Google Unfurls Managed Kubernetes Service for the Enterprise

Google this week launched an enterprise edition of its managed Google Kubernetes Engine (GKE) service through which it will manage fleets of clusters in addition to applying customer configurations and policy guardrails.

GKE Enterprise was announced at the Google Cloud Next 2023 conference. In addition to making it easier to isolate workloads, GKE Enterprise makes available security services, including workload vulnerability insights, governance and policy controls and a managed service mesh based on open source Istio software that Google initially co-developed.

Step 1 of 7

14%

Which of the following best describes how you currently monitor and manage systems across your hybrid cloud environment?(Required)

Multiple separate tools for different environments

Multiple separate tools for different monitoring and management functions

Single consolidated dashboard

Custom built monitoring and management solution

No full monitoring and management solution

Don’t know

How valuable would it be for your monitoring and management solution to incorporate predictive analytics to help you identify and address system issues that are critical and not waste time or effort working through all the data and “noise” that is not critical?(Required)

Extremely valuable

Moderately valuable

Somewhat valuable

Slightly valuable

Not valuable

How do you most typically address potential system issues BEFORE they impact your operations?(Required)

Automated workflows and playbooks to remediate potential issues as soon as they are detected

An orchestrated response process that leverages automation, AI, and human expertise to rapidly respond to potential issues

Advanced monitoring tools such as predictive analytics and anomaly detection to proactively identify and address potential issues

Respond to issues as they arise, using manual processes and troubleshooting techniques

A combination of automated remediation, orchestrated response, and/or proactive monitoring to address potential system issues

Don’t know

Other

What methods does your organization use to control and monitor cloud/IT spending?(Required)

Centralized procurement processes

Pre-approved budget thresholds

Automated cost monitoring tools

Department-level spending controls

Regular cost audits and reviews

Cloud cost optimization platform

Approval workflows for resource provisioning

Native financial operations tools provided by the cloud provider

No formal controls in place

Don’t know

Other

Please specify:

Which methods do you rely on most to detect security vulnerabilities in your systems?(Required)

Automated scanning tools

Manual assessments

Vendor notifications

Threat modeling (for early detection)

None of these

Don’t know

Other

Please specify:

What are the TWO most common ways you prioritize vulnerabilities to address within your environment?(Required)

Based on potential impact

Based on exploitability

Regulatory compliance requirements

Based on CVSS scores

Risk-based analysis of multiple factors

First-in-first-out approach

No formal prioritization process

Don’t know

How do you typically handle system updates and security patches?(Required)

Automated updates and patches

Manually review and apply updates and patches on a regular schedule

A combination of automation and manual review

Apply updates and patches on an as-needed basis.

Outsourced update and patch management

No defined process

Don’t know

Other

Δ

GKE Enterprise has been integrated with Google Distributed Cloud, an integrated platform based on software and hardware from Google that enables IT teams to deploy applications distributed across public clouds and on-premises IT environments.

Finally, Google announced that GKE Enterprise also now supports Cloud TPU v5e instances to make it simpler to deploy complex artificial intelligence (AI) models on Kubernetes clusters. Google has been making a case for adopting its TPU ASIC to run neural networks to train generative AI applications.

Google and NVIDIA this week also announced the general availability of its latest A3 instances, powered by NVIDIA H100 Tensor Core graphical processor units. (GPUs).

Thomas Kurian, CEO for Google Cloud, told conference attendees that Google is already making extensive use of NVIDIA GPUs to train the foundational models that drive its generative AI models.

NVIDIA CEO Jensen Huang added that generative AI is revolutionizing every layer of the computing stack. The two companies are working together to reengineer cloud infrastructure to optimize it for generative AI models, he added.

It’s not clear whether cloud-native computing platforms such as Kubernetes are at the core of building and deploying AI models. At the very least, almost every cloud-native application will need to invoke multiple AI models via application programming interfaces (APIs). Inevitably, that will introduce some orchestration challenges that platforms such as Kubernetes are meant to address.

In the meantime, each organization will need to decide whether they prefer to manage Kubernetes clusters themselves or rely on a managed service that enables them to devote more resources to the development of applications and AI models. The tradeoff, historically, has been the limitations that managed services have imposed on DevOps workflows that tend to be fairly unique within each organization.

Of course, in the case of Kubernetes, the expertise required to manage these platforms is still hard to find and retain. As AI advances continue, it’s only a matter of time before more infrastructure management tasks are automated. It remains to be seen how quickly internal IT teams will benefit from the advances compared to a managed service provider that also happens to build and host the large language models (LLMs) used to create AI models.

Regardless of the outcome, it’s clear that many of the tasks that once made Kubernetes clusters challenging to manage are being automated due to higher levels of abstraction.