ITR: Data Management Using Smart Storage Systems

存储系统已经从文件服务器控制下的小型磁盘系统发展到可以由应用程序直接访问的大型独立磁盘和磁盘阵列系统。系统管理员手动管理这些复杂的存储系统变得越来越困难。这项工作通过提出开发智能存储系统来解决这个问题，这些智能存储系统除了执行数据存储和检索以及数据备份等常规任务外，还执行配置，容量规划，故障管理和负载平衡等复杂任务。自动化存储系统无需人工干预即可适应工作负载和系统配置的变化。存储管理软件使用设备和工作负载模型来计算最佳的数据到设备映射。这些模型嵌入在使用约束满足编程技术开发的存储管理软件中。拟议的工作的主要贡献是工作负载感知和系统感知存储技术，显着提高系统性能和简化存储数据管理的发展。

Storage systems have evolved from small disk systems under control of a file server to large, independent disk and disk-array systems that can be directly accessed by applications. It is becoming increasingly difficult for system administrators to manage these complex storage systems manually. This work addresses this problem by proposing to develop smart storage systems that perform complex tasks like configuration, capacity planning, fault management, and load balancing in addition to routine tasks like data storage and retrieval and data backups. The automated storage system adapts to changes in the workload and system configuration without human intervention. The storage management software uses device and workload models to compute the optimum data-to-device mapping. These models are embedded inside the storage management software that is developed using constraint satisfaction programming techniques. The main contribution of the proposed work is the development of workload-aware and system-aware storage techniques that significantly improve system performance and ease storage data management.