This article explores the use of Kubernetes (K8s) clusters for deploying AI workloads, focusing on multi-tenancy strategies. It delves into the benefits of K8s, the different types of multi-tenancy (team and customer), and the critical considerations for implementing secure and efficient solutions.
Organizations are increasingly deploying AI workloads on Kubernetes (K8s) clusters to harness the benefits of containerization and orchestration. K8s is a portable, extensible, open-source platform for managing containerized workloads and services. It provides building blocks for building developer platforms. With automatic bin packing, K8s uses a cluster of nodes to run containerized tasks.
By setting how much CPU and memory each container needs, K8s can fit containers onto nodes to make the best use of resources. Service discovery and load balancing of container traffic to containers ensure that deployment is stable. The term 'multi-tenancy' has been used to describe the sharing of clusters where different applications or multiple instances of an application run in the same cluster. Multi-tenancy in K8s clusters falls into variations and hybrids of two broad categories – multi-team and multi-customer. A cluster is commonly shared between multiple teams within an organization, each of whom may operate one or more workloads. While team members have access to K8s resources, K8s policies such as role-based access control (RBAC), quotas, and network policies are essential to safely and fairly share clusters. In a multi-customer tenancy scenario, a software-as-a-service (SaaS) vendor runs multiple instances of a workload for customers with K8s policies, isolating the workloads. Here, customers do not have access to the cluster. The vendor uses K8s to manage the workloads and optimize costs. Many GPU-as-a-Service (GPUaaS) or AI factory operators deploy multiple K8s clusters for multi-customer tenancy. Key considerations in designing multi-tenant solutions with K8s include the isolation level, implementation effort, operational complexity, and service cost. Isolating the workloads of different tenants from each other to prevent unauthorized access is critical for both the control plane and the data plane in the K8s cluster
KUBERNETES AI MULTITENCY CONTAINERIZATION CLOUD COMPUTING
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