Evolutionary approaches to signal decomposition in an application service management system

Abstract

The increased demand for autonomous control in enterprise information systems has generated interest on efficient global search methods for multivariate datasets in order to search for original elements in time-series patterns, and build causal models of systems interactions, utilization dependencies, and performance characteristics. In this context, activity signals deconvolution is a necessary step to achieve effective adaptive control in Application Service Management. The paper investigates the potential of population-based metaheuristic algorithms, particularly variants of particle swarm, genetic algorithms and differential evolution methods, for activity signals deconvolution when the application performance model is unknown a-priori. In our approach, the Application Service Management System is treated as a black-or grey-box, and the activity signals deconvolution is formulated as a search problem, decomposing time-series that outline relations between action signals and utilization-execution time of resources. Experiments are conducted using a queuebased computing system model as a test-bed under different load conditions and search configurations. Special attention was put on high-dimensional scenarios, testing effectiveness for large-scale multivariate data analyses that can obtain a near-optimal signal decomposition solution in a short time. The experimental results reveal benefits, qualities and drawbacks of the various metaheuristic strategies selected for a given signal deconvolution problem, and confirm the potential of evolutionary-type search to effectively explore the search space even in high-dimensional cases. The approach and the algorithms investigated can be useful in support of human administrators, or in enhancing the effectiveness of feature extraction schemes that feed decision blocks of autonomous controllers.

Neural adaptive admission control framework: SLA-driven action termination for real-time application service management

“…This also relates to advances in Power-Aware Computing, focusing to maximise the computation efficiency as a function of energy consumed (Fan, Weber, and Barroso 2007;Kim, Beloglazov, and Buyya 2009;Berl et al 2010;Kim, Beloglazov, and Buyya 2011) relying on dynamic resources allocation (Beloglazov, Abawajy, and Buyya 2012;Lee and Zomaya 2012) or virtual machines placement (Beloglazov and Buyya 2010;Moreno et al 2013;Duolikun, Enokido, and Takizawa 2017), but may also be facilitated by application action level control (Sikora and Magoulas 2015). Moreover, this approach can be beneficial in the context of Energy Proportional Computing, where the main issue is to deal with the high static power that is caused by the fact that a computer consumes significant energy when it is idle but also to control the power to operate under load, which is not linear (Barroso and Hölzle 2007).…”