A large number of user requests execute (often concurrently) within a server system. A single request may exhibit fluctuating hardware characteristics (such as instruction completion rate and on-chip resource usage) over the course of its execution, due to inherent variations in application execution semantics as well as dynamic resource competition on resource-sharing processors like multicores. Understanding such behavior variations can assist fine-grained request modeling and adaptive resource management. This paper presents operating system management to track request behavior variations online. In addition to metric sample collection during periodic interrupts, we exploit the frequent system calls in server applications to perform low-cost in-kernel sampling. We utilize identified behavior variations to support or enhance request modeling in request classification, anomaly analysis, and online request signature construction. A foundation of our request modeling is the ability to quantify the difference between two requests' time series behaviors. We evaluate several differencing measures and enhance the classic dynamic time warping technique with additional penalties for asynchronous warp steps. Finally, motivated by fluctuating request resource usage and the resulting contention, we implement contention-easing CPU scheduling on multicore platforms and demonstrate its effectiveness in improving the worst-case request performance. Experiments in this paper are based on five server applicationsApache web server, TPCC, TPCH, RUBiS online auction benchmark, and a user-content-driven online teaching application called WeBWorK.
REF provides a characterization of request behavior variations using server applications and finds that the inter-core resource sharing on multicore platforms obfuscates the request execution performance.
Request behavior variations
ASPLOS XV
Computer Science