Caching is crucial for enabling high-throughput networks for data intensive applications. Traditional caching technology relies on DRAM, as it can transfer data at a high rate. However, DRAM capacity is subject to contention by most system components and thus is very limited, implying that DRAM-only caches cannot scale to meet growing demand. Fortunately, persistent memory and flash storage technologies are rapidly evolving and can be utilized alongside DRAM to increase cache capacities. To do so without compromising network performance requires caching techniques adapted to the characteristics of these technologies. In this paper, we model the cache as a collection of storage blocks with different rate parameters and utilization costs. We introduce an optimization technique based on the drift-plus-penalty method and apply it in a framework which enables joint caching and forwarding. We show that it achieves an optimal trade-off between throughput and cache utilization costs in a virtual control plane. We then develop a corresponding practical policy in the data plane. Finally, through simulations in several settings, we demonstrate the superior performance of our proposed approach with respect to total user delay and cache utilization costs.

翻译：缓存技术对于实现数据密集型应用的高吞吐量网络至关重要。传统缓存技术依赖DRAM（动态随机存取存储器），因其能以高速率传输数据。然而，DRAM容量受系统多数组件的竞争影响而极为有限，这意味着仅依赖DRAM的缓存无法扩展以满足日益增长的需求。幸运的是，持久性内存与闪存存储技术正在快速发展，可与DRAM协同使用以增加缓存容量。要在不损害网络性能的前提下实现这一目标，需要采用适应这些技术特性的缓存技术。本文中，我们将缓存建模为具有不同速率参数和使用成本的存储块集合。我们提出了一种基于漂移加惩罚法的优化技术，并将其应用于支持联合缓存与转发的框架中。研究表明，该技术能在虚拟控制平面中实现吞吐量与缓存使用成本之间的最优权衡。随后，我们在数据平面中制定了相应的实用策略。最后，通过多种场景下的仿真，我们验证了所提方法在总用户时延和缓存使用成本方面的优越性能。