Slurm is a resource management and job scheduling system widely used in the field of parallel computing. Kubernetes is an open source container orchestration platform widely used in cloud native and AI fields. With the development of technologies such as parallel computing, AI, and large-scale data processing, the demand for computing resources in business scenarios has become more complex and diverse. In order to better adapt to business needs and give full play to the advantages of both in different fields, this paper proposes a fusion scheduling solution based on Slurm and Kubernetes. The solution is mainly aimed at two scenarios: partitioned deployment and hybrid deployment. The fusion scheduling between the two is realized by developing the heterogeneous resource manager Unify. Dynamic node management function is provided for partitioned deployment, and unified resource view and unified scheduling function are provided for hybrid deployment. Application results show that the solution can effectively solve the problem of dynamic node division under partitioned deployment and the resource scheduling conflict problem of Slurm and Kubernetes under hybrid deployment. Through this solution, both can be applied to more complex demand scenarios and improve the overall resource utilization of the cluster.
Cloud-native virtualization technology combines virtualization technology with cloud-native computing to provide a more efficient, flexible, and scalable cloud computing environment. In the process of analysis and research in the field of bioinformatics, it is usually necessary to deal with large-scale data sets and complex computing tasks, and the demand for computing power throughout the research and development cycle is characterized by peaks and troughs. The elastic scalability of cloud-native virtualization technology allows for the expansion of computing resources according to demand, meeting the data processing and analysis requirements throughout the entire research and development cycle. By integrating virtualized InfiniBand high-speed NICs, data transfer and the execution of computational tasks are accelerated, further reducing the research and development cycle. In summary, cloud-native virtualization technology has significant application value in the field of bioinformatics, providing an efficient computing environment while saving time and costs.
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