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Dynamic Task Caching and Computation Offloading for Mobile Edge Computing

机译:移动边缘计算的动态任务缓存和计算卸载

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Mobile edge computing (MEC) provides information technology and cloud-computing capabilities within the network edge close to mobile users, thereby addressing computing demand for users. However, the energy consumption of data uploading from users to the MEC server makes it hard to meet users' demand in some specific applications, i.e., interactive gaming and augmented reality. Motivated by this, we integrate a task caching mechanism into computation offloading technique. Specifically, it allows the MEC server to proactively cache some tasks and users to offload their tasks to the MEC server. Since the limited storage capacity and task demands for users are changing dynamically, which tasks are cached has to be judiciously decided to maximize the MEC system performance. The objective of this paper is to minimize the system cost, which is defined as the average total user energy consumption of all time slots. By formulating the problem as an integer-programming, we propose to find the optimal solution with two steps. Through which we have obtained the optimal online computation offloading and task cache update strategy. Simulation results show that in comparison with the other two baselines, the proposed scheme can effectively reduce the system cost.
机译:移动边缘计算(MEC)在靠近移动用户的网络边缘内提供信息技术和云计算能力,从而寻址对用户的计算需求。然而,从用户上传到MEC服务器的数据的能量消耗使得难以满足用户在某些特定应用程序中的需求,即交互式游戏和增强现实。通过此激励,我们将任务缓存机制集成到计算卸载技术中。具体来说,它允许MEC服务器主动缓存一些任务和用户以将其任务卸载到MEC服务器。由于对用户的有限存储容量和任务需求动态更改,因此缓存的任务必须明智地决定最大化MEC系统性能。本文的目的是最大限度地减少系统成本,其定义为所有时隙的平均用户能量消耗。通过将问题作为整数编程,我们建议使用两个步骤找到最佳解决方案。我们通过它获得了最佳的在线计算卸载和任务缓存更新策略。仿真结果表明,与其他两个基线相比,所提出的方案可以有效降低系统成本。

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