CAREER: Computing-Aware Network Optimization for Efficient Distributed Data Analytics at the Wireless Edge

职业：计算感知网络优化，用于无线边缘的高效分布式数据分析

• 批准号: 1943226
• 负责人: Jia Liu
• 金额: $ 52.41万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2021-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1943226&HistoricalAwards=false
• 关键词: CAREER; Computing; Aware; Network; Optimization

In recent years, machine learning (ML) and artificial intelligence (AI) applications are quickly finding their ways into our everyday life. All of these applications generate and inject a massive volume of data into the network for a wide range of complex ML/AI data analytics tasks, including but not limited to the training and/or inferences in computer vision, natural language processing, recommendation systems, etc. However, most of the existing wireless network control and optimization algorithms rarely take the new characteristics of ML/AI data analytic traffic into considerations. Likewise, most ML/AI data analytics algorithms oversimplify the underlying wireless networks as "bit pipes" and ignore their complex networking and physical layer constraints, hence leading to poor overall data analytics efficiency. The overarching theme of this CAREER research program is to bridge the gap between the rapidly growing ML/AI data analytics demands and the existing networking and communication technologies. The principal investigator (PI) explore a cross-disciplinary understanding between wireless networking and data analytics through a unified research program, which consists of the development of tractable theoretical models, exploration of theoretical performance bounds and limits, and the development of low-complexity distributed algorithms and protocols that are easy to implement in practice.In this CAREER program, the PI will develop networking-computing co-designs to facilitate ML/AI data analytics with data and model parallelisms in wireless edge networks. The PI will focus on three complementary research thrusts, each of which addresses one key aspect in supporting distributed data analytics at a different protocol layer: (i) communication-efficient distributed optimization at the physical layer; (ii) joint-queueing-computing scheduling at the medium access control layer; and (iii) admission control and resource virtualization at the transport layer. Collectively, the results in this research contribute to a new direction of wireless network control and optimization theory and systems design. The proposed research will serve as a foundation of the next-generation wireless networking that supports a plethora of data analytics and ML/AI applications. Due to its unique scientific and engineering challenges, this research program encompasses strong and holistic expertise in mathematical modeling, optimization, control, queueing theory, stochastic analysis, as well as deep knowledge of ML/AI system operations in practice. The proposed research will support not only the networking, communications, control, and machine learning research communities, but also the general public, by developing new optimization technologies for substantially improved network and data analytics performances.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.

近年来，机器学习（ML）和人工智能（AI）应用正在迅速进入我们的日常生活。所有这些应用程序都会生成大量数据并将其注入到网络中，以执行各种复杂的ML/AI数据分析任务，包括但不限于计算机视觉、自然语言处理、推荐系统等方面的训练和/或推理。同样，大多数ML/AI数据分析算法将底层无线网络过度简化为“位管道”，并忽略了其复杂的网络和物理层约束，从而导致整体数据分析效率低下。这个CAREER研究计划的首要主题是弥合快速增长的ML/AI数据分析需求与现有网络和通信技术之间的差距。首席研究员（PI）通过统一的研究计划探索无线网络和数据分析之间的跨学科理解，该研究计划包括开发易于处理的理论模型，探索理论性能界限和限制，以及开发易于在实践中实现的低复杂度分布式算法和协议。PI将开发网络计算协同设计，以促进ML/AI数据分析与无线边缘网络中的数据和模型并行性。PI将专注于三个互补的研究方向，每个方向都解决了在不同协议层支持分布式数据分析的一个关键方面：（i）物理层的通信高效分布式优化;（ii）媒体访问控制层的联合调度计算调度;（iii）传输层的准入控制和资源虚拟化。这些研究成果为无线网络控制与优化理论和系统设计提供了一个新的方向。拟议的研究将作为下一代无线网络的基础，支持大量的数据分析和ML/AI应用。由于其独特的科学和工程挑战，该研究计划包括数学建模，优化，控制，嵌入式理论，随机分析方面的强大和全面的专业知识，以及ML/AI系统操作实践的深入知识。该研究计划不仅将支持网络、通信、控制和机器学习研究社区，还将通过开发新的优化技术来大幅提高网络和数据分析性能，从而支持公众。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

The Sample Complexity of Best-k Items Selection from Pairwise Comparisons

• 发表时间: 2020-07
• 作者: Wenbo Ren;Jia Liu;N. Shroff

MATE: A Memory-Augmented Time-Expansion Approach for Optimal Trip-Vehicle Matching and Routing in Ride-Sharing

MATE：一种内存增强时间扩展方法，用于拼车中的最佳出行车辆匹配和路线

• DOI: 10.1145/3396851.3397726
• 发表时间: 2020
• 期刊: Proc. ACM e-Energy
• 作者: Tian, Ye;Liu, Jia;Xia, Cathy H.
• 通讯作者: Xia, Cathy H.

Adaptive Multi-Hierarchical signSGD for Communication-Efficient Distributed Optimization

用于通信高效分布式优化的自适应多层次符号SGD

• DOI: 10.1109/spawc48557.2020.9154256
• 发表时间: 2020
• 期刊: Special Session on Distributed Signal Processing for Coding and Communications
• 作者: Yang, Haibo;Zhang, Xin;Fang, Minghong;Liu, Jia
• 通讯作者: Liu, Jia