A framework to compute statistics of system parameters from very large trace files

Abstract

In this paper, we present a framework to compute, store and retrieve statistics of various system metrics from large traces in an efficient way. The proposed framework allows for rapid interactive queries about system metrics values for any given time interval. In the proposed framework, efficient data structures and algorithms are designed to achieve a reasonable query time while utilizing less disk space. A parameter termed granularity degree (GD) is defined to determine the threshold of how often it is required to store the precomputed statistics on disk. The solution supports the hierarchy of system resources and also different granularities of time ranges. We explain the architecture of the framework and show how it can be used to efficiently compute and extract the CPU usage and other system metrics. The importance of the framework and its different applications are shown and evaluated in this paper.