The Road to Open Hybrid Cloud: Part 2 - Workload Balancing

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How can you get the benefits of open hybrid cloud fast? Watch these step-by-step demos from Red Hat Summit that show how to go from bare metal servers to a flexible, scalable infrastructure that supports your business applications.

Part 2: Need to handle workload spikes in your applications? This live demo shows how to automatically scale out to multiple public cloud vendors on-demand using OpenShift Container Platform and AMQ Interconnect . And we see how Red Hat Insights can troubleshoot and optimize traffic problems in the cloud using machine learning and AI technologies. 

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[Burr] In the average business, you're gonna have peaks and valleys, you're gonna have spikes in your application traffic at any given time where you are overwhelmed with transactions and you have to scale out to handle that additional load. In this demonstration, what you're about to see us do, is scale out from our onstage private data center to multiple public cloud providers, begin to scale back and scale out, and then have Red Hat Insights watch over us to see how that application's performing.

[Ted] We're running on the stage in our private cloud, an application that's providing fraud detection service for financial transactions. So in order to keep our latency down and keep our customers happy, we've deployed extra service capacity in the public cloud. So we have capacity with Microsoft Azure in Texas and with Amazon Web Services in Ohio. So we use OpenShift container platform on all three locations because OpenShift makes it easy for us to deploy our containerized services wherever we want to put them. But the question still remains, how do we balance the workload across these three locations in such a way that we efficiently use our resources and that we give our customers the best possible experience? So this is where Red Hat AMQ Interconnect comes in. As you can see, we've deployed AMQ Interconnect alongside our fraud detection application in all three locations. Once these connections are established, AMQ figures out the best way automatically to route traffic to where it needs to get to. What we have right now is a distributed, reliable, brokerless message bus that expands our entire enterprise.

[Burr] I love how the AMQ Interconnect router allows you to measure latency as well as to scale out and scale in of your application or cross those multiple cloud providers. Let's find out what happens when there's a spike in our transaction workload where we may actually have a situation where one of our cloud providers is underperforming.

[Ted] But now if we take a little bit of a burst of increased traffic, we're gonna see the AMQs gonna push a little of that traffic out onto the public cloud. So Azure's picking up some of the load now to keep the latencies down and Amazon Web Services is gonna get thrown into the fray as well. Burr, what's the story with AWS?

[Burr] I notice there's a problem right here right now. We seem to have a little bit of a performance issue.

[Brent] So guys, I notice that as well and a little bit ago I actually got an alert from Red Hat Insights letting us know that there might be some potential optimizations we could make to our environment. So let's take a quick look at Insights. Here's the Red Hat Insights interface, you can see our three OpenShift deployments and Insights is highlighting that that deployment in Ohio may have some issues that need some attention. Insights uses machine learning and A.I. techniques to analyze all collected data so we combine collected data from not only this system's configuration but also with other systems from across the Red Hat customer base. We also get access to tailored recommendations that let us know what we can do to optimize our systems. So if we want to automate that type of a remediation, we can use this inside of Red Hat Ansible Tower, Red Hat Satellite, Red Hat CloudForms. It's really, really powerful. The other thing here is that we can actually apply these recommendations right from within the Red Hat Insights interface. So with just a few clicks, I can select all the recommendations that Insights is making and using that built-in Ansible automation, I can apply those recommendations really, really quickly across a variety of systems and if we go back and look at the map, we should see that our AWS deployment in Ohio is in a much better state than it was just a few minutes ago.

[Burr] Looks like we went green. Now let's see what it looks like over here. There it goes. Awesome, so now we're load balancing across the three clouds.

[Ted] Very nice.

[Burr] Fantastic. So now you've seen a true hybrid cloud native application in action. The ability to scale out and manage application peaks and valleys based on your business needs but we recognize you have an existing investment and legacy applications. We're gonna take those existing virtual machines you have in your private data center and we're gonna bring them to the modern world, the Red Hat way.