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Network Congestion and Performance Management

Creative Commons 'BY' version 4.0 license

We increasingly depend on well-behaved networks in the course of every-day activities for business, community, government, science, and recreation. And with more people demanding a greater variety of services comes sharp disagreement about which needs are most important. Unfortunately, today’s technology is inadequate to guarantee the performance of dynamic and diverse workloads in such a way that we are prevented from hurting each other’s goals.

No one likes waiting in traffic, whether on a road or on a computer network. Stuttering progress and slow interactive feedback annoy everyone and cost time and money. A network should aim to (1) minimize latency, (2) maximize bandwidth, (3) share resources according to agreements, (4) enable incremental deployment, and (5) minimize administrative overhead. Many technologies have been developed, but none yet satisfactorily address all five of these goals. The best performing solutions to date achieve some success in goals 1-3, but they often downplay the importance of goals 4-5. Solutions almost always require coordinated configurations across many pieces of a network, but they end up being either impractical or suffer poor performance when even a single piece of a network is not cooperating.

In this dissertation I present an overview of the multilayered problem surrounding network performance, a transport-level solution called TCP Inigo, and foundations for a clean-

slate queueing discipline solution based on a new combination of recent real-time and economic scheduling algorithms. TCP Inigo uses independent delay-based algorithms on the sender and receiver. In emulated experiments with single administrative domains, a situation common in data centers or a single HPC cluster, TCP Inigo’s fairness, bandwidth, and latency indices are up to 1.3× better than the best available solution. When deployed in a more complex environment, such as across administrative domains, TCP Inigo performs up to 42× better.

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