FRuIT: The Federated RaspberryPi Micro-Infrastructure Testbed

FRuIT:联合 RaspberryPi 微基础设施测试床

基本信息

  • 批准号:
    EP/P004024/1
  • 负责人:
  • 金额:
    $ 115.89万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Research Grant
  • 财政年份:
    2017
  • 资助国家:
    英国
  • 起止时间:
    2017 至 无数据
  • 项目状态:
    已结题

项目摘要

The introduction of ubiquitous low-cost, low-power compute devices, e.g. in the Internet of Things (IoT) is fundamentally changing the computational landscape. Although we already see the benefits provided by special purpose IoT devices, the true capability is only realised when we can re-purpose large numbers of distributed devices as part of a much larger federated service. This is captured in the FRUIT project hypothesis: "Massive aggregation of low-cost, low-power, commodity infrastructure can form an efficient and effective compute fabric for key distributed applications."The participating institutions and external collaborators will build a distributed UK-wide federated testbed of lightweight compute nodes capable of exploring the research issues that arise from such a dynamic infrastructure. This is in contrast to the existing centralised datacentre approach which occupies acres of land, cost millions of dollars; consumes megawatts of power and relies on a huge global network bandwidth. These trends are not sustainable and it is the emergence of low cost hardware such as the Raspberry Pi that will let us explore better solutions. We envisage a testbed comprising tens of thousands of nodes, geographically distributed and capable of challenging remote off-grid scenarios. In order to facilitate this we introduce the concept of a micro-datacentre, a physically small, low power and low cost compute cluster that can be pushed to the edge of distributed networks, fundamentally changing the current model of the centralised datacentre. We have already built prototype micro-data centres at multiple sites , for example a mini-HPC cluster (Southampton University), a scale model cloud datacentre (Glasgow and Liverpool John Moores University ),and a decentralized sensing and communication platform for ultra-remote internet blackspots (Cambridge University)This project is about connecting together our isolated micro-datacenters to produce a federated, geo-distributed testbed. We currently have hundreds of individual nodes and intend to grow this to tens of thousands across the UK academic network. The key challenge in this expansion will be the management of the underlying infrastructure, particularly given complexities in the networking fabric. For example intermittent connectivity, firewalls, and low bandwidth connections. We will use lightweight management and orchestration software, combined with software-defined networking infrastructure to manage our federated testbed. Through this work, we aim to demonstrate how resource-constrained micro-data centres can be harnessed as generic platforms to run virtualised, network-wide services in a resource-efficient and still high-performing manner, and enable the 'as-a-Service' paradigm over low-cost low-power federated infrastructures.
无处不在的低成本、低功耗计算设备的引入,例如在物联网(IoT)中,正在从根本上改变计算格局。尽管我们已经看到了特殊用途的物联网设备提供的好处,但只有当我们能够将大量分布式设备作为更大的联合服务的一部分重新调整用途时,才能实现真正的功能。水果项目假说体现了这一点:“低成本、低功耗、大宗商品基础设施的大规模聚合可以为关键的分布式应用程序形成高效有效的计算结构。”参与机构和外部合作者将在全英国范围内构建一个分布式的轻量级计算节点联合试验台,能够探索由这种动态基础设施引发的研究问题。这与现有的集中式数据中心方法形成了鲜明对比,现有的集中式数据中心方法占用大量土地,耗资数百万美元,消耗兆瓦的电力,并依赖于巨大的全球网络带宽。这些趋势是不可持续的,正是低成本硬件的出现,如树莓PI,将让我们探索更好的解决方案。我们设想了一个由数以万计的节点组成的试验台,这些节点分布在地理上,能够挑战远程离网场景。为了促进这一点,我们引入了微数据中心的概念,这是一个物理上很小的、低功耗和低成本的计算集群,可以被推到分布式网络的边缘,从根本上改变当前集中式数据中心的模式。我们已经在多个地点建立了微型数据中心原型,例如微型高性能计算集群(南安普顿大学)、比例尺模型云数据中心(格拉斯哥和利物浦约翰摩尔斯大学),以及用于超远程互联网黑点的分散式传感和通信平台(剑桥大学)该项目旨在将我们孤立的微型数据中心连接在一起,以生产一个联合的、地理上分布的试验床。我们目前有数百个单独的节点,并打算在整个英国学术网络中将这一数字增加到数万个。这种扩张的关键挑战将是底层基础设施的管理,特别是考虑到网络交换矩阵的复杂性。例如,间歇性连接、防火墙和低带宽连接。我们将使用轻量级管理和协调软件,并结合软件定义的网络基础设施来管理我们的联合试验台。通过这项工作,我们的目标是展示如何利用资源受限的微型数据中心作为通用平台,以资源高效且仍然高性能的方式运行虚拟的、网络范围的服务,并在低成本、低功耗的联合基础设施上实现“即服务”模式。

项目成果

期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Performance analysis of single board computer clusters
On the Optimality of Task Offloading in Mobile Edge Computing Environments
Resource-aware placement of softwarised security services in cloud data centers
云数据中心中软件化安全服务的资源感知部署
  • DOI:
    10.23919/cnsm.2017.8255975
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Ali A
  • 通讯作者:
    Ali A
On the Optimality of Virtualized Security Function Placement in Multi-Tenant Data Centers
多租户数据中心虚拟化安全功能配置的优化
  • DOI:
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Ali, A. F.
  • 通讯作者:
    Ali, A. F.
Evaluation of RPL's Single Metric Objective Functions
RPL 单一度量目标函数的评估
  • DOI:
    10.1109/ithings-greencom-cpscom-smartdata.2017.98
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Alayed W
  • 通讯作者:
    Alayed W
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Jeremy Singer其他文献

A Collaborative Problem-Solving Approach to Improving District Attendance Policy
改进学区出勤政策的协作解决问题方法
  • DOI:
    10.1177/0895904820974402
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    1.8
  • 作者:
    S. Lenhoff;Erica B. Edwards;Joi Claiborne;Jeremy Singer;K. French
  • 通讯作者:
    K. French
Capable VMs Project Overview (Poster Abstract)
Capable VMs 项目概述(海报摘要)
COVID-19, Online Learning, and Absenteeism in Detroit
底特律的 COVID-19、在线学习和缺勤情况
ChatGPT, Make a Secure Malloc for me
ChatGPT,为我创建一个安全的 Malloc
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Jeremy Singer;Zheng Wang
  • 通讯作者:
    Zheng Wang
Explorer Task Variant Allocation in Distributed Robotics
分布式机器人中的探索者任务变体分配
  • DOI:
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    0
  • 作者:
    José Cano;D. White;Alejandro Bordallo;Ciaran McCreesh;P. Prosser;Jeremy Singer;Vijay Nagarajan
  • 通讯作者:
    Vijay Nagarajan

Jeremy Singer的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Jeremy Singer', 18)}}的其他基金

M4Secure: Making Memory Management More Secure
M4Secure:让内存管理更安全
  • 批准号:
    EP/X037525/1
  • 财政年份:
    2023
  • 资助金额:
    $ 115.89万
  • 项目类别:
    Research Grant
Capabilities for Coders
编码员的能力
  • 批准号:
    EP/X015831/1
  • 财政年份:
    2022
  • 资助金额:
    $ 115.89万
  • 项目类别:
    Research Grant
Capable VMs
有能力的虚拟机
  • 批准号:
    EP/V000349/1
  • 财政年份:
    2020
  • 资助金额:
    $ 115.89万
  • 项目类别:
    Research Grant
Manycore Research Innovation and Opportunities Network (MaRIONet)
众核研究创新和机会网络 (MaRIONet)
  • 批准号:
    EP/P006434/1
  • 财政年份:
    2016
  • 资助金额:
    $ 115.89万
  • 项目类别:
    Research Grant
AnyScale Applications
AnyScale应用程序
  • 批准号:
    EP/L000725/1
  • 财政年份:
    2013
  • 资助金额:
    $ 115.89万
  • 项目类别:
    Research Grant

相似海外基金

Collaborative Research: OAC CORE: Federated-Learning-Driven Traffic Event Management for Intelligent Transportation Systems
合作研究:OAC CORE:智能交通系统的联邦学习驱动的交通事件管理
  • 批准号:
    2414474
  • 财政年份:
    2024
  • 资助金额:
    $ 115.89万
  • 项目类别:
    Standard Grant
Collaborative Research: IRES Track I: Wireless Federated Fog Computing for Remote Industry 4.0 Applications
合作研究:IRES Track I:用于远程工业 4.0 应用的无线联合雾计算
  • 批准号:
    2417064
  • 财政年份:
    2024
  • 资助金额:
    $ 115.89万
  • 项目类别:
    Standard Grant
CICI: TCR: Transitioning Differentially Private Federated Learning to Enable Collaborative, Intelligent, Fair Skin Disease Diagnostics on Medical Imaging Cyberinfrastructure
CICI:TCR:转变差异化私有联合学习,以实现医学影像网络基础设施上的协作、智能、公平的皮肤病诊断
  • 批准号:
    2319742
  • 财政年份:
    2024
  • 资助金额:
    $ 115.89万
  • 项目类别:
    Standard Grant
Efficient Federated Learning for Deep Learning Through Structured Training
通过结构化训练实现深度学习的高效联邦学习
  • 批准号:
    24K20845
  • 财政年份:
    2024
  • 资助金额:
    $ 115.89万
  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
Towards an Explainable, Efficient, and Reliable Federated Learning Framework: A Solution for Data Heterogeneity
迈向可解释、高效、可靠的联邦学习框架:数据异构性的解决方案
  • 批准号:
    24K20848
  • 财政年份:
    2024
  • 资助金额:
    $ 115.89万
  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
CRII: CSR: Adaptive Federated Continuous Learning on Heterogeneous Edge Devices with Unlabeled Data
CRII:CSR:具有未标记数据的异构边缘设备的自适应联合连续学习
  • 批准号:
    2348279
  • 财政年份:
    2024
  • 资助金额:
    $ 115.89万
  • 项目类别:
    Standard Grant
CRII: CNS: IoT-aware Federated On-Device Intelligence
CRII:CNS:物联网感知的联合设备上智能
  • 批准号:
    2418308
  • 财政年份:
    2024
  • 资助金额:
    $ 115.89万
  • 项目类别:
    Standard Grant
CPS: Medium: Federated Learning for Predicting Electricity Consumption with Mixed Global/Local Models
CPS:中:使用混合全局/本地模型预测电力消耗的联合学习
  • 批准号:
    2317079
  • 财政年份:
    2024
  • 资助金额:
    $ 115.89万
  • 项目类别:
    Standard Grant
Federated Omniverse Facilities for Smart Digital Futures
智能数字未来的联合全宇宙设施
  • 批准号:
    LE240100131
  • 财政年份:
    2024
  • 资助金额:
    $ 115.89万
  • 项目类别:
    Linkage Infrastructure, Equipment and Facilities
Federated Reinforcement Learning Empowered Point Cloud Video Streaming
联合强化学习赋能点云视频流
  • 批准号:
    24K14927
  • 财政年份:
    2024
  • 资助金额:
    $ 115.89万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了