Evolutionary approaches to signal decomposition in an application service management system

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 queue-based 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

CloudReports: An Extensible Simulation Tool for Energy-Aware Cloud Computing Environments

The cloud computing paradigm integrates several technological models to provide services to a large number of clients distributed around the world. It involves the management of large data centers that represent very complex scenarios and demand sophisticated techniques for optimization of resource utilization and power consumption. Since the utilization of real testbeds to validate such optimization techniques requires large investments, simulation tools often represent the most viable way to conduct experimentation in this field. This chapter presents CloudReports, an extensible simulation tool for energy-aware cloud computing environments to enable researchers to model multiple complex simulation scenarios through an easy-to-use graphical user interface. It provides report generation features and a simple API (Application Programming Interface) that makes possible the development of extensions that are added to the system as plugins. CloudReports is an open-source project composed of five mandatory modules and an optional extensions module. This chapter describes all these modules, their integration with the CloudSim toolkit, and a case study that demonstrates an evaluation of power consumption of data centers with a power model that is created as a CloudReports extension