STORM: An Approach to Database Storage Management in Clustered Storage Environments

K. Dutta, R. Rangaswami
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Abstract

Database storage management in clustered storage environments is a manual, time-consuming, and error-prone task. Such management involves regular movement of database objects across nodes in the storage cluster so that storage utilization is maximized. We present STORM, an automated approach that guides this task by combining low-overhead information gathering about database access and storage usage patterns, efficient analysis of gathered information, and effective decision-making for reconfiguring data layout. The reconfiguration process is guided by the primary optimization objective of minimizing the total data movement required for the reconfiguration, with the secondary constraints of space and balanced I/O bandwidth utilizations across the storage nodes in the cluster. We model the reconfiguration decision-making as a multi-constraint optimization problem which is NP-hard. We then present a heuristic that provides an approximate solution in O(Nlog(N/M) + (N/M)2) time, where M is the number of storage nodes and N is the total number of database objects. A simulation study shows that the heuristic converges to an acceptable solution that is successful in balancing storage utilization with an accuracy that lies within 7% of the ideal solution.
STORM:集群存储环境下的数据库存储管理方法
集群存储环境中的数据库存储管理是一项手动、耗时且容易出错的任务。这种管理涉及在存储集群中的节点之间定期移动数据库对象,以便最大限度地利用存储。我们提出了STORM,一种自动化的方法,通过结合低开销的数据库访问和存储使用模式的信息收集、对收集到的信息的有效分析和对重新配置数据布局的有效决策来指导这项任务。重新配置过程的主要优化目标是最小化重新配置所需的总数据移动,其次是集群中存储节点之间的空间约束和平衡I/O带宽利用率。我们将重构决策建模为一个np困难的多约束优化问题。然后,我们提出了一个启发式算法,在O(Nlog(N/M) + (N/M)2)时间内提供了一个近似解,其中M是存储节点的数量,N是数据库对象的总数。仿真研究表明,启发式收敛到一个可接受的解决方案,该解决方案成功地平衡了存储利用率,精度在理想解决方案的7%以内。
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