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首页> 外文期刊>Parallel and Distributed Systems, IEEE Transactions on >SmartSLA: Cost-Sensitive Management of Virtualized Resources for CPU-Bound Database Services
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SmartSLA: Cost-Sensitive Management of Virtualized Resources for CPU-Bound Database Services

机译:SmartSLA:针对CPU绑定数据库服务的虚拟资源的成本敏感管理

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摘要

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.
机译:基于虚拟化的多租户数据库整合是一项重要的技术,对于数据库即服务(DBaaS)提供商而言,将其总成本降至最低是由SLA罚款成本,基础架构成本和操作成本组成的。由于租户工作负载的突发性和多样性,高峰的超额配置或非高峰的超额配置通常会导致基础架构成本或服务水平协议(SLA)罚款成本。此外,尽管横向扩展数据库系统的过程将帮助DBaaS提供商满足租户的服务水平协议,但其不加选择的使用会影响性能或增加行动成本。在本文中,我们提出了SmartSLA,这是一个由成本敏感的虚拟化资源管理系统,用于CPU绑定的数据库服务,它由两个模块组成。系统建模模块使用机器学习技术来学习用于预测在不同资源分配下每个租户的SLA惩罚成本的模型。基于学习的模型,资源分配模块通过权衡SLA惩罚成本的潜在减少与基础架构成本和操作成本的增加来动态调整资源分配。通过使用TPC-W和经过修改的YCSB基准以及动态工作负载跟踪和多个数据库租户来评估SmartSLA。实验结果表明,与其他对成本不敏感的方法相比,SmartSLA在时变工作负载下能够将总成本降至最低。

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