With serverless computing, providers deploy application code and manage resource allocation dynamically, eliminating infrastructure management from application development.

We introduce split-level I/O scheduling, a new framework that splits I/O scheduling logic across handlers at three layers of the storage stack: block, system call, and page cache. We demonstrate that traditional block-level I/O schedulers are unable to meet throughput, latency, and isolation goals. By utilizing the split-level framework, we build a variety of novel schedulers to readily achieve these goals: our Actually Fair Queuing scheduler reduces priority-misallocation by 28x; our Split-Deadline scheduler reduces tail latencies by 4x; our Split-Token scheduler reduces sensitivity to interference by 6x. We show that the framework is general and operates correctly with disparate file systems (ext4 and XFS). Finally, we demonstrate that split-level scheduling serves as a useful foundation for databases (SQLite and PostgreSQL), hypervisors (QEMU), and distributed file systems (HDFS), delivering improved isolation and performance in these important application scenarios.

We describe ROOT, a new method for incorporating the nondeterministic I/O behavior of multithreaded applications into trace replay. ROOT is the application of Resource-Oriented Ordering to Trace replay: actions involving a common resource are replayed in an order similar to that of the original trace. ROOT is based on the idea that how a program manages resources, as seen in a trace, provides hints about an application's internal dependencies. Inferring these dependencies allows us to partially constrain trace replay in a way that reflects the constraints of the original program. We make three contributions: (1) we describe the ROOT approach, (2) we release ARTC, a new ROOT-based tool for replaying I/O traces, and (3) we create Magritte, a file-system benchmark suite generated by applying ARTC to 34 Apple desktop application traces. When collecting traces on one platform and replaying on another, ARTC achieves an average timing inaccuracy of 10.6% on our benchmark workloads, halving the 21.3% achieved by the next-best replay method we evaluate.

We analyze the I/O behavior of iBench , a new collection of productivity and multimedia application workloads. Our analysis reveals a number of differences between iBench and typical file-system workload studies, including the complex organization of modern files, the lack of pure sequential access, the influence of underlying frameworks on I/O patterns, the widespread use of file synchronization and atomic operations, and the prevalence of threads. Our results have strong ramifications for the design of next generation local and cloud-based storage systems.

We analyze the I/O behavior of iBench, a new collection of productivity and multimedia application workloads. Our analysis reveals a number of differences between iBench and typical file-system workload studies, including the complex organization of modern files, the lack of pure sequential access, the influence of underlying frameworks on I/O patterns, the widespread use of file synchronization and atomic operations, and the prevalence of threads. Our results have strong ramifications for the design of next generation local and cloud-based storage systems.