Control of Communication Networks: Modeling, Simulation, and Optimization

该建议描述了一种新的方法，用于设计网络控制算法，包括使用流量模型的在线仿真。 该方法便于对仿真数据进行快速启发式分析，可应用于各种网络控制问题，也可应用于各种网络决策问题。 PI有两个简单的问题，这种方法的概念验证实现：多类加权调度问题，和随机早期检测（RED）选择丢弃策略的问题。 在这两种情况下，与以前的控制政策相比，它们都以经验证明了显著的上级性能。 PI提出了大量的探索，分析和实施他们的技术。 他们计划将这些想法应用于网络决策问题，包括准入/访问控制，流量/拥塞控制，各种问题有关的代理服务，并选择诊断和恢复行动。 他们还计划将这些想法扩展到TCP流等闭环系统，并评估使用各种流量模型时出现的问题，包括流体流和远程依赖流量模型。 该计划利用了流量建模方面不断增长的工作，提供了使用此类模型影响网络控制性能的潜力。 所提出的方法的广泛适用性打开了一个广泛的控制算法在整个网络的同时改善的可能性。 使用这种控制方法与熟悉的模型推理技术，以更新不断变化的网络环境（包括攻击）下的流量模型，也自然导致自适应控制机制，同样广泛的各种问题。

This proposal describes a novel approach for designing network control algorithms that incorporate online simulation using traffic models. The approach facilitates rapid heuristic analysis of simulation data, and can be applied to a wide variety of network control problems, and can be applied to a wide variety of network decision problems. The PIs have proof-of-concept implementations of this approach for two simple problems: the multiclass weighted scheduling problem, and the problem of selecting a dropping policy for random early detection (RED). In both cases, they have empirically demonstrated substantially superior performance when comparing to previous control policies. The PIs propose the substantial exploration, analysis, and implementation of their technique. They plan to apply these ideas to network decision problems including admission/access control, flow/congestion control, various problems relating to proxy services, and selection of diagnosis and recovery actions. They also plan to extend these ideas to closed-loop systems such as TCP flows, as well as to evaluate the issues that arise in utilizing various traffic models, including fluid-flow and long-range dependence traffic models. This plan exploits the growing work on traffic modeling, providing the potential to use such models to impact network control performance. The broad applicability of the proposed approach opens the possibility for simultaneous improvement of a wide array of control algorithms across the network. The use of this control approach together with familiar model inference techniques to update the traffic model under changing network environments (including attacks) also leads naturally to adaptive control mechanisms for the same wide variety of problems.