No full-text available
To read the full-text of this research,
you can request a copy directly from the author.
Kubernetes on Google Cloud Platform
Abstract
Google Cloud Platform is a public cloud computing platform that includes database services and infrastructure on which applications and websites may be hosted on managed virtual machines. This integrated PaaS/IaaS is a collection of services that may be categorized into Compute, Storage and Databases, Networking, Big Data, and Machine Learning, to list a few.
The rise of containerization has led to the development of container cloud technology, which offers container deployment and management services. However, scheduling a large number of containers efficiently remains a significant challenge for container cloud service platforms. Traditional load prediction methods and scheduling algorithms do not fully consider interdependencies between containers or fine-grained resource scheduling, leading to poor resource utilization and scheduling efficiency. To address these challenges, this paper proposes a new load prediction model CNN-BiGRU-Attention and a container scheduling strategy based on load prediction. The prediction model CNN and BiGRU focus on the local features of load data and long sequence dependencies, respectively, as well as introduce the attention mechanism to make the model more easily capture the features of long distance dependencies in the sequence. A container scheduling strategy based on load prediction is also designed, which first uses the load prediction model to predict the load state and then generates a scheduling strategy based on the load prediction value to determine the change of the number of container replicas in a fine-grained manner based on the load prediction value in the next time window, while the established domain-based container selection method is employed to facilitate the coarse-grained online migration of containers. Experiments conducted using public datasets and open-source simulation platforms demonstrate that the proposed approach achieves a 37.4% improvement in container load prediction accuracy and a 21.7% improvement in container scheduling efficiency compared to traditional methods. These results highlight the effectiveness of the proposed approach in addressing the challenges faced by container cloud service platforms.
ResearchGate has not been able to resolve any references for this publication.