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Modern content delivery networks (CDNs) allow their customers (i.e., operators of web services) to customize the processing of requests by uploading and executing code at the edges of the CDN's network. To achieve scale, CDNs have forgone heavyweight virtualization techniques. Instead, all requests often execute within the same OS or even process. However, performance interference may arise when these requests have differing demands on multiple system resources. In this paper, we study the sources of performance interference based on workloads from real customers, identify the lack of multi-resource fairness as the culprit, and show that existing schedulers available in commodity OSs are insufficient to enforce fairness between customers.We then design Sandpaper, a new and practical multiresource request scheduler for mitigating performance interference in CDN edge environments. Sandpaper enforces fairness despite constraints, such as sitting within the application runtime and running atop the OS's underlying resource schedulers. By leveraging key insights about the differences between theoretical system models and real systems, Sandpaper bridges the trade-off between resource utilization and multi-resource fairness that plagues existing schedulers. We implement Sandpaper atop Varnish, an open-source CDN edge proxy, and show that it mitigates performance interference while maintaining high resource utilization and with little performance overhead.

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In this thesis we present SIRUM: a system for Scalable Informative RUle Mining from multidimensional data. Informative rules have recently been studied in several contexts, including data summarization, data cube exploration and data quality. The objective is to produce a concise set of rules (patterns) over the values of the dimension attributes that provide the most information about the distribution of a numeric measure attribute. SIRUM optimizes this task for big, wide and distributed datasets. We implemented SIRUM in Spark and observed significant performance improvements on real data due to our optimizations.iii Acknowledgements First, I would like to give the most sincere thanks to my supervisor, Professor Lukasz Golab, for his invaluable support and guidance, his patience and encouragement. This thesis would not have been possible without his help and dedication. My sincere thanks also go to Dr. Divesh Srivastava for his insights and feedback.

Over the last two decades, several algorithms have been proposed to infer the type of relationship between Autonomous Systems (ASes). While the recent works have achieved increasingly higher accuracy, there has not been a systematic study on the uncertainty of AS relationship inference. In this paper, we analyze the factors contributing to this uncertainty and introduce a new paradigm to explicitly model the uncertainty and reflect it in the inference result. We also present PARI, an exemplary algorithm implementing this paradigm, that leverages a novel technique to capture the interdependence of relationship inference across AS links.