Storage systems are designed and optimized relying on wisdom derived from analysis studies of file-system and block-level workloads. However, while SSDs are becoming a dominant building block in many storage systems, their design continues to build on knowledge derived from analysis targeted at hard disk optimization. Though still valuable, it does not cover important aspects relevant for SSD performance. In a sense, we are “searching under the streetlight,” possibly missing important opportunities for optimizing storage system design. We present the first I/O workload analysis designed with SSDs in mind. We characterize traces from four repositories and examine their “temperature” ranges, sensitivity to page size, and “logical locality.” We then take the first step towards correlating these characteristics with three standard performance metrics: write amplification, read amplification, and flash read costs. Our results show that SSD-specific characteristics strongly affect performance, often in surprising ways.

As solid state drives (SSDs) are increasingly replacing hard disk drives, the reliability of storage systems depends on the failure modes of SSDs and the ability of the file system layered on top to handle these failure modes. While the classical paper on IRON File Systems provides a thorough study of the failure policies of three file systems common at the time, we argue that 13 years later it is time to revisit file system reliability with SSDs and their reliability characteristics in mind, based on modern file systems that incorporate journaling, copy-on-write, and log-structured approaches and are optimized for flash. This article presents a detailed study, spanning ext4, Btrfs, and F2FS, and covering a number of different SSD error modes. We develop our own fault injection framework and explore over 1,000 error cases. Our results indicate that 16% of these cases result in a file system that cannot be mounted or even repaired by its system checker. We also identify the key file system metadata structures that can cause such failures, and, finally, we recommend some design guidelines for file systems that are deployed on top of SSDs.

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Accessing the display of a computer remotely, is popularly called remote desktopping. Remote desktopping software installs at both the user-facing client computer and the remote server computer; it simulates user's input events at server, and streams the corresponding display changes to client, thus providing an illusion to the user of controlling the remote machine using local input devices (e.g., keyboard/mouse). Many such remote desktopping tools are widely used. We show that if the remote server is a virtual machine (VM) and the client is reasonably powerful (e.g., current laptop and desktop grade hardware), VM deterministic replay capabilities can be used adaptively to significantly reduce the network bandwidth consumption and server-side CPU utilization of a remote desktopping tool. We implement these optimizations in a tool based on Qemu/KVM virtualization platform and VNC remote desktopping platform. Our tool reduces VNC's network bandwidth consumption by up to 9x and server-side CPU utilization by up to 56% for popular graphics-intensive applications. On the flip side, our techniques consume higher CPU/memory/disk resources at the client. The effect of our optimizations on user-perceived latency is negligible.