Automation is a Key Cog in the Next Wave of Multi-Cloud Management Tools
Enterprises are adopting multi-cloud strategies to allow them to cherry-pick services from multiple public cloud providers that fit them best. But this is not as easy as it sounds. The number of cloud services an enterprise uses can easily get out of hand. The more cloud services an organization uses, the more complex the management becomes. But all is not lost. The right cloud management tools can help. The trouble is in choosing the right one.
The market for multi-cloud management tools is constantly evolving. This has made it difficult for enterprises to find tools that are the right fit for them. Market consolidation has played a huge role in this. Larger players have acquired many third-party tool vendors. For example, Microsoft bought Cloudyn while VMware bought CloudHealth Technologies. This trend is bound to continue well into the future. With time, the research and development momentum required to improve these third-party tools will slow down. For this reason, enterprises should consider if an ownership change at their multi-cloud management tools provider will affect them. After all, it might affect the quality of the products and, consequently, their multi-cloud operations.
Furthermore, specialized multi-cloud management tools will not be the endgame for most enterprises. These tools could include those meant for managing cost, performance, and security. Sometimes, orchestration frameworks, such as Kubernetes, can provide a more consistent solution for multi-cloud management.
Multi-Cloud Tools Are Becoming More About Automation and Governance
The last couple of years have seen a lot of changes in multi-cloud management tools. The tools were frequently used as abstraction layers of brokerage services. They tried to boil down public clouds to the lowest common denominator. However, organizations are increasingly adopting agile development practices and more complex cloud services. This has made providers of cloud management tools focus more on automation and governance.
From now on, multi-cloud management tools are going to focus on these three features:
Tools focused on cloud ops handle cloud services based on the individual components they need to keep track of. The tools do this by automating the use of services based on “who” and “what” to assist with security. For example, they might track the developers using specific services. They will keep track of when they use the services and for what purpose. The tool keeps a log of all service usage to facilitate change management, automated updates, and configuration management. These are key to operations and development.
Abstraction involves the deployment of services on top of multiple cloud services. Doing this helps to create a single pane of glass view of computing, storage, security, and other resources. The idea is to hide the complexity of managing multiple clouds by bringing them together under one user interface. This brings the back-end down to the native API level. Abstraction helps reduce human intervention and effectively limits errors and speeds up management.
You should remember the more native services you hide under the abstraction services, the more you dilute the services’ value. It leaves you with a least common denominator approach. A good example is when you use a common storage concept. If you use a service to delete, retrieve, and store data, it’s possible to miss out on some native services. For example, you may miss out on rollbacks that may only be available from a single provider.
3. Container orchestration
Technologies, such as Kubernetes and Docker Swarm, popularized container orchestration. Container orchestration makes it possible for enterprises to manage native cloud services as containers. These tools can help enterprises cluster and manage these containers.
The tools can pool resources to manage and scale them as groups or single clusters. Operations teams and developers get more control of their workloads. This is because they’re able to deal with containers as macro and micro concepts.
This approach helps to provide portability between clouds. Organizations have adopted these tools because containers are standardized and are independent of any tech stack. This means that they can run on any multi-cloud platform predictably. Nonetheless, the containerization of some applications is not always a straightforward process. For instance, file-based databases and COBOL have poor deployment analogs for containers. Consequently, there will be redevelopment and redeployment efforts required.
So, where are multi-cloud management tools headed? The demand for multi-cloud management is rising. Most enterprises are realizing that they have to use more than one cloud to optimize their operations. This means there is a need for tools that enable organizations to do this at scale.
Vendors are bound to adopt machine learning in their tools to facilitate these efforts. Many providers are already incorporating artificial intelligence (AI) into their products. This is in a bid to help enterprises automate the management of workloads across multiple cloud environments.