计算机科学
边缘计算
移动边缘计算
分布式计算
云计算
服务器
服务质量
云朵
负载平衡(电力)
计算机网络
高效能源利用
延迟(音频)
工程类
操作系统
电信
电气工程
数学
网格
几何学
作者
Wei Duan,Xiaohui Gu,Miaowen Wen,Yancheng Ji,Jianhua Ge,Guoan Zhang
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2021-09-29
卷期号:23 (7): 9797-9808
被引量:3
标识
DOI:10.1109/tits.2021.3114957
摘要
To overcome the inherent defect of centralized data processing in cloud computing, the mobile edge computing (MEC) brings data storage and computing capacities, to the edge closer to end users. However, the uneven distribution of access vehicles, as well the volume of computing data, cause the workload diversity among various mobile edge computing servers (MECSs). In this paper, we propose a hierarchical model with quality of service (QoS)-aware and power-aware resource management for the cooperative edge-computing-based intelligent vehicular network (CEC-IoV), and the system latency and energy efficiency at MECSs are respectively optimized. Specifically, considering the changing response times versus MECSs’ workloads, the Minimum Latency with Migration Loads (MLML) scheme is developed for workload balance among multiple MECSs. By selecting the appropriate response time threshold and migration loads from overloading MECSs to idle MECSs simultaneously, the load-balancing problem can be efficiently solved for multiple MECSs with unbalanced workloads. On the other hand, through performing workload redistribution and dynamic reconfiguration of virtual machines (VMs) instantiated onto the parallel computing platform at one MECS, the energy-efficiency can be also optimized while guaranteeing the QoS requirement on the processing delay. With the latency constraint, the power minimization problem is formulated to be a convex one, and the semi-closed forms for optimal solutions of VMs’ workloads and processing rates are provided using KKT conditions. Compared with the performance obtained by benchmark schemes, numerical results exhibit that our resource management schemes gain lower system latency and higher energy efficiency.
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