Search for: All records

We present uFS, a user-level filesystem semi-microkernel. uFS takes advantage of a high-performance storage development kit to realize a fully-functional, crash-consistent, highly-scalable filesystem,with relative developer ease. uFS delivers scalable high performance with a number of novel techniques: careful partitioning of in-memory and on-disk data structures to enable concurrent access without locking, inode migration for balancing load across filesystem threads, and a dynamic scaling algorithm for determining the number of filesystem threads to serve the current workload. Through measurements, we show that uFS has good base performance and excellent scalability; for example, uFS delivers nearly twice the throughput of ext4 for LevelDB on YCSB workloads. 

We describe WiSER, a clean-slate search engine designed to exploit high-performance SSDs with the philosophy "read as needed". WiSER utilizes many techniques to deliver high throughput and low latency with a relatively small amount of main memory; the techniques include an optimized data layout, a novel two-way cost-aware Bloom filter, adaptive prefetching, and space-time trade-offs. In a system with memory that is significantly smaller than the working set, these techniques increase storage space usage (up to 50%), but reduce read amplification by up to 3x, increase query throughput by up to 2.7x, and reduce latency by 16x when compared to the state-of-the-art Elasticsearch. We believe that the philosophy of "read as needed" can be applied to more applications as the read performance of storage devices keeps improving 

Container systems (e.g., Docker) provide a well-defined, lightweight, and versatile foundation to streamline the process of tool deployment, to provide a consistent and repeatable experimental interface, and to leverage data centers in the global cloud infrastructure as measurement vantage points. However, the virtual network devices commonly used to connect containers to the Internet are known to impose latency overheads which distort the values reported by measurement tools running inside containers. In this study, we develop a tool called MACE to measure and remove the latency overhead of virtual network devices as used by Docker containers. A key insight of MACE is the fact that container functions all execute in the same kernel. Based on this insight, MACE is implemented as a Linux kernel module using the trace event subsystem to measure latency along the network stack code path. Using CloudLab, we evaluate MACE by comparing the ping measurements emitted from a slim-ping container to the ones emitted using the same tool running in the bare metal machine under varying traffic loads. Our evaluation shows that the MACE-adjusted RTT measurements are within 20 µs of the bare metal ping RTTs on average while incurring less than 25 µs RTT perturbation. We also compare RTT perturbation incurred by MACE with perturbation incurred by the built-in ftrace kernel tracing system and find that MACE incurs less perturbation.