Networked systems, such as cloud infrastructures, are growing in size and complexity.They hold and generate a vast amount of configuration and operational data, whichis maintained in various locations and formats, and changes at various time scales.A wide range of protocols and technologies is used to access this data for networkmanagement tasks. A concept called ‘network search’ is introduced to make all thisdata available in real-time through a search platform with a uniform interface, whichenables location-independent access through search queries. Network search requires a network of search nodes, where the nodes have identicalcapabilities and work cooperatively to process search queries in a peer-to-peer fashion.A search node should indicate good performance results in terms of low query responsetimes, high throughputs, and low overhead costs and should scale to large networkedsystems with at least hundred thousands nodes. This thesis contributes in several aspects towards the design and implementation of anetwork search node. We designed a search node that includes three major components,namely, a real-time data sensing component, a real-time database, and a distributedquery-processing component. The design takes indexing of search terms and concurrencyof query processing into consideration, which accounts for fast response timesand high throughput of search queries. We implemented a network search node as asoftware package that runs on a server that provides a cloud service, and we evaluatedits performance on a cloud testbed of nine servers. The performance measurementssuggest that a network search system based on our design can process queries at lowquery latencies for a high query load, while maintaining a low overhead of computationalresources.

Bayesian network (BN) structure learning from data has been an active research area in the machine learning field in recent decades. Much of the research has considered BN structure learning as an optimization problem. However, the finding of optimal BN from data is NP-hard. This fact has driven the use of heuristic algorithms for solving this kind of problem. Amajor recent focus in BN structure learning is on search and score algorithms. In these algorithms, a scoring function is introduced and a heuristic search algorithm is used to evaluate each network with respect to the training data. The optimal network is produced according to the best score evaluated. This thesis investigates a range of search and score algorithms to understand the relationship between technique performance and structure features of the problems. The main contributions of this thesis include (a) Two novel Ant Colony Optimization based search and score algorithms for BN structure learning; (b) Node juxtaposition distribution for studying the relationship between the best node ordering and the optimal BN structure; (c) Fitness landscape analysis for investigating the di erent performances of both chain score function and the CH score function; (d) A classifier method is constructed by utilizing receiver operating characteristic curve with the results on fitness landscape analysis; and finally (e) a selective o -line hyperheuristic algorithm is built for unseen BN structure learning with search and score algorithms. In this thesis, we also construct a new algorithm for producing BN benchmark structures and apply our novel approaches to a range of benchmark problems and real world problem.