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) 应用正在迅速融入我们的日常生活。所有这些应用程序都会生成大量数据并将其注入网络，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、推荐系统中的训练和/或推理，然而，大多数现有的无线网络控制和优化算法很少考虑ML/AI数据分析流量的新特征。同样，大多数机器学习/人工智能数据分析算法将底层无线网络过度简化为“比特管道”，而忽略了其复杂的网络和物理层限制，从而导致整体数据分析效率低下。该职业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，该计划包括开发易于处理的理论模型、探索理论性能边界和限制以及开发低复杂性分布式易于在实践中实施的算法和协议。在这个职业计划中，PI 将开发网络计算协同设计，以促进 ML/AI 数据分析，并在无线边缘网络中实现数据和模型并行性。该 PI 将重点关注三个互补的研究方向，每个方向都涉及支持不同协议层的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排队计算调度； (iii) 传输层的准入控制和资源虚拟化。总的来说，这项研究的结果为无线网络控制和优化理论以及系统设计的新方向做出了贡献。拟议的研究将作为下一代无线网络的基础，支持大量数据分析和机器学习/人工智能应用。由于其独特的科学和工程挑战，该研究项目涵盖了数学建模、优化、控制、排队论、随机分析方面强大而全面的专业知识，以及 ML/AI 系统实践操作的深入知识。拟议的研究不仅将通过开发新的优化技术来显着提高网络和数据分析性能，从而支持网络、通信、控制和机器学习研究社区，而且还将支持公众。该奖项反映了 NSF 的法定使命，并被视为值得通过使用基金会的智力优点和更广泛的影响审查标准进行评估来支持。

项目成果

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

成对比较中 Best-K 项目选择的样本复杂度

• 发表时间: 2020-07
• 期刊: Proc. of International Conference on Machine Learning
• 作者: Ren, Wenbo;Liu, Jia;Shroff, Ness B.
• 通讯作者: Shroff, Ness B.

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: 2020-06
• 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: 2020-05
• 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