To stay ahead of competition, organizations are constantly looking for ways to drive innovation with speed and agility, while maximizing operational and economic efficiency at the same time. To that end, they have been migrating their applications to multi-cloud and hybrid cloud environments for quite some time.
Initially, these applications were moved to the cloud using a “lift-and-shift” approach, retaining their original monolithic architecture. However, such monolithic applications are unable to fully exploit the benefits offered by cloud, such as elasticity and distributed computing, and are also difficult to maintain and scale.
Consequently, as the next evolutionary step, organizations have started to rearchitect their existing applications or develop new ones as cloud-native applications.
A related aspect to deploying cloud-native applications is that of using automation to do so. Organizations deploying such applications have had success in automating the deployment of the underlying infrastructure on which these cloud-native applications run, as well with the initial deployment of these applications. They have, however, struggled with the subsequent steps, such as making these applications accessible to end users, scaling the applications up and down, or moving them from one cloud to another.
The main reason for this is that load balancers, which are used to front-end these applications, and make them accessible to end users, were designed with monolithic applications in mind, and hence are unable to keep pace with the agile manner in which these cloud-native applications are deployed.
These load balancers were designed for a deployment process in which network resources for the applications are provisioned manually by network and security teams, a process that could take days if not weeks, and then manually configured on the load balancer. This process clearly inhibits achieving the goal of automation to which organizations aspire.
Further compounding this problem is the fact that when deploying applications in multi-cloud and hybrid cloud environments, each public cloud provider has its own custom load balancer and management system. For example, AWS has its own Elastic Load Balancing solution, which is different from Microsoft’s Azure Load Balancer. This makes the task of automating application deployment even more complex and time consuming. It also makes the task of applying a consistent set of policies across the different cloud environments more error prone as each load balancer has its own separate configuration.
So, What’s the Solution?
