Fuzzy Modeling Based Resource Management for Virtualized Database Systems
Fuzzy Modeling Based Resource Management for Virtualized Database Systems
复制标题
基于模糊建模的虚拟化数据库系统资源管理
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
J. Fortes
中科院分区：
文献类型：
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作者：
Lixi Wang;Jing Xu;Ming Zhao;Yi;J. Fortes
虚拟机（VM）上数据库的托管具有提高资源利用效率和数据库系统部署易于的巨大潜力。本文认为，在满足QoS（服务质量）要求的同时，将资源分配给运行动态和复杂的查询工作负载的VM分配的问题。提出了一种自主资源管理方法来解决此问题。它使用自适应模糊建模来捕获具有动态变化工作负载的数据库的VM的行为，并预测其多型资源需求。该方法的原型是在基于XEN的VM上实现的，并使用基于TPC-H和Rubis的工作负载进行了评估。结果表明，CPU和磁盘I/O带宽可以有效地分配给数据库VMS，以在满足QoS目标的同时，具有动态变化的强度和组成的工作负载。对于基于TPC-H的实验，所得的吞吐量在89.5 - 使用基于峰值载荷的资源分配获得的响应时间目标（基于基于峰值负载下的峰值的性能设置的资源分配）的100％。分配）在97％的时间内满足。此外，与基于峰值负载的分配相比，保存大量资源（约占CPU的62.6％，占磁盘I/O带宽的76.5％）。
The hosting of databases on virtual machines (VMs) has great potential to improve the efficiency of resource utilization and the ease of deployment of database systems. This paper considers the problem of on-demand allocation of resources to a VM running a database serving dynamic and complex query workloads while meeting QoS (Quality of Service) requirements. An autonomic resource-management approach is proposed to address this problem. It uses adaptive fuzzy modeling to capture the behavior of a VM hosting a database with dynamically changing workloads and to predict its multi-type resource needs. A prototype of the proposed approach is implemented on Xen-based VMs and evaluated using workloads based on TPC-H and RUBiS. The results demonstrate that CPU and disk I/O bandwidth can be efficiently allocated to database VMs serving workloads with dynamically changing intensity and composition while meeting QoS targets. For TPC-H-based experiments, the resulting throughput is within 89.5 -- 100% of what would be obtained using resource allocation based on peak loads, For RUBiS, the response time target (set based on the performance under peak-load-based allocation) is met for 97% of the time. Moreover, substantial resources are saved (about 62.6% of CPU and 76.5% of disk I/O bandwidth) in comparison to peak-load-based allocation.