CAREER: In-Network Memory Management for Disaggregated Datacenters

职业：分类数据中心的网络内内存管理

• 批准号: 2047220
• 负责人: Anurag Khandelwal
• 金额: $ 62.66万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047220&HistoricalAwards=false
• 关键词: CAREER; Network; Memory; Management; Disaggregated

Data centers — the factories of the digital age — can consume as much power as a city of two million people, and in total consume two percent of the world’s electricity. Larger data centers can comprise over a million servers, each of which house CPUs, memory and storage. Memory in particular, can consume as high as 46% of average system energy, and even so, memory usage in today’s data centers can be as low as 20−30%. A key contributor to this problem is poor provisioning and utilization of memory across various data center applications. To address this problem, recent proposals have argued for memory disaggregation, which physically separates memory and CPUs into separate blades and connects them via the network. This approach not only promises better memory and CPU utilization, significantly improving data center energy efficiency, but also offers a number of additional benefits. Unfortunately, such a physical separation comes at a cost of performance for accessing memory efficiently, limiting its applicability. This project envisions a radically new design for memory disaggregation, which places memory management at emerging programmable elements in the network to enable high performance for disaggregated memory. If successful, this research will incentivize cloud providers to transition their data centers to disaggregated architectures, improving memory utilization, reducing energy consumption and consequently, total cost of ownership for their infrastructure. Planned outreach and curriculum development as a part of this project will broaden participation of underrepresented groups and educate high school, undergrad and graduate students on cloud systems and data center architectures. Over the last few years, significant improvements in inter-server network performance, coupled with stagnating intra-server interconnect performance, have driven advances in data center resource disaggregation — where server compute, memory and storage resources are physically separated into network attached resource “blades”. However, actualizing the benefits of resource disaggregation, while ensuring application performance, requires operating system (OS) support. Unfortunately, existing proposals to this end expose a hard tradeoff between application performance on one hand and resource elasticity on the other. The driving vision of this project is a fundamentally new network-centric design for the disaggregated OS — one that places resource management and access functionality in the data center network fabric to break the above tradeoff. This proposal specifically focuses on in-network memory management for the envisioned OS, and will exploit recent advances in programmable network hardware to realize the memory subsystem design. The end-goal is a data center-scale shared memory abstraction, where each disaggregated core can efficiently access any memory word in the data center’s disaggregated memory pool. The research goals of the project are i) enable compute/memory elasticity and hardware flexibility via network-assisted shared memory; ii) facilitate performant access to network-attached memory via network-driven optimizations; and iii) ensure scalability and fault-tolerance for the memory subsystem for data center-wide disaggregation. The project also provides a multidisciplinary platform to realize educational objectives of (i) developing system and experimental components for our systems and networking curriculum, (ii) involving undergraduate students in publishable research, and (iii) promoting science and engineering in high-school students and underrepresented populations.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.

数据中心-数字时代的工厂-消耗的电力相当于一个200万人口的城市，总共消耗了世界电力的2%。更大的数据中心可以包括超过一百万台服务器，每个服务器都包含CPU，内存和存储。特别是内存，可以消耗高达46%的平均系统能量，即使如此，今天的数据中心的内存使用率可以低至20 - 30%。造成此问题的一个关键因素是跨各种数据中心应用程序的内存配置和利用率低下。为了解决这个问题，最近的提议主张内存分解，它将内存和CPU物理地分离到单独的刀片中，并通过网络将它们连接起来。这种方法不仅保证了更好的内存和CPU利用率，显著提高了数据中心的能源效率，而且还提供了许多额外的贝内。不幸的是，这种物理分离是以有效访问内存的性能为代价的，限制了其适用性。该项目设想了一种全新的内存分解设计，将内存管理放在网络中新兴的可编程元件上，以实现分解内存的高性能。如果成功，这项研究将激励云计算提供商将其数据中心过渡到分散式架构，提高内存利用率，降低能耗，从而降低其基础设施的总拥有成本。作为该项目的一部分，计划的推广和课程开发将扩大代表性不足的群体的参与，并教育高中，本科和研究生云系统和数据中心架构。在过去几年中，服务器间网络性能的显著改善，加上服务器内互连性能的停滞不前，推动了数据中心资源分解的进步-其中服务器计算，内存和存储资源在物理上被分离到网络连接的资源“刀片”。然而，实现资源分解的贝内，同时确保应用程序的性能，需要操作系统（OS）的支持。不幸的是，现有的建议，为此暴露了一方面的应用程序的性能和另一方面的资源弹性之间的硬权衡。该项目的驱动愿景是为分散式操作系统提供一种全新的以网络为中心的设计-将资源管理和访问功能置于数据中心网络结构中，以打破上述权衡。该提案特别关注设想的操作系统的网络内内存管理，并将利用可编程网络硬件的最新进展来实现内存子系统设计。最终目标是数据中心规模的共享内存抽象，其中每个分解的核心可以有效地访问数据中心的分解内存池中的任何内存字。该项目的研究目标是：i）通过网络辅助共享内存实现计算/内存弹性和硬件可扩展性; ii）通过网络驱动优化促进对网络连接内存的高性能访问; iii）确保内存子系统的可扩展性和容错性，以实现数据中心范围的分解。该项目还提供了一个多学科的平台，以实现（i）为我们的系统和网络课程开发系统和实验组件，（ii）让本科生参与可持续研究，及（iii）在高等教育中推广科学及工程学，学生和代表性不足的人群。该奖项反映了NSF的法定使命，并且通过使用基金会的评估被认为值得支持知识价值和更广泛的影响审查标准。

项目成果

Prefetching Using Principles of Hippocampal-Neocortical Interaction

• DOI: 10.1145/3593856.3595901
• 发表时间: 2023-06
• 期刊: Proceedings of the 19th Workshop on Hot Topics in Operating Systems
• 作者: Michael Wu;Ketaki Joshi;Andrew Sheinberg;Guilherme Cox;Anurag Khandelwal;Raghavendra Pradyumna Pothukuchi;A. Bhattacharjee

Jiffy: elastic far-memory for stateful serverless analytics

• DOI: 10.1145/3492321.3527539
• 发表时间: 2022-03
• 期刊: Proceedings of the Seventeenth European Conference on Computer Systems
• 作者: Anurag Khandelwal;Yupeng Tang;R. Agarwal;Aditya Akella;I. Stoica

Karma: Resource Allocation for Dynamic Demands

• DOI: 10.48550/arxiv.2305.17222
• 发表时间: 2023-05
• 作者: Midhul Vuppalapati;Giannis Fikioris;R. Agarwal;Asaf Cidon;Anurag Khandelwal;É. Tardos

MIND: In-Network Memory Management for Disaggregated Data Centers

• DOI: 10.1145/3477132.3483561
• 发表时间: 2021-07
• 期刊: Proceedings of the ACM SIGOPS 28th Symposium on Operating Systems Principles
• 作者: Seung-seob Lee;Yanpeng Yu;Yupeng Tang;Anurag Khandelwal;Lin Zhong;A. Bhattacharjee