• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computer Communications >A Cloud-RAN based end-to-end computation offloading in Mobile Edge Computing
【24h】

A Cloud-RAN based end-to-end computation offloading in Mobile Edge Computing

机译：基于云的端到端计算卸载在移动边缘计算中

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

Cloud Radio Access Network (C-RAN) and Mobile Edge Computing (MEC) have recently emerged as promising leading technologies for next generation mobile networks. Due to its low access latency, MEC is not only a convenient candidate for deployment of C-RAN, but it can also be served by Mobile Users (MUs) to offload their computation-intensive applications. This convergence can facilitate the utilization of knowledge acquired through inter-BBU information sharing to improve the quality of offloading decision. In this paper, we propose an end-to-end communication and computation offloading architecture which takes the full advantage of C-RAN to solve the MEC offloading problem with regard to both partitioning as well as sending and return RRH assignment problems. Based on the proposed architecture, we model the end-to-end offloading problem as an ILP with the objective of minimizing the cost of offloading considering the intra and inter cluster handover costs besides other factors. Due to the complexity of the end-to-end offloading problem, we propose a combination of utility functions and modified min-cut algorithms to solve the aforementioned problems in a timely manner. Simulation results demonstrate that the proposed approach outperforms significantly other alternatives in terms of execution time, energy consumption and aggregated cost under scenarios with different amounts of normalized throughput, invocation data and workload.
机译：云无线电接入网络（C-RAN）和移动边缘计算（MEC）最近被出现为下一代移动网络的有希望的领先技术。由于其低访问延迟，MEC不仅是部署C-RAN的方便候选者，而且它也可以由移动用户（MU）为卸载其计算密集型应用程序的服务。这种融合可以促进通过BBU间信息共享获取的知识，以提高卸载决策的质量。在本文中，我们提出了一种端到端的通信和计算卸载架构，该架构是C-RAN的完全优势，以解决两个分区的MEC卸载问题以及发送和返回RRH分配问题。基于所提出的架构，我们将端到端的卸载问题模拟为ILP，目的是考虑除了其他因素之外，考虑到帧内帧内切换成本卸载的成本。由于端到端卸载问题的复杂性，我们提出了实用功能和修改的最小切割算法的组合，以及时解决上述问题。仿真结果表明，在具有不同数量的规范化吞吐量，调用数据和工作量的情况下，所提出的方法在执行时间，能耗和聚合成本下表现出显着的其他替代方案。

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