Cloud Computing I/O Thread Performance Optimization with VirtIO and Queue Size Tuning

Cloud storage performance is critical for ensuring efficient data access especially in scenarios of financial transaction and application responsiveness in cloud environments. This paper presents a study on improving network connected cloud storage volume performance through virtIO-blk configuration optimizations. Specifically, we explore the impact of adjusting parameters such as queue size and queues in hypervisor nodes on the performance of cloud storage volumes. We conduct experiments using a testbed simulating typical network connected cloud storage scenarios and measure performance metrics such as throughput, queue latency, and IOPS (Input/Output Operations Per Second). Our results demonstrate that optimizing these configurations can significantly enhance the performance of network attached cloud storage volumes based on the nature of the workload, leading to better resource utilization and improved user experience. The findings of this study provide valuable insights for cloud administrators and system architects seeking to maximize the performance of their network connected cloud storage infrastructure.