Abstract
Performance profiling for the system is necessary and has already been widely supported by hardware performance counters (HPC). HPC is based on the registers to count the number of events in a time interval and uses system interruption to read the number from registers to a recording file. The profiled result approximates the actual running states and is not accurate since the profiling technique uses sampling to capture the states. We do not know the actual running states before, which makes the validation on profiling results complex. Jianwei YinSome experiments-based analysis compared the running results of benchmarks running on different systems to improve the confidence of the profiling technique. But they have not explained why the sampling technique can represent the actual running states. We use the probability theory to prove that the expectation value of events profiled is an unbiased estimation of the actual states, and its variance is small enough. For knowing the actual running states, we design a simulation to generate the running states and get the profiled results. We refer to the applications running on production data centers to choose the parameters for our simulation settings. Comparing the actual running states and the profiled results shows they are similar, which proves our probability analysis is correct and improves our confidence in profiling accuracy.
Highlights
In data centers, performance is critical to improve the quality of service [1] and save costs [2]
Hardware Performance Counters (HPC) [9] are register-based counters to count the number of events in a time interval
For profiling each task’s performance, only one extra information is in need—the instruction address. e instruction address indicates which task the processor is working for at the moment of interruption. e profiling technique treats the counted events in the last time interval as all caused by this task indicated by the instruction address
Summary
Performance is critical to improve the quality of service [1] and save costs [2]. E profiling technique treats the counted events in the last time interval as all caused by this task indicated by the instruction address. It is not accurate to use the instant instruction address to represent the running states of a long sustaining time interval. Is kind of Mathematical Problems in Engineering validations cannot deduce other workloads’ conditions since the mechanism lacks proof and analysis—they have not explained why the sampling technique can represent the actual running states. We model the profiling process with two main elements: the running granularity of a task and the sampling interval. E implementation of simulation includes the generation of actual running states and the sampling process. We simulate single tasks and mixed tasks running with multiple running granularities and under multiple resource utilization levels All of these experiments show that the expectation value is an unbiased estimation.
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