CIF: Small: Fundamental Tradeoffs Between Communication Load and Storage Resources in Distributed systems

CIF：小：分布式系统中通信负载和存储资源之间的基本权衡

• 批准号: 1910309
• 负责人: Daniela Tuninetti
• 金额: $ 47.5万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910309&HistoricalAwards=false
• 关键词: CIF; Small; Fundamental; Tradeoffs; Between

Future data communication networks will have to support multitude of devices abundantly spread all over, collecting, processing and transferring data on the fly. Such networks are expected to struggle to meet the demand from such data communication needs. At the same time, the devices can leverage their inexpensive and distributed memory units to reduce the cost of exchanging information. A key question is which data should be placed in the memories so that, irrespective of what the devices will be requested to compute in the future, the amount of communication to satisfy these requests will be the smallest possible. The more devices the system can satisfy with a single transmission, the larger the savings are in terms of reduction of the number of transmissions. These savings translate to reduced communication costs and an ability to run computation-intensive and latency-critical applications at resource-limited devices. This project aims to develop the theoretical and algorithmic foundation for distributed computation in presence of local and limited storage resources for future hybrid hierarchical networks, where distributed devices collaborate to solve big-data inference tasks. Despite their fundamental nature, the results of this research are impact the design of emerging communication models in industry. This project also develops a rich educational program for students, who will acquire critical skills to be successful in a competitive, diverse, and global workforce market.This research identifies critical questions related to communication in distributed peer-to-peer settings with local limited storage resources. The technical aims of the project are divided into two related thrusts: distributed cache-aided "Fog Radio Access Network" architectures and peer-to-peer distributed data shuffling. The former problem models the hybrid network architecture envisaged for 5G wireless networks, while the latter finds applications in distributed computation in big data and machine learning algorithms. Both share the caching theme, i.e., leverage local storage to reduce global communication load, but more importantly leverage the distributed nature of the encoding in either the delivery or data shuffling phase. The concomitant technical questions are novel in many aspects, and span information theory, coding theory, and combinatorics. The research includes innovative approaches to derive converse and achievable bounds, with provable performance guarantees. The overarching goal is to develop a fundamental framework for distributed computation, with implications to other open problems such as distributed index coding and distributed interference alignment.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.

未来的数据通信网络将必须支持大量分布在各处的设备，实时收集、处理和传输数据。预计此类网络将难以满足此类数据通信需求。同时，这些设备可以利用其廉价的分布式存储单元来降低交换信息的成本。一个关键问题是哪些数据应该放置在存储器中，以便无论设备将来需要计算什么，满足这些请求的通信量都将尽可能小。系统一次传输可以满足的设备越多，在减少传输次数方面节省的费用就越大。这些节省意味着通信成本的降低以及在资源有限的设备上运行计算密集型和延迟关键型应用程序的能力。该项目旨在为未来混合分层网络的本地和有限存储资源开发分布式计算的理论和算法基础，其中分布式设备协作解决大数据推理任务。尽管具有基本性质，但这项研究的结果正在影响工业中新兴通信模型的设计。该项目还为学生开发了丰富的教育计划，他们将获得在竞争激烈、多元化的全球劳动力市场中取得成功的关键技能。这项研究确定了与本地有限存储的分布式点对点环境中的通信相关的关键问题资源。该项目的技术目标分为两个相关的主旨：分布式缓存辅助的“雾无线接入网络”架构和点对点分布式数据混洗。前一个问题对 5G 无线网络设想的混合网络架构进行建模，而后者则在大数据和机器学习算法的分布式计算中得到应用。两者共享缓存主题，即利用本地存储来减少全局通信负载，但更重要的是在交付或数据混洗阶段利用编码的分布式特性。随之而来的技术问题在许多方面都是新颖的，涵盖信息论、编码理论和组合学。该研究包括创新方法来推导出可逆且可实现的边界，并提供可证明的性能保证。总体目标是开发分布式计算的基本框架，并对分布式索引编码和分布式干扰对齐等其他开放问题产生影响。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能力进行评估，被认为值得支持。更广泛的影响审查标准。

项目成果

Cache-Aided General Linear Function Retrieval

缓存辅助的一般线性函数检索

• DOI: 10.3390/e23010025
• 发表时间: 2021-01
• 期刊: Entropy
• 影响因子: 2.7
• 作者: Wan, Kai;Sun, Hua;Ji, Mingyue;Tuninetti, Daniela;Caire, Giuseppe
• 通讯作者: Caire, Giuseppe

Optimal Linear Coding Schemes for the Secure Decentralized Pliable Index Coding Problem

安全分散柔韧指数编码问题的最优线性编码方案

• DOI: 10.1109/itw46852.2021.9457649
• 发表时间: 2021-01
• 期刊: 2020 IEEE Information Theory Workshop (ITW
• 作者: Liu, Tang;Tuninetti, Daniela
• 通讯作者: Tuninetti, Daniela

Robust, Private and Secure Cache-Aided Scalar Linear Function Retrieval From Coded Servers

从编码服务器检索稳健、私密且安全的缓存辅助标量线性函数

• DOI: 10.1109/jsac.2022.3142360
• 发表时间: 2021-02-06
• 期刊: IEEE Journal on Selected Areas in Communications
• 影响因子: 16.4
• 作者: Qifa Yan;Daniela Tuninetti
• 通讯作者: Daniela Tuninetti

“New optimal trade-off point for coded caching systems with limited cache size

Ø 具有有限缓存大小的编码缓存系统的新最佳权衡点

• 发表时间: 2023-10
• 期刊: submitted to IEEE ICC 2024
• 作者: Yinbin Ma;Daniela Tuninetti
• 通讯作者: Daniela Tuninetti

A General Coded Caching Scheme for Scalar Linear Function Retrieval

标量线性函数检索的通用编码缓存方案

• DOI: 10.1109/isit45174.2021.9518262
• 发表时间: 2021-01
• 期刊: 2021 IEEE International Symposium on Information Theory (ISIT
• 作者: Ma, Yinbin;Tuninetti, Daniela
• 通讯作者: Tuninetti, Daniela