As per Gartner Research, new computing abstractions are changing how and where enterprises will consume virtualization. Enterprise architecture and technology innovation leaders should use this decision framework to align their use cases with the appropriate virtualization technologies to benefit from these innovations.
Some of the assumptions and expectations lead to the facts that:
- By 2023, 70% of artificial intelligence workloads will use application containers or be built using a serverless programming model necessitating a DevOps culture.
- By 2022, more than 75% of global organizations will be running containerized applications in
production, which is a significant increase from less than 30% today. - By 2022, more than 50% of containerized workloads will span hybrid environments, up from less than 20% today.
So, when to use what – VMs, Containers, Serverless? What experts from Garter say?
Virtual machines are used when:
- Applications are monolithic, large and static with stringent data persistence needs
- A variety of operating systems and version releases need to be run and managed
- Complete isolation from the host OS is required
Containers are used when:
- Applications are lightweight, ephemeral and need rapid deployment.
- Software environments are highly dynamic such as development, QA environments with agile code development or to foster collaborative environments such as continuous integration/continuous delivery (CI/CD)
- Provisioning and scaling time is critical (measured in seconds, not minutes like VMs).
Serverless functions can be used for:
- Applications with unpredictable scaling needs that are ephemeral in nature
- Applications where fine-grained operational control of the runtime environment isn’t required
- Batch computing tasks like multimedia or data processing that aren’t latency-sensitive and can subscribe to an event-driven operating model
Choosing the appropriate computing abstraction by evaluating positive factors of VMs, Containers and Serverless functions may be very challenging.
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Source: SUSE
