EMA Blog: Business Intelligence and IT Management

IT organizations have always relied on multiple tools to manage and monitor their networks. This reality is a legacy of the evolution of network management tools. Initially, vendors offered point products that solved very narrow sets of tool use cases like device discovery, device metric collection, config management, traffic monitoring, and so on. Network engineers needed a sprawling toolset to address all aspects of operations.

OpenManage Enterprise is a big deal, but underrated: OpenManage Enterprise is the new infrastructure automation platform that could unlock the private and hybrid cloud for Dell. Deploying Dell infrastructure through a simple YAML file is within reach.

For readers who would like to see what happened to our 2017 predictions, please take a look. Jens Soeldner and the EMA Team sat down again this month to look into the crystal ball for 2018.

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.

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.

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.'

Why are there still so many repetitive tasks in data center and cloud management today? Why does application management still contain so many manual steps? Why do most organization still suffer from automation and monitoring silos that prevent them from avoiding preventable application outages and service degradations?

VMware’s container management strategy should be seen as the key to success for this year’s VMworld. VMware needs to conclusively convince its customers that instead of doing their own thing the company is able to offer a strong value proposition based  on the fact that Kubernetes and Amazon ECS are the key container technologies to embrace. DockerCon has shown that VMware admins have realized that it is time to “learn about containers,” but VMware’s challenge today is to convince customers that Kubernetes management will not happen entirely separately from virtualization management. Even worse, Kubernetes management may not even require virtualization at all.

“Oh, I’m not coming to VMworld this year, we are at Jenkins instead.” This is a sentence I’ve heard quite a few times now from vendors that I’m used to seeing at VMworld and it makes me wonder what’s going on.

(You can read more about the content of the EMA Top 3 - Enterprise Decision Guide in my first Post)