SPINE: Stochastic Performance Bounds for Information-Centric Communication Networks

内容缓存和分组转发功能的集成是通信网络中的最近的进化步骤，其是联网的存在理由从连接主机到递送内容的转变的结果。尽管最近兴起的缓存能力的网络，如信息中心网络（ICN）的概念，作为未来的互联网架构的候选人，缓存和转发元素的相互作用的基础仍然没有得到很好的理解。大多数方法都基于当前建立的内容交付体系结构，这些体系结构不捕获网络层上的缓存和缓存交互。对于ICN，其中，此外，传统的概念的流量和端到端的连接不举行，这样的方法引入性能建模错误，不能解释这些approaches.In这个项目中，我们的目标是推导出基于排队论的方法，利用随机通信系统的信息为中心的网络的性能分析和自适应控制。这样的系统包括在时变可用带宽资源和突发业务下具有缓存能力的路由器和路由器。我们预计，来自网络理论和随机网络演算的公式，如Fork-Join模型和统计服务曲线，有可能产生新的见解，例如，关于ICN调度算法的最优性，这可以导致ICN中的应用定制的通信方案。我们预计，自适应控制方法，如马尔可夫决策过程，与统计推理算法配对，可以获得自适应ICN通信的基本性能限制的见解。该提案的预期结果将提供对ICN设计空间的基本理解，并实现对ICN协议和应用的严格优化，这些优化超出了当今理论的最先进水平，例如，实现对具有马尔可夫加性交叉流量的ICN流量的最佳ICN分组调度。

The integration of content caching and packet forwarding functionalities is a recent evolutionary step in communication networks that is a consequence of a shift in the raison d'être of networking from connecting hosts to delivering content. Despite the recent rise of caching-capable networks, such as the concept of Information-Centric Networking (ICN), as candidates for future Internet architectures, the foundations of the interactions of caching and forwarding elements are still not well understood. Most methods are based on currently established content-delivery architectures that do not capture caching and queueing interactions on the network layer. For ICN, where, in addition, traditional notions of traffic flows and end-to-end connections do not hold, such methods introduce performance modeling errors that cannot be explained by these approaches.In this project, we aim at deriving a queuing theory based methodology for the performance analysis and adaptive control of information-centric networks that utilize stochastic communication systems. Such systems include caching-capable routers and schedulers under time-varying available bandwidth resources and bursty traffic. We expect that formulations from queueing theory and stochastic network calculus, such as Fork-Join models and statistical service curves, have the potential to yield new insights, e.g., regarding the optimality of ICN scheduling algorithms, which can lead to application-tailored communication schemes in ICN. We anticipate that adaptive control methods, such as Markov decision processes, paired with statistical inference algorithms allow gaining insights on fundamental performance limits for adaptive ICN communications. The expected results of this proposal will provide a fundamental understanding of the design space of ICN and enable rigorous optimizations of ICN protocols and applications that are beyond the state-of-the-art of today's theories, for example enabling optimal ICN packet scheduling for ICN traffic with Markov-additive cross traffic.