Characterization of runtime resource usage from analysis of binary executable programs

Abstract

This paper introduces a methodology for characterizing the runtime resource demands of a computer program from the analysis of its binary executable file. Categorization of applications according to the kind of resources required during execution – such as CPU and memory usage – is a sough-after piece of knowledge for the aims of computer system design and management. Conventional techniques available for this purpose include white-box static source code analysis and profile matching based on historical execution data. The former tends to be challenging in face of complex software architectures and requires access to the source code; the latter is dependent on the availability of reliable past data and on the selection of features yielding effective correlations with resource usage. The alternative data mining approach proposed in this paper avoids those difficulties by manipulating binary executable files. The method combines techniques from information theory, complex networks and phylogenetics to produce a hierarchical clustering of a set of executable files, which can be used to infer potential similarities in terms of runtime resource usage. The paper introduces the method's rationales and presents results of its application to characterize CPU and IO usages of benchmark applications executed on a standard PC platform. Essays carried out over a set of 80 programs from varying sources yielded numerically significant evidences that the prediction of resource usage similarity obtained by the approach is consistent with experimentally measured runtime profile.