Coz: Finding Code that Counts with Causal Profiling

Improving performance is a central concern for software developers. To locate optimization opportunities, developers rely on software profilers. However, these profilers only report where programs spent their time: optimizing that code may have no impact on performance. Past profilers thus both waste developer time and make it difficult for them to uncover significant optimization opportunities. This paper introduces causal profiling. Unlike past profiling approaches, causal profiling indicates exactly where programmers should focus their optimization efforts, and quantifies their potential impact. Causal profiling works by running performance experiments during program execution. Each experiment calculates the impact of any potential optimization by virtually speeding up code: inserting pauses that slow down all other code running concurrently. The key insight is that this slowdown has the same relative effect as running that line faster, thus "virtually" speeding it up. We present Coz, a causal profiler, which we evaluate on a range of highly-tuned applications: Memcached, SQLite, and the PARSEC benchmark suite. Coz identifies previously unknown optimization opportunities that are both significant and targeted. Guided by Coz, we improve the performance of Memcached by 9%, SQLite by 25%, and accelerate six PARSEC applications by as much as 68%; in most cases, these optimizations involve modifying under 10 lines of code.Comment: Published at SOSP 2015 (Best Paper Award

Deconstructing Commodity Storage Clusters

The traditional approach for characterizing complex systems is to run standard workloads and measure the resulting performance as seen by the end user. However, unique opportunities exist when characterizing a system that is itself constructed from standardized components: one can also look inside the system itself by instrumenting each of the components. In this paper, we show how intra-box instrumentation can help one understand the behavior of a large-scale storage cluster, the EMC Centera. In our analysis, we leverage standard tools for tracing both the disk and network traffic emanating from each node of the cluster. By correlating this traffic with the running workload, we are able to infer the structure of the software system (e.g., its write update protocol) as well as its policies (e.g., how it performs caching, replication, and load-balancing). Further, by imposing variable intra-box delays on network and disk traffic, we can confirm the causal relationships between network and disk events. Thus, we are able to infer the semantics of the messages between nodes without examining a single line of source code.