RINGS: Intelligent and Resilient Virtualization of Massive MIMO Physical Layer

RINGS：大规模 MIMO 物理层的智能、弹性虚拟化

• 批准号: 2147946
• 负责人: Lin Zhong
• 金额: $ 99.99万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2147946&HistoricalAwards=false
• 关键词: RINGS; Intelligent; Resilient; Virtualization; Massive

NextG network systems rely on virtualization to move away from specialized, dedicated equipment to cloud and edge datacenters, to reduce cost and accelerate innovation. So far such virtualization efforts have met with very limited success, making inroads largely with 4G/LTE small cells. This is because 5G and beyond employ compute-intensive technologies such as massive multiple-input, multiple output (MIMO) and low-density parity-check (LDPC) code to deliver the unprecedented network performance. Massive MIMO not only demands massive computational power itself, but also proportionally increases that of LDPC. Not surprisingly, existing commercial massive MIMO solutions all rely on specialized, dedicated hardware such as FPGA and application-specific integrated circuits. Massive MIMO remains the largest barrier toward virtualized mobile networks. The goal of the proposed project is to overcome this technical barrier and virtualize the massive MIMO physical layer for NextG network systems. In doing so, the project will expedite the adoption of massive MIMO, resulting in more capable, more efficient, and more cost-effective mobile networks. Through our ongoing collaborations with industry leaders, the project will timely transfer technologies into practice. It also provides a meeting ground for software systems and wireless communication research and creates timely content for teaching Computer Science majors about the wireless physical layer. The project will provide a platform to engage undergraduate students and high-school students in computing research, especially women and underrepresented minorities.The project targets the following scientific contributions. (1) Design and implementation of the massive MIMO physical layer that scales up on a many-core server efficiently and intelligently. We will combine the efficiency of static and elasticity of dynamic task scheduling and devise latency-driven, automated schemes for optimal resource provisioning. (2) Distributed design and implementation that can utilize compute resources integrated over a local-area network, beyond a single server. We will leverage programmable switches to minimize the impact of the network and to utilize commodity servers as well as accelerators to achieve scale, cost effectiveness and energy efficiency beyond the reach of a single many-core server. (3) Elastic design and implementation that match up to dynamics in mobile network workload, simultaneously achieving high resilience, low cost for the network operator, and high utilization for the cloud provider, in a multi-tenant environment. We will explore serverless computing and develop it further to better meet the stringent latency requirement of the massive MIMO physical layer.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.

NextG 网络系统依靠虚拟化从专用设备转向云和边缘数据中心，以降低成本并加速创新。到目前为止，此类虚拟化工作取得的成功非常有限，主要是通过 4G/LTE 小型基站取得进展。这是因为 5G 及更高版本采用大规模多输入多输出 (MIMO) 和低密度奇偶校验 (LDPC) 代码等计算密集型技术来提供前所未有的网络性能。 Massive MIMO不仅本身需要大量的计算能力，还会成比例地提高LDPC的计算能力。毫不奇怪，现有的商业大规模 MIMO 解决方案都依赖于专门的专用硬件，例如 FPGA 和专用集成电路。大规模 MIMO 仍然是虚拟化移动网络的最大障碍。该项目的目标是克服这一技术障碍，虚拟化 NextG 网络系统的大规模 MIMO 物理层。在此过程中，该项目将加快大规模 MIMO 的采用，从而打造更强大、更高效且更具成本效益的移动网络。通过我们与行业领导者的持续合作，该项目将及时将技术转化为实践。它还为软件系统和无线通信研究提供了一个聚会场所，并为计算机科学专业的无线物理层教学创建了及时的内容。该项目将为本科生和高中生，特别是女性和代表性不足的少数族裔参与计算机研究提供一个平台。该项目的目标是做出以下科学贡献。 (1)大规模MIMO物理层的设计与实现，可在多核服务器上高效、智能地扩展。我们将结合静态的效率和动态任务调度的弹性，并设计延迟驱动的自动化方案以实现最佳资源配置。 (2) 分布式设计和实现，可以利用局域网上集成的计算资源，而不仅仅是单个服务器。我们将利用可编程交换机来最大限度地减少网络的影响，并利用商用服务器和加速器来实现超出单个多核服务器所能达到的规模、成本效益和能源效率。 (3) 与移动网络工作负载的动态相匹配的弹性设计和实施，在多租户环境中同时实现高弹性、网络运营商的低成本和云提供商的高利用率。我们将探索无服务器计算并进一步开发它，以更好地满足大规模 MIMO 物理层严格的延迟要求。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，认为值得支持。

项目成果

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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
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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