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

• 批准号: 1718796
• 负责人: Konstantinos Slavakis
• 金额: $ 20万
• 依托单位: SUNY at Buffalo
• 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 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718796&HistoricalAwards=false
• 关键词: CIF; Small; Collaborative; Research; Distributed

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.

在我们这个数据泛滥的时代，由无处不在的传感器永久收集的大量信息通过分布式计算架构进行通信和处理。为了解决新出现的大数据计算问题，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集的数据分析通用算法框架来推进最先进的网络/分布式大数据处理。所提出的算法框架能够在广泛的计算架构上针对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利于大数据分析以外的领域，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、教程和会议演示分发。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非凸、非结构化和随机优化任务，目标函数不可分离。算法是为数据分布在大量多核计算节点上的设置而设计的，这些节点位于具有（可能）时变甚至随机链路的任意拓扑结构的网络内。这类新的算法通过以下方式解决了当前（非并行和非分布式）凸化技术的缺点：（i）完全控制处理器/网络节点之间的并行度和计算/信令的分布，以及（ii）通过提供过多的凸近似，正则化项，步长规则和通信协议。设计时变的，甚至随机的网络拓扑结构，所倡导的框架还展示了分布式计算的另一个可取的属性：弹性（随机）网络故障。

项目成果

Stochastic Composite Convex Minimization with Affine Constraints

• DOI: 10.1109/acssc.2018.8645298
• 发表时间: 2018-10
• 期刊: 2018 52nd Asilomar Conference on Signals, Systems, and Computers
• 作者: K. Slavakis

Bi-Linear Modeling of Data Manifolds for Dynamic-MRI Recovery

• DOI: 10.1109/tmi.2019.2934125
• 发表时间: 2018-12
• 期刊: IEEE Transactions on Medical Imaging
• 影响因子: 10.6
• 作者: Gaurav N. Shetty;K. Slavakis;Abhishek Bose;Ukash Nakarmi;G. Scutari;L. Ying

Bi-Linear modeling of manifold-data geometry for Dynamic-MRI recovery

• DOI: 10.1109/camsap.2017.8313115
• 发表时间: 2017-12
• 期刊: 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
• 作者: K. Slavakis;Gaurav N. Shetty;Abhishek Bose;Ukash Nakarmi;L. Ying

Robust Hierarchical-Optimization RLS Against Sparse Outliers

• DOI: 10.1109/lsp.2019.2963188
• 发表时间: 2019-10
• 期刊: IEEE Signal Processing Letters
• 影响因子: 3.9
• 作者: K. Slavakis;Sinjini Banerjee