GPU Cloud Providers Should Adopt Virtual Clusters for Kubernetes Multi-Tenancy
As cloud computing enters the AI era, cost management and resource utilization are crucial considerations for cloud providers. The volume of generative AI and machine learning (ML) workloads has exploded, with Bloomberg Intelligence reporting that the generative AI market is set to grow from $40 billion in 2022 to $1.3 trillion by the end of the decade. Companies of all types are eager to deploy AI workloads for a vast range of use cases, and to do so typically requires utilizing GPU resources. GPUs provide the computational power necessary for complex data analytics and ML tasks, but they require significant management and come at a high cost. As demand continues to skyrocket to meet the explosion of AI workloads now in production, cloud providers must utilize GPU resources efficiently to limit excessive spending. Maximizing resources while cutting costs is a challenge, but GPU cloud providers using Kubernetes for container orchestration can achieve both goals by implementing a multi-tenant architecture with a novel framework — virtual Kubernetes clusters.
Why Multi-Tenancy?
Before turning to the benefits of virtual clusters, it is crucial to understand why a multi-tenant architecture is increasingly necessary for GPU cloud providers. Broadly, multi-tenant architectures facilitate resource sharing among several ‘tenants’ — either applications or users. For Kubernetes workloads, this means one cluster is shared by multiple applications or users.
Under a multi-tenant setup, GPU resources can be used to their full capacity, meaning companies will avoid over-provisioning resources or wasting them. This is possible because when different workloads operate from one cluster, Kubernetes can dynamically allocate resources depending on the individual needs of each tenant.
Further, a multi-tenant architecture makes Kubernetes’ inherent horizontal scalability more cost-efficient. Horizontal scaling is important to manage applications that require GPU resources, but creating personal clusters for each tenant is expensive. With multi-tenancy, scalability becomes more accessible as each new workload shares the same underlying cluster resources.
The result of Kubernetes multi-tenancy is significant cost savings for GPU cloud providers. With several tenants sharing GPU resources in one cluster, providers can reduce overall operational costs by optimizing GPU utilization and minimizing idle time.
However, to truly realize the benefits of Kubernetes multi-tenancy for GPU workloads, cloud providers should turn to a new approach for Kubernetes management — virtual clusters.
How Virtual Clusters Unlock Optimal Multi-Tenancy
Virtual clusters provide game-changing advantages in cost efficiency, security and scalability compared to traditional Kubernetes management frameworks. A virtual cluster is an isolated Kubernetes environment that behaves just like a regular cluster, but which can be spun up from inside a single physical cluster to enable better control and efficient sharing of resources.
With virtual clusters, GPU cloud providers can maintain as many Kubernetes tenant environments as they like within a single physical cluster, which greatly reduces the operational overhead of security and compliance requirements. While traditional Kubernetes clusters are complicated and resource-intensive to maintain, virtual clusters centralize management tasks into a single overarching framework.
Virtual clusters also give cloud providers unmatched flexibility, as they can be spun up or torn down instantly to meet the workload demands of tenants, all without affecting the foundational physical cluster. ML, scientific computation and other tasks requiring GPUs often have workloads that change rapidly, requiring dynamic reconfiguration of resources and quick deployment. This makes the flexibility of virtual clusters even more critical for GPU cloud providers.
Additionally, virtual clusters solve the isolation challenges of traditional Kubernetes multi-tenancy. Bringing tenants together in one cluster may result in weaker isolation between workloads, meaning the performance of one tenant may interfere with another or cause security vulnerabilities. In contrast, virtual clusters are completely isolated, as each one is a distinct and fully functional cluster. GPU cloud providers can thus ensure that activities in one virtual cluster will not affect any others, leading to a stable and secure architecture.
Just as with regular multi-tenancy, the bottom line is that virtual clusters make resource management more cost-effective. But for GPU cloud providers, virtual clusters offer the additional advantage that each one can be managed and monitored independently. This makes it easy to track resource usage to a specific tenant, making the billing practice more granular, fair and accurate.
Overall, implementing a multi-tenant Kubernetes architecture via virtual clusters offers GPU cloud providers unprecedented levels of efficiency, agility and security lacking in traditional cluster management practices. Not only will this approach reduce the internal operational burden and maximize ROI for GPU cloud providers, it will also empower them to offer the best experience for their customers.
Given the rapidly changing AI landscape at every level of the technology stack, it is more important than ever for cloud providers to proactively optimize spending and resource management. Virtual clusters are a strategic advantage in this competitive, ever-changing cloud ecosystem.