Zhen Tang, Heng Wu, Lei Sun, Zhongshan Ren, Wei Wang, Wei Zhou, Liang Yang
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引用次数: 2
摘要
基于flash的SSD (Solid State Disk)硬盘被广泛应用于基于互联网的虚拟计算环境中,通常作为基于硬盘驱动器的虚拟机存储的缓存。现有的SSD caching方案主要是将虚拟机作为独立的单元,关注单个虚拟机的关键性能指标,如IO时延、吞吐量、cache miss率等。然而,在基于internet的虚拟计算环境中,一个事务性应用程序通常由不同管理程序上的多个vm组成。事务感知的SSD缓存方案可能会更好地提高最终用户感知的服务质量。这里的关键观点是利用事务性应用程序内部vm之间的关系来更好地指导SSD缓存的分配,从而帮助了解工作负载变化的模式并构建自适应的SSD缓存方案。为此,我们提出了基于事务感知的SSD缓存(TA-SSD),它考虑了事务的特性,使用闭环自适应来应对工作负载的变化,并引入遗传算法来实现近乎最优的规划。评估表明,与等分区缓存相比,TA-SSD产生的分配可以在工作负载的强度和类型动态变化的情况下提高高达40%的性能。
Transaction-aware SSD Cache Allocation for the Virtualization Environment
Flash-based Solid State Disk (SSD) is widely used in the Internet-based virtual computing environment, usually as cache of the hard disk drive-based virtual machine (VM) storage. Existing SSD caching schemes mainly treat the VMs as independent units and focus on critical performance metrics concerning one single VM, such as the IO latency, throughput, or the cache miss rate. However, in the Internet-based virtual computing environment, one transactional application usually consists of multiple VMs on different hypervisors. Transaction-aware SSD caching schemes may potentially better improve the end user-perceived quality of service. The key insight here is to utilize the relationships among VMs inside the transactional application to better guide the allocation of the SSD cache, so as to help learn the pattern of workload changes and build adaptive SSD caching schemes. To this end, we propose the Transaction-Aware SSD caching (TA-SSD), which takes the characteristics of transactions into consideration, uses closed loop adaptation to react to changing workload, and introduces the genetic algorithm to enable nearly optimal planning. The evaluation shows that comparing to the equally partitioned cache, the allocation produced by the TA-SSD can boost the performance by up to 40%, with dynamic changes in the intensity and the type of the workload.