Technical advances in network communication systems (e.g. radio access networks) combined with evolving concepts based on virtualization (e.g. clouds), require new management algorithms in order to handle the increasing complexity in the network behavior and variability in the network environment. Current network management operations are primarily centralized and deterministic, and are carried out via automated scripts and manual interventions, which work for mid-sized and fairly static networks. The next generation of communication networks and systems will be of significantly larger size and complexity, and will require scalable and autonomous management algorithms in order to meet operational requirements on reliability, failure resilience, and resource-efficiency. A promising approach to address these challenges includes the development of probabilistic management algorithms, following three main design goals. The first goal relates to all aspects of scalability, ranging from efficient usage of network resources to computational efficiency. The second goal relates to adaptability in maintaining the models up-to-date for the purpose of accurately reflecting the network state. The third goal relates to reliability in the algorithm performance in the sense of improved performance predictability and simplified algorithm control. This thesis is about probabilistic approaches to fault management that follow the concepts of probabilistic network management (PNM). An overview of existing network management algorithms and methods in relation to PNM is provided. The concepts of PNM and the implications of employing PNM-algorithms are presented and discussed. Moreover, some of the practical differences of using a probabilistic fault detection algorithm compared to a deterministic method are investigated. Further, six probabilistic fault management algorithms that implement different aspects of PNM are presented. The algorithms are highly decentralized, adaptive and autonomous, and cover several problem areas, such as probabilistic fault detection and controllable detection performance; distributed and decentralized change detection in modeled link metrics; root-cause analysis in virtual overlays; event-correlation and pattern mining in data logs; and, probabilistic failure diagnosis. The probabilistic models (for a large part based on Bayesian parameter estimation) are memory-efficient and can be used and re-used for multiple purposes, such as performance monitoring, detection, and self-adjustment of the algorithm behavior. / <p>QC 20140509</p>

Change detection using satellite imagery has applications in urban development, disaster response and precision agriculture. Current deep learning models show promising results. However, on-board computers are typically highly constrained which poses a challenge for deployment. On-board processing is desirable for saving bandwidth by downlinking only novel and valuable data. The goal of this work is to determine what change detection models are most technically feasible for on-board use in satellites. The novel patch based model MobileGoNogo is evaluated along current state-of-the-art models. Technical feasibility was determined by observing accuracy, inference time, storage buildup, memory usage and resolution on a satellite computer tasked with detecting changes in buildings from the SpaceNet 7 dataset. Three high level approaches were taken; direct classification, post classification and patch-based change detection. None of the models compared in the study fulfilled all requirements for general technical feasibility. Direct classification models were highly resource intensive and slow. Post classification model had critically low accuracy but desirable storage characteristics. Patch based MobileGoNogo performed better by all metrics except in resolution where it is significantly lower than any other model. We conclude that the novel model offers a feasible solution for low resolution, noncritical applications. / Upptäckt av förändringar med hjälp av satellitbilder har tillämpningar inom bl.a. stadsutveckling, katastrofinsatser och precisionsjordbruk. De nuvarande modellerna för djupinlärning visar lovande resultat. Datorerna ombord satelliter är dock vanligtvis mycket begränsade, vilket innebär en utmaning för användningen av dessa modeller. Databehandling ombord är önskvärd för att spara bandbredd genom att endast skicka ner nya och värdefulla data. Målet med detta arbete är att fastställa vilka modeller för upptäckt av förändringar som är mest tekniskt genomförbara för användning ombord på satelliter. Den nya bildfältbaserade modellen MobileGoNogo utvärderas tillsammans med de senaste modellerna. Den tekniska genomförbarheten fastställdes genom att observera träffsäkerhet, inferenstid, lagring, minnesanvändning och upplösning på en satellitdator med uppgift att upptäcka förändringar i byggnader från SpaceNet 7dataset. Tre tillvägagångssätt på hög nivå användes: direkt klassificering, postklassificering och fältbaserad klassificering. Ingen av de modeller som jämfördes i studien uppfyllde alla krav på allmän teknisk genomförbarhet. Direkta klassificeringsmodeller var mycket resurskrävande och långsamma. Postklassificeringsmodellen hade kritiskt låg träffsäkerhet men önskvärda lagringsegenskaper. Den bildfältbaserade MobileGoNogo-modellen var bättre i alla mätvärden utom i upplösningen, där den var betydligt lägre än någon annan modell. Vi drar slutsatsen att den nya modellen erbjuder en genomförbar lösning för icke-kritiska tillämpningar med låg upplösning.