Adaptive Resource Scheduling based on Neural Network and Mobile Traffic Prediction

Plamen T. Semov, P. Koleva, V. Poulkov
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引用次数: 3

Abstract

Nowadays with the deployment of a large and dense heterogeneous networks more sophisticated algorithms for resource scheduling are needed. Implementing hard coded scheduling algorithms without taking into account the very specific dynamic of the traffic generated by the mobile users can lead to a network performance quite far from the optimal. By using novel machine learning (ML) algorithms we can store not only the raw traffic data and its variations but also build the so-called heat maps, reflecting the changes of the traffic over time, space and per user. Using neural network (NN) architectures, trained by the raw data statistics, we can store the network traffic model at minimum data storage without the need of keeping and looking up at the raw data. Using such NN architecture the network state in next time intervals could be predicted and this prediction used for decision making about how the network resources to be scheduled among the active mobile users. To implement adaptive resource scheduling named “AdaptSch” a neural network architecture with two main blocks is proposed. The simulation results show that by incorporating a neural classifier for adapting the resource scheduler we can utilize the advantages and the effectiveness of multiple scheduler algorithms and improve overall throughput and packet delay.
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基于神经网络和移动流量预测的自适应资源调度
随着大型、密集异构网络的部署,需要更复杂的资源调度算法。实现硬编码调度算法而不考虑移动用户产生的流量的非常具体的动态,可能导致网络性能与最佳性能相去甚远。通过使用新颖的机器学习(ML)算法,我们不仅可以存储原始流量数据及其变化,还可以构建所谓的热图,反映流量随时间、空间和每个用户的变化。利用经过原始数据统计训练的神经网络(NN)架构,我们可以在不需要保存和查找原始数据的情况下,以最小的数据存储网络流量模型。利用这种神经网络结构,可以预测下一个时间间隔内的网络状态,并将此预测用于如何在活动移动用户之间调度网络资源的决策。为了实现自适应资源调度,提出了一种由两个主要模块组成的神经网络结构。仿真结果表明,通过引入神经分类器来适应资源调度,可以利用多种调度算法的优点和有效性,提高整体吞吐量和数据包延迟。
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