Energy-Efficient Autonomic Offloading in Mobile Edge Computing
Energy-Efficient Autonomic Offloading in Mobile Edge Computing
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DOI：
10.1109/dasc-picom-datacom-cyberscitec.2017.104
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
Changqing Luo;Sergio Salinas;Ming Li;Pan Li
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文献类型：
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作者：
Changqing Luo;Sergio Salinas;Ming Li;Pan Li
移动设备的蓬勃发展和普及导致了各种移动应用程序的激增。许多移动应用程序（例如在线视频、游戏）本质上是计算密集型的，因此会快速耗尽移动设备的电池能量。为了解决这个问题，学术界和工业界提出了移动边缘计算（MEC），它可以使移动设备自动将计算卸载到位于蜂窝运营商无线接入网络内的边缘服务器。然而，耗能的无线通信会产生额外的能源消耗，这可能会抵消由于计算卸载而节省的能源。为此，我们综合考虑物理层设计和应用程序运行延迟，设计了一种节能的自主卸载方案。具体来说，我们首先通过考虑同一应用程序的任务之间的交互所产生的能耗，对 MEC 环境中移动应用程序的能耗进行数学建模，这在以前的研究中很大程度上被忽略了。然后，我们根据任务交互矩阵识别任务执行流，并将任务流延迟的最大值表示为应用程序的延迟。最后，我们提出了一个节能卸载问题（通常是 NP 难问题），并开发了一种有效的启发式方法来解决该问题。我们提供了大量的模拟结果来表明，与之前的方案相比，我们提出的方案可以实现能耗的显着降低（高达 20% 左右）。
The booming growth and popularity of mobile devices have led to the surge of various mobile applications. Many mobile applications, such as online vedio, gaming, are essentially computation-intensive, and hence can quickly deplete mobile devices' battery energy. To address this issue, academia and industry have proposed mobile edge computing (MEC) that can enable mobile devices to automatically offload computations to the edge servers located within the radio access networks of cellular operators. However, energy-hungry wireless communications incur extra energy consumption that may offset the energy saving due to computation offloading. To this end, we design an energy-efficient autonomic offloading scheme by jointly considering the physical layer design and application running latency. Specifically, we first mathematically model the energy consumption of a mobile application in MEC environment by taking into account the energy consumption incurred by the interactions among the tasks for the same application, which is largely ignored by previous studies. Then, we identify task execution flows based on a task interaction matrix, and formulate the maximum of the task flow's latencies as the application's latency. Finally, we formulate an energy-efficient offloading problem, which is generally NP-hard, and develop an efficient heuristic method to solve the problem. We present extensive simulation results to show that our proposed scheme can achieve significant reduction (up to 20% around) in energy consumption compared with previous schemes.