Search for: All records

Modern applications run various auxiliary tasks. These tasks gain high observability and control by executing in the application address space, but doing so causes safety and performance issues. Running them in a separate process offers strong isolation but poor observability and control. In this paper, we propose special OS support for auxiliary tasks to address this challenge with an abstraction called orbit. An orbit task offers strong isolation. At the same time, it conveniently observes the main program with an automatic state synchronization feature. We implement the abstraction in the Linux kernel. We use orbit to port 7 existing auxiliary tasks and add one new task in 6 large applications. The evaluation shows that the orbit-version tasks have strong isolation with comparable performance of the original unsafe tasks. 

Modern desktop applications involve many asynchronous, concurrent interactions that make performance issues difficult to diagnose. Although prior work has used causal tracing for debugging performance issues in distributed systems, we find that these techniques suffer from high inaccuracies for desktop applications. We present Argus, a fast, effective causal tracing tool for debugging performance anomalies in desktop applications. Argus introduces a novel notion of strong and weak edges to explicitly model and annotate trace graph ambiguities, a new beam-search-based diagnosis algorithm to select the most likely causal paths in the presence of ambiguities, and a new way to compare causal paths across normal and abnormal executions. We have implemented Argus across multiple versions of macOS and evaluated it on 12 infamous spinning pinwheel issues in popular macOS applications. Argus diagnosed the root causes for all issues, 10 of which were previously unknown, some of which have been open for several years. Argus incurs less than 5% CPU overhead when its system-wide tracing is enabled, making always-on tracing feasible. 

Misconfiguration is a major cause of system failures. Prior solutions focus on detecting invalid settings that are introduced by user mistakes. But another type of misconfiguration that continues to haunt production services is specious configuration—settings that are valid but lead to unexpectedly poor performance in production. Such misconfigurations are subtle, so even careful administrators may fail to foresee them. We propose a tool called Violet to detect specious configuration. We realize the crux of specious configuration is that it causes some slow code path to be executed, but the bad performance effect cannot always be triggered. Violet thus takes a novel approach that uses selective symbolic execution to systematically reason about the performance effect of configuration parameters, their combination effect, and the relationship with input. Violet outputs a performance impact model for the automatic detection of poor configuration settings. We applied Violet on four large systems. To evaluate the effectiveness of Violet, we collect 17 real-world specious configuration cases. Violet detects 15 of them. Violet also identifies 11 unknown specious configurations