Dynamic Adaptive Task Scheduling for Cloud Computing Platform Based on Deep Learning
Task scheduling in cloud computing environment is a hot research issue to optimize the quality of service of cloud applications.At present,industry and academia have proposed a variety of task scheduling strategies.Existing data-driven scheduling strategies rely on complex deep neural networks,which require high computing resources and incur higher execution costs,and are difficult to adapt to dy-namically changing diverse task types.To solve this problem,we propose a dynamic adaptive task scheduling strategy for cloud computing platform based on deep learning.Firstly,the task scheduling features were extracted from three aspects of pending tasks,available cloud resources and system running status.Then,a deep learning model was constructed to encode the features,and the execution cost and response time of the strategy were predicted through inference and decoding.Finally,the current optimal task scheduling strategy was selected from the strategy set according to the scheduling profit,and the online optimization model of loss function was calculated.The experimental results show that the proposed strategy improves the execution cost,response time and energy consumption compared with the existing methods.
万方数据
维普
