Search-guided activity signals extraction in application service management control

Abstract

The increased interest in autonomous control in Application Service Management-ASM environments has driven the demand for analysis of multivariate datasets in this area. Gathered metrics form time-series that can be considered as signals, which should be decomposed in order to find relations between system utilization and effective activity. This paper introduces a metrics signal deconvolution method that can be used to support human administrators or can be incorporated into feature extraction schemes that feed decision blocks of autonomous controllers. The method considers ASM environments signals decomposition as a search problem that is solved using heuristics and metaheuristic strategies. Quantitative and qualitative relations between activity and system resources signals are searched with use of a model that is based on similarity and variability of the changes, under minimal assumptions about the ASM system architecture and design. Experimental results show that the model can be successfully integrated with optimization techniques and the results produced when tested using data produced through queue modeling meet human perception of the signal unmixing problem.