邓晓衡

教授 博士生导师 硕士生导师

入职时间:2006-01-05

所在单位:电子信息学院

职务:院长

学历:博士研究生毕业

性别:男

联系方式:Email:dxh@csu.edu.cn

学位:博士学位

在职信息:在职

主要任职:湖南省数据传感与交换设备工程中心 主任 IEEE RS Chapter长沙 主席CCF普适计算专委 委员 CCF长沙 执委

毕业院校:中南大学

学科:信息与通信工程
计算机科学与技术

当前位置: 邓晓衡 >> 论文成果

E. Liu, X. Deng, Z. Cao, et al. Design and evaluation of a prediction-based dynamic edge computing system[C]//2018 IEEE Global Communications Conference (GLOBECOM). IEEE, 2018: 1-6.

发布时间:2024-03-13

点击次数:

发表刊物:2018 IEEE Global Communications Conference (GLOBECOM)

摘要:Abstract—We investigate a mobile edge computing environment where edge computing nodes provide their computation capacities to process the computation intensive tasks submitted by end users. We introduce a Cloudlet Assisted Cooperative Task Assignment (CACTA) system that organizes edge nodes that are geographically close to a user into a cluster to collaboratively work on the user’s tasks. The system enables a user to minimize his/her total cost which is a weighted combination of latency (i.e., the task’s completion time), and the costs incurred in working on the task. The total cost captures the tradeoff that the user would like to make between latency and computing related costs. It is challenging for the system to find an optimal strategy that assigns workload to edge nodes to meet the user’s optimization goal, due to the time-varying available capacities and the mobility of edge nodes. To address the challenge, we model the system as a discrete time system in which each edge node’s capacity and cost vary over different time slots, and the system assigns parts of the task to the edge nodes in the cluster over time. We introduce a prediction-based dynamic task assignment algorithm, referred to PA-OPT, that assigns workload to edge nodes in each time slot based on the prediction of their capacities/costs and an empirical optimal allocation strategy which is learned from an offline optimal solution from historical data. Then we apply our system design to a video data analysis application, and conduct extensive simulations driven by a Google cloud data trace. We have demonstrated that our proposed algorithm/system achieves significantly higher performance than several other algorithms, and especially its performance is very close to that of an offline optimal solution.

备注:http://faculty.csu.edu.cn/dengxiaoheng/zh_CN/lwcg/10445/content/49194.htm

是否译文:

附件:

  • 54-Design_and_Evaluation_of_a_Prediction-Based_Dynamic_Edge_Computing_System.pdf

  • 上一条: L. C, X. Deng, H. Shen, et al. Dycusboost: Adaboost-based imbalanced learning using dynamic clustering and undersampling[C]//2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech). IEEE, 2018: 208-215.

    下一条: Jie Luo, Xiaoheng Deng, Honggang Zhang, Huamei Qi. Ultra-Low Latency Service Provision in Edge Computing. ICC 2018:1-6