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Supporting Big Data at the Vehicular Edge

机译:在车辆边缘支持大数据

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摘要

Vehicular networks are commonplace, and many applications have been developed to utilize their sensor and computing resources. This is a great utilization of these resources as long as they are mobile. The question to ask is whether these resources could be put to use when the vehicle is not mobile. If the vehicle is parked, the resources are simply dormant and waiting for use. If the vehicle has a connection to a larger computing infrastructure, then it can put its resources towards that infrastructure. With enough vehicles interconnected, there exists a computing environment that could handle many cloud-based application services. If these vehicles were electric, then they could in return receive electrical charging services.;This Thesis will develop a simple vehicle datacenter solution based upon Smart Vehicles in a parking lot. While previous work has developed similar models based upon the idea of migration of jobs due to residency of the vehicles, this model will assume that residency times cannot be predicted and therefore no migration is utilized. In order to offset the migration of jobs, a divide-and-conquer approach is created. This uses a MapReduce process to divide the job into numerous sub-jobs and process the subtask in parallel. Finally, a checkpoint will be used between the Map and Reduce phase to avoid loss of intermediate data. This will serve as a means to test the practicality of the model and create a baseline for comparison with future research.
机译:车辆网络是司空见惯的,并且已经开发了许多应用来利用它们的传感器和计算资源。只要这些资源是可移动的,这就是对它们的极大利用。要问的问题是,当车辆不移动时是否可以使用这些资源。如果车辆停放,资源将处于休眠状态并等待使用。如果车辆与更大的计算基础设施建立了连接,则可以将其资源投入该基础设施。随着足够多的车辆互连,存在一个可以处理许多基于云的应用程序服务的计算环境。如果这些车辆是电动的,那么它们可以反过来获得充电服务。;本文将基于停车场中的智能车辆开发一种简单的车辆数据中心解决方案。尽管先前的工作已经基于由于车辆的居住而导致的工作迁移的思想开发了类似的模型,但是该模型将假设无法预测居住时间,因此不进行迁移。为了抵消工作的迁移,创建了分而治之的方法。这使用MapReduce流程将作业分为多个子作业,并并行处理子任务。最后,在Map和Reduce阶段之间将使用一个检查点,以避免丢失中间数据。这将用作测试模型的实用性并为与未来研究进行比较创建基准的一种手段。

著录项

  • 作者

    Decker, Lloyd.;

  • 作者单位

    Old Dominion University.;

  • 授予单位 Old Dominion University.;
  • 学科 Computer science.
  • 学位 M.S.
  • 年度 2018
  • 页码 130 p.
  • 总页数 130
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 古生物学;
  • 关键词

  • 入库时间 2022-08-17 11:41:16

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