Nebula: Distributed Edge Cloud for Data Intensive Computing

Albert Jonathan; Mathew Ryden; Kwangsung Oh; Abhishek Chandra; Jon Weissman

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Nebula: Distributed Edge Cloud for Data Intensive Computing

机译：星云：用于数据密集型计算的分布式边缘云

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Centralized cloud infrastructures have become the popular platforms for data-intensive computing today. However, they suffer from inefficient data mobility due to the centralization of cloud resources, and hence, are highly unsuited for geo-distributed data-intensive applications where the data may be spread at multiple geographical locations. In this paper, we present Nebula: a dispersed edge cloud infrastructure that explores the use of voluntary resources for both computation and data storage. We describe the lightweight Nebula architecture that enables distributed data-intensive computing through a number of optimization techniques including location-aware data and computation placement, replication, and recovery. We evaluate Nebula performance on an emulated volunteer platform that spans over 50 PlanetLab nodes distributed across Europe, and show how a common data-intensive computing framework, MapReduce, can be easily deployed and run on Nebula. We show Nebula MapReduce is robust to a wide array of failures and substantially outperforms other wide-area versions based on emulated existing systems.

机译：集中式云基础架构已成为当今数据密集型计算的流行平台。但是，由于云资源的集中化，它们的数据移动效率低下，因此非常不适合地理分布的数据密集型应用程序，在这些应用程序中数据可能分布在多个地理位置。在本文中，我们介绍了Nebula：一个分散的边缘云基础架构，该基础架构探索了将自愿性资源用于计算和数据存储的用途。我们描述了轻量级的Nebula体系结构，该体系结构通过许多优化技术来实现分布式数据密集型计算，这些技术包括位置感知数据以及计算放置，复制和恢复。我们在遍布欧洲的50多个PlanetLab节点的仿真志愿者平台上评估了Nebula的性能，并展示了如何轻松地在Nebula上轻松部署和运行通用的数据密集型计算框架MapReduce。我们展示了Nebula MapReduce对各种各样的故障具有鲁棒性，并且在性能上远远超过了基于模拟现有系统的其他广域网版本。

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《IEEE Transactions on Parallel and Distributed Systems》 |2017年第11期|3229-3242|共14页
作者
Albert Jonathan; Mathew Ryden; Kwangsung Oh; Abhishek Chandra; Jon Weissman;
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作者单位

University of Minnesota Twin Cities, Computer Science & Engineering, Minneapolis, MN;

University of Minnesota Twin Cities, Computer Science & Engineering, Minneapolis, MN;

University of Minnesota Twin Cities, Computer Science & Engineering, Minneapolis, MN;

University of Minnesota Twin Cities, Computer Science & Engineering, Minneapolis, MN;

University of Minnesota Twin Cities, Computer Science & Engineering, Minneapolis, MN;

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原文格式 PDF
正文语种 eng
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关键词
Cloud computing; Distributed databases; Bandwidth; Computational modeling; Memory; Data models; Wide area networks;

机译：云计算;分布式数据库;带宽;计算建模;内存;数据模型;广域网;

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1. DCCP: an effective data placement strategy for data-intensive computations in distributed cloud computing systems [J] . Wang Tao, Yao Shihong, Xu Zhengquan, Journal of supercomputing . 2016,第7期

机译：DCCP：一种有效的数据放置策略，用于分布式云计算系统中的数据密集型计算
2. Data-intensive application scheduling on Mobile Edge Cloud Computing [J] . Alkhalaileh Mohammad, Calheiros Rodrigo N., Quang Vinh Nguyen, Journal of network and computer applications . 2020,第Octa期

机译：移动边缘云计算上的数据密集型应用计划
3. Neighborhood search-based job scheduling for IoT big data real-time processing in distributed edge-cloud computing environment [J] . Li Chunlin, Zhang YiHan, Luo Youlong Journal of supercomputing . 2021,第2期

机译：基于邻域搜索的JOB调度，用于分布式边缘云计算环境中的IOT大数据实时处理
4. Nebula: Distributed Edge Cloud for Data Intensive Computing [C] . Ryden Mathew, Oh Kwangsung, Chandra Abhishek, IEEE International Conference on Cloud Engineering . 2014

机译：星云：用于数据密集型计算的分布式边缘云
5. Implementation of a Distributed Middleware Framework to Span the Edge - Fog - Cloud Tiers of a Mobile Distributed Computing IoE Platform Requiring Real-Time and Near-Real-Time Response [D] . Seal, Arindrajit. 2020

机译：实现分布式中间件框架，用于跨越移动分布式计算IOE平台的边缘 - 云层，需要实时和近实时响应
6. Heart beats in the cloud: distributed analysis of electrophysiological ‘Big Data’ using cloud computing for epilepsy clinical research [O] . Satya S Sahoo, Catherine Jayapandian, Gaurav Garg, 2014

机译：云中的心跳：使用云计算进行癫痫临床研究的电生理学大数据的分布式分析
7. Location, Location, Location: Data-Intensive Distributed Computing in the Cloud [O] . Luckeneder, Michael, Barker, Adam 2014

机译：位置，位置，位置：数据密集型分布式计算云端

Nebula: Distributed Edge Cloud for Data Intensive Computing

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