The massive amount of sensing and communication data that needs to be processed during the production process of complex heavy equipment generates heavy storage pressure on the cloud server-side, thus limiting the convergence of sensing, communication, and computing in intelligent factories. To solve the problem, based on machine learning techniques, a storage optimization model is proposed in this paper for reducing the storage pressure on the cloud server and enhancing the coupling between communication and sensing data. At first, based on the operation rules of the distributed file system on the cloud server, the proposed model screens and organizes the system logs. With the filtered logs, the model sets feature labels, constructs feature vectors, and builds sample sets. Then, based on the ID3 decision tree, a file elimination model is trained to analyze the files stored in the cloud server and predict their reusability. In practice, the proposed model is applied in the Hadoop Distributed File System and helps the system delete underutilized and low-value files and save storage space. Experiments show that the proposed model can effectively reduce the storage load on the cloud server and improve the integration efficiency of multisource heterogeneous data during complex heavy equipment production.
Elasticity capability is one of the most important capabilities of cloud computing, which combines large-scale resource allocation capability to quickly achieve minute-level resource demand provisioning to meet the elasticity requirements of different scale scenarios. The elasticity capability is mainly determined by the container start-up speed and container scaling strategy together, where the container scaling strategy contains both vertical container scaling strategy and horizontal container scaling strategy. In order to make the container scaling policy more effective and improve the application service quality and resource utilization, we briefly introduce Kubernetes’ horizontal pod autoscaling (HPA) strategy, analyze the existing problem of HPA, and develop a container scaling strategy based on reinforcement learning. First, we analyze the problems of Kubernetes’ existing HPA container autoscaling strategy in the scale-up and scale-down phases, respectively. Second, the Markov decision model is used to model the container scaling problem. Then, we propose a model-based reinforcement learning algorithm to solve the container scaling problem. Finally, we compare the experimental results of the HPA scaling strategy and the model-based reinforcement learning strategy with the results from the resource utilization of the application, the change of the number of pods, and the application response time; through the experimental analysis, we verify that the reinforcement learning-based container scaling strategy can guarantee the application service quality and improve the utilization of the application resources more effectively than the HPA strategy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.