Evaluating and Improving Internet Load Balancing with Large-Scale Latency Measurements
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This dissertation evaluates and improves global and path-level load balancing in terms of performance similarity. We achieve this with large-scale latency measurements, which not only allow us to systematically identify and evaluate the performance issues of Internet load balancing at scale, but also enable us to develop data-driven approaches to improve its performance. Specifically, this dissertation consists of three parts. First, we study the issues of existing client aggregations for global load balancing and then design AP-atoms, a data-driven client aggregation learned from passive large-scale latency measurements. Second, we show that the latency imbalance between load-balanced paths, previously deemed insignificant, is now both significant and prevalent. We design Flipr, a network prober that actively collects large-scale latency measurements to characterize the latency imbalance issue. Lastly, we design another network prober, Congi, that can detect congestion at scale and use Congi to study the congestion imbalance problem at scale. We further demonstrate that latency and congestion imbalance could greatly affect the performance of various applications.