论文标题
记忆分解系统：挑战与机遇
Systems for Memory Disaggregation: Challenges & Opportunities
论文作者
论文摘要
内存分解通过解耦CPU和应用程序的内存分配来解决集群中的内存不平衡，同时还将（内存密集型）应用程序的有效内存容量增加到传统固定容量服务器施加的本地内存限制之外。随着网络在紧密联系的环境中的速度（如现代数据中心）更接近DRAM速度，在此空间中，最近的工作量越来越大，从汇总传统服务器的存储器的群集使用群集的共享系统到针对硬件中存储器的系统的系统。在本报告中，我们查看了这些最近的记忆分解系统，并研究了指导其设计的重要因素，例如，内存接触到应用程序的界面，其运行时设计和相关优化，以保留近乎本地的应用程序性能，保留他们在管理集群内存中采用的各种方法来管理集群内存以最大程度地利用且分析了相关的贸易端。最后，我们讨论了一些开放的问题和潜在的未来方向，这些方向可以使分解更适合采用。
Memory disaggregation addresses memory imbalance in a cluster by decoupling CPU and memory allocations of applications while also increasing the effective memory capacity for (memory-intensive) applications beyond the local memory limit imposed by traditional fixed-capacity servers. As the network speeds in the tightly-knit environments like modern datacenters inch closer to the DRAM speeds, there has been a recent proliferation of work in this space ranging from software solutions that pool memory of traditional servers for the shared use of the cluster to systems targeting the memory disaggregation in the hardware. In this report, we look at some of these recent memory disaggregation systems and study the important factors that guide their design, such as the interface through which the memory is exposed to the application, their runtime design and relevant optimizations to retain the near-native application performance, various approaches they employ in managing cluster memory to maximize utilization, etc. and we analyze the associated trade-offs. We conclude with a discussion on some open questions and potential future directions that can render disaggregation more amenable for adoption.
