计算机科学 ›› 2016, Vol. 43 ›› Issue (3): 44-48.doi: 10.11896/j.issn.1002-137X.2016.03.008

• 第十五届中国机器学习会议 • 上一篇    下一篇

云计算平台中面向车联网应用的能耗感知调度算法

邓聃婷,滕飞,杨燕   

  1. 西南交通大学信息科学与技术学院 成都611756,西南交通大学信息科学与技术学院 成都611756;计算机软件新技术国家重点实验室南京大学 南京210023,西南交通大学信息科学与技术学院 成都611756
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金资助

Energy-aware Scheduling Algorithm for Internet of Vehicles on Cloud Platform

DENG Dan-ting, TENG Fei and YANG Yan   

  • Online:2018-12-01 Published:2018-12-01

摘要: 针对面向车联网应用的云计算平台的高能耗问题,提出一种采用节能整合策略的能耗感知调度算法——任务集整合算法(Task Set Consolidation Algorithm)。该算法的主要思想是通过减少活跃物理服务器的数目,有效降低云平台的能量消耗。建立了云平台模型、车联网任务集模型和能耗模型,确定了云平台的节能目标函数和变量因子。仿真实验通过模拟多维资源多并发任务集的云平台环境,以物理服务器的活跃时间和活跃数目、云平台的能量消耗作为性能指标,将任务集整合算法与现有算法进行了比较。实验结果表明,TSC算法能够在避免任务集资源发生冲突的情况下,使面向车联网应用的云平台激活的物理服务器数量达到最少,能耗降到最低。

关键词: 车联网,云计算,能耗感知,节能整合

Abstract: For the issue of high energy consumption of the internet of vehicles on cloud platform,we put forward an energy-aware scheduling algorithm,called task set consolidation algorithm (TSC).The main idea of the algorithm is reducing the number of active physical servers to reach the goal of bringing the cloud platform’s energy consumption down.We built the objective function and the variable factors through the energy consumption model,the internet of vehicles task set model and cloud platform model.Our simulation experiment comparesd TSC with the existing algorithms on cloud platform environment,with the performance index of physical servers’ active time and number and the energy consumption of cloud platform.The experimental results show that TSC is able to minimize the number of activate physical servers on the cloud platform,as well as the energy consumption.

Key words: Internet of vehicles,Cloud computing,Energy-aware,Power-saving consolidation

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