Tatsuo Kumano

Distributed object storage toward storage and usage of packet data in a high-speed network

As the amount of data flowing in the network increases and as the number of applications having complex traffic patterns increases as well, packet data analysis is becoming all the more important for improving the quality of communications and facilitating troubleshooting. In particular, demand for the storage and analysis of individual items of packet data is increasing in addition to analysis based on statistical information to enable a detailed diagnosis of phenomena that have occurred on the network and at terminals. This paper proposes distributed object storage that can achieve both high-throughput storage of packet data and high-speed session searching. Specifically, we propose and evaluate a system for storing small-size data such as packet data with high throughput while deleting old data and conducting searches, and compare the storage performance of the proposed system with that of other distributed storage systems, and present the results of evaluating search performance for typical search patterns.

Understanding storage traffic characteristics on enterprise virtual desktop infrastructure

Despite the growing popularity of enterprise virtual desktop infrastructure (VDI), little is known about its storage traffic characteristics. In addition, no prior work has considered the detailed characteristics of virtual machine (VM) behavior on VDI. In this paper, we analyze the enterprise storage traffic on commercial office VDI using designated VMs. For 28 consecutive days, we gathered various types of traces, including a usage questionnaire and active and passive measurements. To characterize the storage traffic, we focused on two perspectives: fibre channel (FC) traffic and VM behavior. From the FC traffic perspective, we found that read traffic is dominant, although the applications are similar to those in a previous small-scale VDI. In particular, the write response time of large transactions, e.g.,128 KiB, is strongly affected by a slight decrease in cache hits during an update storm. From the VM behavior, we found that all active user VMs generate only 25% of traffic. Although a few VMs generate massive traffic, their impact is small. These characteristics are unique in comparison with the small-scale VDI. Our results have significant implications for designing the next generation of VDI and improving its performance.

Analysis of Block Access Address Correlation in Enterprise VDI Storage

Virtual desktop infrastructure (VDI) is widely used in enterprises and universities. Since the storage system is shared by many VDIs, it becomes a bottleneck. In order to solve this problem, we need to analyze and understand the I/O characteristics of real VDI storage workloads. However, there are no prior works analyzing I/O traces of large scale enterprise VDIs. In this work, we captured packets between servers and storage of an enterprise VDI service, and analyzed its storage trace. Based on accessed logical block address (LBA) and access time from daily traces, contiguous LBA were aggregated fixed-size blocks, and those blocks were then clustered according to their access frequency over time. The results showed that the same 50 GiB-sized clusters were found each day. And detailed analysis showed that only 26% of the LBAs were actually accessed for each of those clusters. Taking into account those block correlation results to improve storage tiering or caching seems a much promising direction to remove storage bottlenecks of current VDIs.