On October 25, Cisco and Google announced their hybrid cloud partnership, where Google brings the container runtime (Kubernetes), the platform to provide, manage, and consume APIs (Apigee), and of course a wide range of consumable cloud services (visual recogngition, machine learning, text to voice, etc.). Cisco contributes the hyperconverged infrastructure (Hyperflex) with Kubernetes management (Harmony), networking (Nexus 9k), and hybrid cloud management software (CloudCenter) to integrate Google’s public cloud services with the customer’s local data center.
SDDC 2.0 with Kubernetes, Apigee, and Istio – Cisco’s Collaboration with Google Follows a Grander Vision
I’m excited to kick off this project, as no other topic in IT seems to have so many different aspects to consider and so many points to attack it. As, hopefully, in all our EMA research projects, we will look at this topic from all angles without any predetermined outcomes. The project will be exclusively guided by what’s best for the customer when it comes to ramping up a container strategy.
Machine Learning and Artificial Intelligence: The Promised Land for Lowering IT OPEX, Decreasing Operational Risk and Optimally Supporting Business Goals
What should machine and artificial intelligence (ML/AI) do for IT operations, DevOps and container management? The following table represents my quick outline of the key challenges and specific problem ML/AI needs to address. The table is based on the believe that ML/AI needs to look over the shoulder of IT ops, DevOps, and business management teams to learn from their decision making. In other words, every virtualization administrator fulfills infrastructure provisioning or upgrade requests a little bit differently. Please regard the below table as a preliminary outline and basis for discussion. At this point, and probably at no future point either, I won't claim to know the 'ultimate truth.'