Cache optimization, i.e., determining the optimal content placement and routing paths, is essential for obtaining high performance of cache-enabled networks. This paper studies the problem of optimizing system throughput and content delivery cost over cache networks with lossy links (i.e., ICN-based wireless IoT systems), where content is divided into packet-level chunks, and packets may be lost in transmission. We first propose a new performance metric – the expected overall content routing cost for satisfied requests (RCS), for better characterizing content delivery cost under packet losses. RCS at the same time possesses the attractive mathematical property of super-modularity. We then formulate an optimization problem for the task through jointly optimizing content caching and request routing, and analyze it under fixed-routing scenario. The formulated problem is NP-hard and we prove it is reducible to the one of minimizing content routing cost without packet losses. We establish rules for the reduction, and leverage existing efficient algorithm to solve the problem. We also propose a potential -based online algorithm that is simple and adaptive to traffic changes and packet losses. The effectiveness of our mechanism is validated through extensive simulations over a wide array of network topologies.

中文翻译：

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联合优化有损缓存网络的吞吐量和内容交付成本

缓存优化，即确定最佳内容放置和路由路径，对于获得启用缓存的网络的高性能至关重要。本文研究了在具有有损链路的缓存网络（即基于 ICN 的无线物联网系统）上优化系统吞吐量和内容交付成本的问题，其中内容被划分为数据包级块，数据包在传输过程中可能会丢失。我们首先提出了一个新的性能指标——满足请求的预期整体内容路由成本(RCS)，以便更好地表征丢包情况下的内容交付成本。RCS 同时具有超模块化的吸引人的数学特性。然后我们通过联合优化内容缓存和请求路由来制定任务的优化问题，并在固定路由场景下对其进行分析。公式化的问题是 NP-hard 问题，我们证明它可以简化为在不丢失数据包的情况下最小化内容路由成本的问题。我们建立了归约规则，并利用现有的高效算法来解决问题。我们还提出了一个潜在的 基于在线算法，简单且适应流量变化和丢包。我们机制的有效性通过对各种网络拓扑的广泛模拟得到验证。