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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.

在我们的数据泛滥时代，由无处不在的传感器不断收集的海量信息通过分布式计算体系结构进行通信和处理。为了解决新出现的大数据计算问题，该项目开始了一项雄心勃勃的多学科研究工作，旨在通过针对大规模分布式数据集的数据分析的通用算法框架，推动最先进的网络内/分布式大数据处理。拟议的算法框架支持对各种计算架构上的各种异类数据集进行完全分布式和并行的大数据分析。发展的研究方向也有利于远远超出大数据分析的领域，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网络。研究成果通过档案出版物、课程、本科生研究机会、教程和会议报告分发，所开发的方案依赖于一种新的凸化/分解技术，该技术适应具有不可分目标函数的丰富的非凸、非结构化和随机优化任务。算法是为这样的设置设计的：在具有(可能)时变甚至随机链路的任意拓扑的网络内，数据分布在大量多核计算节点上。这类新的算法通过(I)完全控制计算/信令在处理器/网络节点之间的并行度和分布，以及(Ii)通过提供过多的凸近似式、正则项、步长规则和通信协议来解决当前(非并行和非分布式)凸化技术的缺点。该框架专为时变甚至随机的网络拓扑而设计，它还展示了分布式计算的另一个理想属性：对(随机)网络故障的恢复能力。

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）

Distributed Saddle-Point Problems Under Similarity

DOI：
发表时间：
2021-07
期刊：
ArXiv
影响因子：
0
作者：
Aleksandr Beznosikov;G. Scutari;A. Rogozin;A. Gasnikov
通讯作者：
Aleksandr Beznosikov;G. Scutari;A. Rogozin;A. Gasnikov

Distributed nonconvex constrained optimization over time-varying digraphs

DOI：
10.1007/s10107-018-01357-w
发表时间：
2018-09
期刊：
Mathematical Programming
影响因子：
2.7
作者：
G. Scutari;Ying Sun
通讯作者：
G. Scutari;Ying Sun

Diminishing stepsize methods for nonconvex composite problems via ghost penalties: from the general to the convex regular constrained case

通过鬼罚减少非凸复合问题的步长方法：从一般到凸正则约束情况

DOI：
10.1080/10556788.2020.1854253
发表时间：
2020
期刊：
Optimization Methods and Software
影响因子：
2.2
作者：
Facchinei, Francisco;Kungurtsev, Vyacheskav;Lampariello, Lorenzo;Scutari, Gesualdo
通讯作者：
Scutari, Gesualdo

Asynchronous Optimization Over Graphs: Linear Convergence Under Error Bound Conditions

DOI：
10.1109/tac.2020.3033490
发表时间：
2020-10
期刊：
IEEE Transactions on Automatic Control
影响因子：
6.8
作者：
Loris Cannelli;F. Facchinei;G. Scutari;V. Kungurtsev
通讯作者：
Loris Cannelli;F. Facchinei;G. Scutari;V. Kungurtsev

A Unified Contraction Analysis of a Class of Distributed Algorithms for Composite Optimization

DOI：
10.1109/camsap45676.2019.9022451
发表时间：
2019-10
期刊：
2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
影响因子：
0
作者：
Jinming Xu;Ying Sun;Ye Tian;G. Scutari
通讯作者：
Jinming Xu;Ying Sun;Ye Tian;G. Scutari