CAREER: End-to-End Network Design for Unified Memory Disaggregation

职业：统一内存分解的端到端网络设计

• 批准号: 1845853
• 负责人: Mosharaf Chowdhury
• 金额: $ 57.82万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1845853&HistoricalAwards=false
• 关键词: CAREER; End; Network; Design; Unified

Applications in modern cloud datacenters are deployed in resource containers to isolate them from each other. Memory stranding is a pervasive problem in such containerized datacenters, where many memory-intensive applications grind to a halt even when free memory exists in other machines. This leads to low utilization, memory fragmentation, and overall increased cost. Memory disaggregation over ultra-fast networks can pool together such stranded memory in theory, but making it practical faces novel systems design, algorithmic, and integration challenges. They include bridging the still-sizable latency gap between local memory access vs. Remote Direct Memory Access (RDMA), transparently addressing network-wide fault-tolerance, load imbalance, and performance isolation issues, scalability, and enabling support for heterogeneous software and hardware technologies.The overarching research objective of this proposal is to realize a Unified Disaggregated Memory (UDM) abstraction over ultra-fast networks to expose stranded memory across the datacenter as a pool of available memory to out-of-memory containers in a fast, resilient, and scalable manner without any changes to the applications. By designing a comprehensive solution to address host-level, network-level, and end-to-end aspects of the aforementioned challenges, this research aims to make memory disaggregation practical. Specifically, by leveraging the unique characteristics of memory-intensive workloads, ultra-low-latency networks, and multi-tenancy in modern datacenters, this proposal will (i) design a low-latency host networking stack; (ii) enable performance isolation throughout the network; (iii) provide resilience to network-wide uncertainties such as failures and load imbalance; and (iv) incorporate support for heterogeneous memory (e.g., persistent memory), networking technologies, and resource management software.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

现代云计算中心中的应用程序部署在资源容器中，以使它们彼此隔离。内存搁浅在这种容器化的数据中心中是一个普遍存在的问题，即使其他机器中存在空闲内存，许多内存密集型应用程序也会陷入停顿。这会导致低利用率、内存碎片和总体成本增加。超高速网络上的内存分解在理论上可以将这些搁浅的内存集中在一起，但使其实用化面临着新的系统设计，算法和集成挑战。它们包括弥合本地内存访问与远程直接内存访问（RDMA）之间仍然相当大的延迟差距，透明地解决网络范围的容错，负载不平衡和性能隔离问题，可扩展性，该方案的主要研究目标是在超高速缓存上实现统一的非聚合存储器（UDM）抽象，快速网络以快速、有弹性且可扩展的方式将数据中心内的闲置内存作为可用内存池公开给内存不足容器，而无需对应用程序进行任何更改。通过设计一个全面的解决方案，以解决主机级，网络级和端到端方面的上述挑战，本研究的目的是使内存分解实用。具体而言，通过利用现代数据中心中的内存密集型工作负载、超低延迟网络和多租户的独特特性，该提案将（i）设计低延迟主机网络堆栈;（ii）实现整个网络的性能隔离;（iii）提供对网络范围的不确定性（例如故障和负载不平衡）的弹性;以及（iv）结合对异构内存（例如，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Aequitas: Admission Control for Performance-Critical RPCs in Datacenters

Aequitas：数据中心中性能关键型 RPC 的准入控制

• DOI: 10.1145/3544216.3544271
• 发表时间: 2022
• 期刊: ACM SIGCOMM
• 作者: Zhang, Yiwen;Kumar, Gautam;Dukkipati, Nandita;Wu, Xian;Jha, Priyaranjan;Chowdhury, Mosharaf;Vahdat, Amin
• 通讯作者: Vahdat, Amin

Hydra : Resilient and Highly Available Remote Memory

• 发表时间: 2019-10
• 作者: Youngmoon Lee;H. Maruf;Mosharaf Chowdhury;Asaf Cidon;K. Shin

NetLock: Fast, Centralized Lock Management Using Programmable Switches

• DOI: 10.1145/3387514.3405857
• 发表时间: 2020-07
• 期刊: Proceedings of the Annual conference of the ACM Special Interest Group on Data Communication on the applications, technologies, architectures, and protocols for computer communication
• 作者: Zhuolong Yu;Yiwen Zhang;V. Braverman;Mosharaf Chowdhury;Xin Jin

Programmable packet scheduling with a single queue

• DOI: 10.1145/3452296.3472887
• 发表时间: 2021-08
• 期刊: Proceedings of the 2021 ACM SIGCOMM 2021 Conference
• 作者: Zhuolong Yu;Chuheng Hu;Jingfeng Wu;Xiao Sun;V. Braverman;Mosharaf Chowdhury;Zhenhua Liu;Xin Jin

Effectively Prefetching Remote Memory with Leap

• 发表时间: 2019-11
• 作者: H. Maruf;Mosharaf Chowdhury