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.

在我们的数据驱散时代，通过分布式计算体系结构对普遍传感器的永久收集和处理的大量信息进行了处理。为了解决新兴的大数据计算问题，该项目着手进行雄心勃勃的多学科研究工作，旨在通过一般算法框架来推进最先进的网络内部/分布式大数据处理，用于大量分布式数据集。所提出的算法框架可以在广泛的计算体系结构上为各种异质数据集提供完全分布和并行的大数据分析。开发的研究方向也对远远超出大数据分析的领域也有益，例如信号处理，机器学习，下一代无线通信，智能基础和智能网格网络。研究结果是通过档案出版物，课程，本科研究机会，教程和会议演讲分发的。开发的计划依赖于一种新颖的凸化/分解技术，该技术可容纳丰富的非结构化，非结构化和随机优化任务，并具有不可分离的目标功能。算法设计用于设置，其中数据分布在大量的多核计算节点上，在任意拓扑网络中具有（可能是时间变化甚至随机链接）。这种新的算法解决了通过（i）通过（i）完全控制处理器/网络节点之间计算/信号的分布的当前（非平行和非分布）凸化技术的缺点，以及（ii）通过提供凸面的convex近似概述，正则化项，台阶规则和通信协议，并提供convex近似值的plethora。该倡导框架专为时间变化甚至随机的网络拓扑设计，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）

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