• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>ACM Computing Surveys >Computation Offloading and Retrieval for Vehicular Edge Computing:Algorithms, Models, and Classification
【24h】

Computation Offloading and Retrieval for Vehicular Edge Computing:Algorithms, Models, and Classification

机译：用于车辆边缘计算的计算卸载和检索：算法，模型和分类

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The rapid evolution of mobile devices, their applications, and the amount of data generated by them causes a significant increase in bandwidth consumption and congestions in the network core. Edge Computing offers a solution to these performance drawbacks by extending the cloud paradigm to the edge of the network using capable nodes of processing compute-intensive tasks. In the recent years, vehicular edge computing has emerged for supporting mobile applications. Such paradigm relies on vehicles as edge node devices for providing storage, computation, and bandwidth resources for resource-constrained mobile applications. In this article, we study the challenges of computation offloading for vehicular edge computing. We propose a new classification for the better understanding of the literature designing vehicular edge computing. We propose a taxonomy to classify partitioning solutions in filter-based and automatic techniques; scheduling is separated in adaptive, social-based, and deadline-sensitive methods, and finally data retrieval is organized in secure, distance, mobility prediction, and social-based procedures. By reviewing and analyzing literature, we found that vehicular edge computing is feasible and a viable option to address the increasing volume of data traffic. Moreover, we discuss the open challenges and future directions that must be addressed towards efficient and effective computation offloading and retrieval from mobile users to vehicular edge computing.
机译：移动设备，应用程序和由它们产生的数据量的快速演变导致网络核心带宽消耗和拥塞的显着增加。边缘计算通过使用能够的处理计算密集型任务将云范例扩展到网络的边缘来提供对这些性能缺点的解决方案。近年来，出现了支持移动应用的车辆边缘计算。这种范例依赖于车辆作为边缘节点设备，用于提供资源受限移动应用程序的存储，计算和带宽资源。在本文中，我们研究了用于车辆边缘计算的计算卸载挑战。我们提出了一种新的分类，以便更好地理解设计车辆边缘计算。我们提出了一个分类法，以基于滤波器和自动技术进行分类的分区解决方案;调度在自适应，社交和截止日期敏感方法中分开，最后在安全，距离，移动预测和社交程序中组织了数据检索。通过审查和分析文献，我们发现车辆边缘计算是可行的和可行的选择来解决增加的数据流量量。此外，我们讨论了必须以高效且有效的计算卸载和从移动用户检索到车辆边缘计算的开放挑战和未来方向。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号