期刊
IEEE TRANSACTIONS ON MOBILE COMPUTING
卷 22, 期 4, 页码 2147-2162出版社
IEEE COMPUTER SOC
DOI: 10.1109/TMC.2021.3119200
关键词
Mobile edge computing; task offloading; dependent tasks; multiple mobile applications
In this paper, a dependent task offloading framework (COFE) is proposed, which allows mobile devices to offload compute-intensive tasks with dependent constraints to the MEC-Cloud system to improve user experience. The task offloading problem is formulated as an average makespan minimization problem, and a heuristic ranking-based algorithm is proposed to assign the offloaded tasks. Theoretical analysis proves the stability of the system under the proposed algorithm, and extensive simulations validate its effectiveness in reducing average makespan and deadline violation probabilities.
With the proliferation of versatile mobile applications, offloading compute-intensive tasks to the MEC/Cloud becomes a dramatic technique due to the limited resources and high user experience requirements at mobile devices. However, most existing works design their task offloading schemes without considering the dependence of tasks and the orchestration of the MEC and Cloud, and thus may limit the system performance. In this paper, we propose a dependent task offloading framework for multiple mobile applications, named COFE, where mobile devices can offload their compute-intensive tasks with dependent constraints to the MEC-Cloud system. It can assign the offloaded tasks to the MEC and Cloud adaptively to improve the user experience. Based on COFE, we formulate the task offloading problem as an average makespan minimization problem, which is proved to be NP-hard. Then, we propose a heuristic ranking-based algorithm to assign the offloaded tasks according to their bottom levels. Theoretical analysis proves the stability of the system under the proposed algorithm and extensive simulations validate that the proposed algorithm can significantly reduce the average makespan and deadline violation probabilities of offloaded applications.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据