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Latency Aware Optimal Workload Assignment in Mobile Edge Cloud Offloading Network

机译:移动边缘云卸载网络中的延迟感知最佳工作负载分配

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Mobile Edge Cloud Architecture (MECA) facilitates mobile devices to perform computational intensive applications in a collaborative manner with cloud computing. The offloading system in MECA is a technique in which application can be divided into local execution and cloud execution. However, existing MECA does not stable due to a longer delay for interactive and real time applications which have to execute within a given deadline. Since, current offloading systems do not consider communication and cloud process delay, they have just focused on mobile execution time. In this paper, we are minimizing the hybrid delay (i.e., mobile process delay, communication delay, cloud process delay) in order to minimize average response time of application. To cope with the above challenges we have proposed Latency Aware Task Assignment Algorithm (LATA), which aim is to minimize the response time of interactive application. Simulation results show that our proposed LATA has reduced response time of application as compared to baseline approaches.
机译:移动边缘云架构(MECA)有助于移动设备与云计算以协作的方式执行计算密集型应用程序。 MECA中的卸载系统是一种可以将应用程序分为本地执行和云执行的技术。但是,由于必须在给定期限内执行的交互式和实时应用程序的延迟较长,因此现有的MECA不稳定。由于当前的卸载系统不考虑通信和云过程延迟,因此它们仅关注移动执行时间。在本文中,我们正在最小化混合延迟(即,移动进程延迟,通信延迟,云进程延迟),以最小化应用程序的平均响应时间。为了应对上述挑战,我们提出了延迟感知任务分配算法(LATA),其目的是最大程度地减少交互式应用程序的响应时间。仿真结果表明,与基线方法相比,我们提出的LATA缩短了应用程序的响应时间。

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