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Optimized Task Offloading Policy in Given Sequence in Mobile Edge Computing

机译:在移动边缘计算中的给定序列中的优化任务卸载策略

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In the era of big data, tasks with complex computation emerge in large numbers. It is hard to solve them properly when the tasks are in a given sequence. So we should find a way to utilize the local CPU and the edge server on the premise of keeping the task order. We consider a task sequence in a user device and a base station that can receive offloaded task to solve. We find out the optimized offloading policy by cutting the task sequence directed acyclic graph (DAG) into basic units. The power and frequency allocated to every task can be also gained at the same time. Then we can perfectly know how to deal with the task chain. We choose to solve the original problem by decomposing it into sub-problems according with different scenarios. We show that our algorithm achieves the least consumed time among the compared algorithms. Simulations are performed to verify the proposed algorithm.
机译:在大数据的时代,具有复杂计算的任务大量出现。当任务处于给定序列时,很难正确解决它们。因此,我们应该找到一种方法来在保持任务订单的前提下使用本地CPU和EDGE服务器。我们考虑用户设备中的任务序列和可以接收卸载任务以解决的基站。我们通过将任务序列指向非循环图(DAG)切割成基本单位来找到优化的卸载策略。分配给每个任务的电源和频率也可以同时获得。然后我们可以完全了解如何处理任务链。我们选择根据不同场景将其分解为子问题来解决原始问题。我们表明,我们的算法在比较算法之间实现了最少的消耗时间。执行仿真以验证所提出的算法。

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