically adjusts the resource allocation by weighing the potential reduction of SLA penalty cost against increase of infrastructure cost and action cost. SmartSLA is evaluated by using the TPC-W and modified YCSB benchmarks with dynamic workload trace and multiple database tenants. The experimental results show that SmartSLA is able to minimize the total cost under time-varying workloads compared to the other cost-insensitive approaches.

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Funika WKoperek PKitowski J(2023)Automated cloud resources provisioning with the use of the proximal policy optimizationThe Journal of Supercomputing10.1007/s11227-022-04924-379:6(6674-6704)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1007/s11227-022-04924-3
Feng XGuo CJiao TSong J(2022)A maturity model for AI-empowered cloud-native databases: from the perspective of resource managementJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-022-00318-111:1Online publication date: 7-Sep-2022
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cover image IEEE Transactions on Parallel and Distributed Systems

IEEE Transactions on Parallel and Distributed Systems Volume 26, Issue 5

May 2015

291 pages

ISSN:1045-9219

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Copyright © 2014.

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IEEE Press

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Published: 01 May 2015

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Cited By

Leis VDietrich C(2024)Cloud-Native Database Systems and Unikernels: Reimagining OS Abstractions for Modern HardwareProceedings of the VLDB Endowment10.14778/3659437.365946217:8(2115-2122)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.14778/3659437.3659462
Funika WKoperek PKitowski J(2023)Automated cloud resources provisioning with the use of the proximal policy optimizationThe Journal of Supercomputing10.1007/s11227-022-04924-379:6(6674-6704)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1007/s11227-022-04924-3
Feng XGuo CJiao TSong J(2022)A maturity model for AI-empowered cloud-native databases: from the perspective of resource managementJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-022-00318-111:1Online publication date: 7-Sep-2022
https://dl.acm.org/doi/10.1186/s13677-022-00318-1
Narasayya VChaudhuri SIves ZBonifati AEl Abbadi A(2022)Multi-Tenant Cloud Data Services: State-of-the-Art, Challenges and OpportunitiesProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3522566(2465-2473)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3522566
Funika WKoperek PKitowski J(2020)Management of Heterogeneous Cloud Resources with Use of the PPOEuro-Par 2020: Parallel Processing Workshops10.1007/978-3-030-71593-9_12(148-159)Online publication date: 24-Aug-2020
https://dl.acm.org/doi/10.1007/978-3-030-71593-9_12
Chen TBahsoon RYao X(2018)A Survey and Taxonomy of Self-Aware and Self-Adaptive Cloud Autoscaling SystemsACM Computing Surveys10.1145/319050751:3(1-40)Online publication date: 12-Jun-2018
https://dl.acm.org/doi/10.1145/3190507

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