Reducing screened program points for efficient error detection

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

Seer: a lightweight online failure prediction approach

Online failure prediction aims to predict the manifestation of failures at runtime before the failures actually occur. Existing online failure prediction approaches typically operate on data which is either directly reported by the system under test or directly observable from outside system executions. These approaches generally refrain themselves from collecting internal execution data that can further improve the prediction quality. One reason behind this general trend is due to the runtime overhead cost incurred by the measurement instruments that are required to collect the data. In this work we conjecture that large cost reductions in collecting internal execution data for online failure prediction can derive from reducing the cost of the measurement instruments, while still supporting acceptable levels of prediction quality. To evaluate this conjecture, we present a lightweight online failure prediction approach, called Seer. Seer uses fast hardware performance counters to perform most of the data collection work. The data is augmented with further data collected by a minimal amount of software instrumentation that is added to the systems software. We refer to the data collected in this manner as hybrid spectra. We applied the proposed approach to three widely used open source subject applications and evaluated it by comparing and contrasting three types of hybrid spectra and two types of traditional software spectra. At the lowest level of runtime overheads attained in the experiments, the hybrid spectra predicted the failures about half way through the executions with an F-measure of 0.77 and a runtime overhead of 1.98%, on average. Comparing hybrid spectra to software spectra, we observed that, for comparable runtime overhead levels, the hybrid spectra provided significantly better prediction accuracies and earlier warnings for failures than the software spectra. Alternatively, for comparable accuracy levels, the hybrid spectra incurred significantly less runtime overheads and provided earlier warnings

Optimization of daytime fuel consumption for a hybrid diesel and photovoltaic industrial micro-grid

2017 Spring.Includes bibliographical references.The work to be presented will examine the optimization of daytime diesel fuel consumption for a hybrid diesel and photovoltaic (PV) industrial micro-grid with no energy storage. The micro-grid utilizes a control system developed to forecast PV transients and manage the diesel generators providing electrical supply to the micro-grid. The work focuses on optimization of daytime fuel consumption when PV generation is available. Simulations were utilized to minimize diesel consumption while maintaining secure operations by controlling both PV curtailment and diesel generation. The control system utilizes a cloud forecast system based upon sky imaging, developed by CSIRO (Australia), to predict the presence of cloud cover in concentric "rings" around the sun's position in the sky. The control system utilizes these cloud detections to establish supervisory settings for PV and diesel generation. Work included methods to optimize control response for the number of rings around the sun, studied the use of two different sizes of generators to allow for increased PV utilization, and modification of generator controller settings to reduce fault occurrence. The work indicates that increasing the number of rings used to create the PV forecast has the greatest impact on reducing the number of faults, while having a minimal impact on the total diesel consumption. Additionally, increasing the total number of generators in the system increases PV utilization and decreases fuel consumption