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Stability and Sensitivity Analysis of Traffic-Shaping Algorithms Inspired by Chemical Engineering

机译:化学工程启发的交通整形算法的稳定性和敏感性分析

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Traffic-shaping algorithms, such as congestion avoidance or rate limiting for cloud services, control the dynamics of packet flows in a distributed setting. However, despite their significance in networking, it is still hard to design them, to predict their dynamic behavior, and to prove their robustness. With the help of a simple chemical metaphor, we recently proposed a novel engineering framework for designing, executing, and analyzing traffic-shaping algorithms: by letting packets react akin to chemical molecules, the design of (distributed) flow-control algorithms becomes drawing reaction networks. In this way, we gain in analyzability: the related fluid model, describing the emergent behavior of the overall system, can be derived from the corresponding reaction networks automatically. In this article, we describe how to fine-tune chemical algorithms. We therefore analyze their transient behavior in order to reveal the semantics and sensitivity of key parameters. Methods from different domains assist us in this endeavor: We first linearize the fluid model as proposed in Metabolic Control Analysis, we then describe it in control-theoretic terms, and finally, we characterize its sensitivity in the frequency domain. We demonstrate the feasibility and reveal the limits of our method by applying it to a chemical algorithm for distributed rate control.
机译:流量整形算法(例如针对云服务的拥塞避免或速率限制)控制分布式设置中数据包流的动态。但是,尽管它们在联网中具有重要意义,但仍然很难对其进行设计,预测其动态行为并证明其鲁棒性。在一个简单的化学隐喻的帮助下,我们最近提出了一种用于设计,执行和分析流量整形算法的新颖工程框架:通过让数据包类似于化学分子进行反应,(分布式)流量控制算法的设计成为绘制反应网络。这样,我们就获得了可分析性:描述整个系统的紧急行为的相关流体模型可以自动从相应的反应网络中导出。在本文中,我们描述了如何微调化学算法。因此,我们分析了它们的瞬时行为,以揭示关键参数的语义和敏感性。来自不同领域的方法可以帮助我们实现这一目标:首先按照代谢控制分析中的要求线性化流体模型,然后以控制理论的术语对其进行描述,最后在频域中表征其敏感性。通过将其应用于分配速率控制的化学算法,我们证明了可行性并揭示了我们方法的局限性。

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