基于大数据深度神经网络与Agent的大规模任务处理方法

Large-scale Tasks Processing Method Based on Deep Neural Network and Agent

因大规模任务处理模型在处理实际任务请求通常是基于历史数据的,若总依据经验和以往知识判断,会出现许多无法识别并处理的任务,以及出现模型过拟合等问题。提出了一种基于深度神经网络的计算模型进行大规模任务部署,并引用Agent强化学习效用进行评价,实现最佳虚拟网络映射方案。实验结果表明,这种BDTard方法法能满足大规模任务请求,稳定系统长期收益,保障了大数据环境下大规模任务处理的高效执行。

Since the large-scale task processing model is usually based on historical data in the processing of actual task requests,if the model is always judged based on experience and previous knowledge,there will be many tasks that cannot be recognized and processed,as well as problems such as model overfitting.A computing model based on deep neural network is proposed for large-scale task deployment,and the Agent reinforcement learning utility is evaluated to realize the optimal virtual network mapping scheme.The experimental results show that the BDTard method can meet the requirements of large-scale task,stabilize the long-term benefits of the system,and ensure the efficient execution of large-scale task processing in the big data environment.