Multicloud architectures offer many benefits, but they also create some special performance challenges--especially if the teams who design and manage multicloud deployments don’t pay careful attention to the performance bottlenecks that can creep up within multicloud architectures.
Here’s a look at three of the most common multicloud performance difficulties, along with tips on overcoming them.
Multicloud Architecture Performance Overview
By now, many IT professionals are familiar with the advantages of a multicloud strategy. By allowing organizations to run workloads on more than one cloud at once, multicloud offers opportunities for greater cost effiency and reliability.
In addition, a multicloud strategy can in some cases help to improve overall workload performance. You might choose to deploy an application using one service from one cloud vendor and another service from another cloud vendor, because that approach offers better performance at a better price point than relying on the services of a single vendor.
Performance Challenges in Multicloud Architectures
Yet, while multicloud architectures offer some potential performance advantages, they can also lead to performance shortcomings. The more clouds you have in the mix, the easier it becomes to connect them together in ways that create performance bottlenecks, thereby slowing down workloads that would run faster if they were hosted in the same cloud.
These multicloud architecture performance problems can be avoided, but only with the proper planning.
1. Networking between clouds
First and foremost, consider network connections that span clouds.
When you have two applications or services exchanging data within the same cloud, the data typically does not need to travel over the internet; instead, it stays within the cloud provider’s infrastructure. Network bandwidth and latency rates can vary depending on whether the data is traveling between different data centers or regions. In virtually all cases, however, data transferred over the network within the same cloud is going to move a lot faster than data that has to travel across the Internet from one cloud to another.
What this means is that network connections between clouds can become a serious performance bottleneck for multicloud architectures.
Given that the network is basically the only way to connect one cloud service to another, there is no way to avoid network performance bottlenecks entirely. However, IT teams can deploy a few strategies to mitigate the problem:
- Avoid multicloud architectures where large volumes of data are stored in one cloud but need to be processed in another. You might be tempted, for instance, to use one cloud provider’s storage service because it is cheaper, while feeding data from that service to an application hosted in another cloud. This might save a little money, but it may not be worth the performance cost.
- When possible, compress data before it leaves one cloud and moves into another. Compressing data might increase your cloud compute bill, but it will improve performance.
- In cases where workloads are mirrored across two or more clouds to improve reliability, design the workloads in such a way that each cloud’s instance of the workload can operate even if its data is not synced with the other instance’s version of the data. This approach ensures that data transfer delays won’t interrupt workload performance.
2. Monitoring multiple clouds
Another frequent performance challenge for multicloud architectures is the increased difficulty of monitoring multiple clouds. When monitoring your clouds becomes harder, identifying performance or availability issues within the clouds is harder, too.
The best way to avoid this pitfall is to rely on cloud monitoring tools that can monitor all of your clouds at once. These days, virtually all APM solutions support all of the major clouds, so it’s easy enough to find a tool that fits the bill.
Keep in mind, however, that optimal performance monitoring for multiple clouds includes not just monitoring all of your clouds at once, but configuring the tools to understand the nuances of multicloud workloads. In other words, your tools must recognize that two services running in different clouds are connected and depend upon each other in order to be able to alert you effectively to potential problems.
3. Scaling limitations
One of the key advantages of cloud computing in general is the ability to scale resource allocations for a workload up or down quickly when demand shifts.
Within a single cloud, it’s quite easy to configure autoscaling of workloads using the cloud vendor’s native autoscaling tool. But when your workloads span multiple clouds, autoscaling becomes trickier. You can’t use Azure’s autoscaling framework to scale up the AWS-based components of your multicloud workload, and vice versa.
You can, of course, configure autoscaling on each cloud separately. That approach will work well enough, and the manual effort it requires may not be so much as to overwhelm an IT team (since autoscaling configuration is typically a set-it-and-forget-it affair).
However, in cases where autoscaling for multicloud workloads is particularly complex, teams might consider relying on a universal control plane to set up and manage their cloud environments. A universal control plane will automate scaling and load-balancing across clouds automatically, eliminating the need to configure autoscaling in each cloud individually.
Multicloud strategies can boost cloud performance, but only with the right architectural design and tooling. Without planning for network bandwidth bottlenecks between clouds, as well as multicloud monitoring and scaling challenges, organizations risk missing out on the performance opportunities that multicloud confers.