Virtualization is a prominent technology used in data centers around the world. While many kinds of workloads can run at near-native performance even when virtualized, I/O intensive workloads still suffer from high overhead precluding the use of virtualization in many applications. In this paper we tackle the problem of improving the performance of paravirtual I/O. We propose an exitless paravirtual I/O model, under which guests and the hypervisor, running on distinct cores, exchange exitless notifications instead of costly exit-based notifications. Our initial proof of concept improved throughput by 45% and latency by 25μsec compared to a traditional network paravirtual I/O model. We show that a single hypervisor I/O core can become saturated when serving multiple I/O intensive guests, and further research is required to improve scalability in this scenario.

OSprof is a versatile, portable, and efficient profiling methodology based on the analysis of latency distributions. Although OSprof has offers several unique benefits and has been used to uncover several interesting performance problems, the latency distributions that it provides must be analyzed manually. These latency distributions are presented as histograms and contain distinct groups of data, called peaks, that characterize the overall behavior of the running code. By automating the analysis process, we make it easier to take advantage of OSprof's unique features.

Benchmarking is critical when evaluating performance, but is especially difficult for file and storage systems. Complex interactions between I/O devices, caches, kernel daemons, and other OS components result in behavior that is rather difficult to analyze. Moreover, systems have different features and optimizations, so no single benchmark is always suitable. The large variety of workloads that these systems experience in the real world also adds to this difficulty. In this article we survey 415 file system and storage benchmarks from 106 recent papers. We found that most popular benchmarks are flawed and many research papers do not provide a clear indication of true performance. We provide guidelines that we hope will improve future performance evaluations. To show how some widely used benchmarks can conceal or overemphasize overheads, we conducted a set of experiments. As a specific example, slowing down read operations on ext2 by a factor of 32 resulted in only a 2--5% wall-clock slowdown in a popular compile benchmark. Finally, we discuss future work to improve file system and storage benchmarking.

Operating systems are complex and their behavior depends on many factors. Source code, if available, does not directly help understand the OS's behavior, as the behavior depends on actual workloads and external inputs. Runtime profiling is a key technique for understanding the behavior and mutual-influence of modern OS components. Such profiling is useful to prove new concepts, debug problems, and optimize the performance of existing OSs. Unfortunately, existing profiling methods lack in important areas: they do not provide much of the necessary information about the OS's behavior; they require OS modification and therefore are not portable; or they exact high overheads thus perturbing the profiled OS.