CIF:Small:Collaborative Research:Distributed Fog Computing for Non-Convex Big-Data Analytics

CIF：小：协作研究：用于非凸大数据分析的分布式雾计算

基本信息

批准号：
1719205
负责人：
Gesualdo Scutari
金额：
$ 27万
依托单位：
Purdue University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1719205&HistoricalAwards=false
关键词：
CIF Small Collaborative Research Distributed

项目摘要

In our data-deluge era, massive chunks of information, perpetually collected by pervasive sensors, are communicated and processed by distributed computational architectures. To address emergent big-data computational issues, this project embarks on an ambitious multidisciplinary research effort that aims at advancing the state-of-the-art in-network/distributed big-data processing via a general algorithmic framework for data analytics over massively distributed data sets. The proposed algorithmic framework enables fully distributed and parallel big-data analytics, for a variety of heterogeneous data sets over a wide range of computational architectures. The developed research directions are beneficial also to domains far beyond big-data analytics, such as signal processing, machine learning, next-generation wireless communications, smart-city and smart-grid networks. Research results are distributed through archival publications, courses, undergraduate research opportunities, tutorials and conference presentations.The developed scheme relies on a novel convexification/decomposition technique which accommodates a rich class of non-convex, unstructured and stochastic optimization tasks with non-separable objective functions. Algorithms are designed for settings where data are distributed across a large number of multi-core computational nodes, within a network of arbitrary topology with (possibly) time-varying and even random links. This new class of algorithms addresses shortcomings of current (non-parallel and non-distributed) convexification techniques via (i) full control of the degree of parallelism and distribution of the computation/signaling among processors/network nodes, and (ii) by offering a plethora of convex approximants, regularization terms, step-size rules, and communication protocols. Designed for time-varying or even random network topologies, the advocated framework demonstrates also another desirable attribute for distributed computations: resiliency to (random) network failures.

在我们的数据洪流时代，无处不在的传感器不断收集的大量信息，通过分布式计算架构进行通信和处理。为了解决新兴的大数据计算问题，该项目开展了一项雄心勃勃的多学科研究工作，旨在通过大规模分布式数据分析的通用算法框架来推进最先进的网络内/分布式大数据处理。数据集。所提出的算法框架能够对各种计算架构上的各种异构数据集进行完全分布式和并行的大数据分析。所开发的研究方向也有益于大数据分析之外的领域，例如信号处理、机器学习、下一代无线通信、智能城市和智能电网。研究成果通过档案出版物、课程、本科生研究机会、教程和会议演示进行分发。所开发的方案依赖于一种新颖的凸化/分解技术，该技术可容纳丰富的具有不可分离目标的非凸、非结构化和随机优化任务功能。算法专为数据分布在大量多核计算节点、具有（可能）时变甚至随机链接的任意拓扑网络内的设置而设计。此类新算法通过以下方式解决当前（非并行和非分布式）凸化技术的缺点：（i）完全控制处理器/网络节点之间的计算/信令的并行度和分布，以及（ii）提供大量的凸近似、正则化项、步长规则和通信协议。所提倡的框架专为时变甚至随机网络拓扑而设计，还展示了分布式计算的另一个理想属性：对（随机）网络故障的弹性。

项目成果

期刊论文数量（19）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Asynchronous parallel algorithms for nonconvex optimization

非凸优化的异步并行算法

DOI：
10.1007/s10107-019-01408-w
发表时间：
2019-06
期刊：
Mathematical Programming
影响因子：
2.7
作者：
Cannelli, Loris;Facchinei, Francisco;Kungurtsev, Vyacheslav;Scutari, Gesualdo
通讯作者：
Scutari, Gesualdo

Feasible methods for nonconvex nonsmooth problems with applications in green communications

非凸非光滑问题的可行方法及其在绿色通信中的应用