论文标题
部分可观测时空混沌系统的无模型预测
A Multi-User Effective Computation Offloading Mechanism for MEC System: Batched Multi-Armed Bandits Approach
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
With the development of 5G technology, mobile edge computing (MEC) is becoming a useful architecture, which is envisioned as a cloud computing extension version. Users within MEC system could deal with data processing at edge terminals, which can reduce time for communication or data transmission. Multi-armed bandits (MAB) algorithms are powerful tools helping users offloading tasks to their best servers in MEC. However, as the number of users and tasks growing, the frequency of selecting servers and the cost of making decision is growing rapidly under traditional MAB algorithms. Inspired by this, in this paper, we propose a Batch-based Multi-user Server Elimination (BMSE) algorithm to solve such problem, which includes two sub-algorithms. We firstly propose a sub-algorithm in user level (BMSE-UL) to reduce the time cost. In BMSE-UL, users can simplify its own available server groups and offload tasks collectively. Then another sub-algorithm in system level (BMSE-SL) is proposed to reduce the frequency of making decision. In BMSE-SL, the system can cut down all the suboptimal task offloading actions and make the choosing option unique. Furthermore, we establish the optimality of the proposed algorithms by proving the sub-linearity convergence of their regrets and demonstrate the effectiveness of BMSE by extensive experiments.
