homeblogsuccessful large scale container deployments depend automation and discovery

Successful large-scale container deployments depend on automation and discovery

Editor's note: Guest author Mary Johnston Turner is the research vice president for enterprise systems management software at technology research firm IDC. This blog post is published with the kind permission of IDC.

Modern application containers like Docker offer IT infrastructure and DevOps teams an opportunity to modernize applications, improve workload portability, and accelerate adoption of microservices application architectures and continuous delivery/continuous integration (CD/CI) tool chains. IDC expects that by 2020, enterprise customers will be running more than a billion simultaneous container instances at any one time. Over 75 percent of DevOps teams are currently investing or planning to invest in containers, and containerized workloads are expected to be evenly split across production and pre-production application environments.

Container orchestration technologies like Kubernetes automate the rapid deployment and scaling of containers across clusters of hosts. Container registries allow developers to store, update, deploy and reuse predefined container images, helping to standardize software implementations and accelerate DevOps initiatives.

Discovery and automation are vital for effective container operations

The downside of containers is that many traditional infrastructure and application processes still rely on manual configuration, patching and deployment strategies, and staff may not have been fully trained on how to adapt their activities to accommodate rapidly changing containerized workloads. In addition, existing discovery, patching, and updating tools often lack the ability to inspect and discover the contents of individual containers. As a result, containers can appear as “black boxes” once deployed onto virtual or physical servers, and many organizations may struggle to validate, patch and maintain both the configuration state and compliance of containers running in large-scale deployments.

IDC’s research indicates that container implementations are acting as a catalyst for many organizations to reexamine and streamline existing configuration, deployment and discovery processes and management tools. In fact, 80 percent of organizations that are currently evaluating container management solutions tell us that they want to reduce operational complexity and simplify operations as part of implementing enterprise-scale container management strategies.

Organizations that already have some level of experience using containers at scale report that automation and discovery, optimized for containerized environments, are critical. Specifically, these decision makers look for automation solutions that can:

  • Maintain the correct order of operations and deployment at scale, across application, middleware and infrastructure resources.
  • Span on-premises and public cloud infrastructure and workloads.
  • Discover resources and dependencies, and inspect container contents.
  • Maintain container configurations and manage drift to ensure that all containerized workloads are fully patched and up-to-date.

IDC expects that over 70 percent of production containers will be deployed on virtualized infrastructure during the next several years. Container management solutions must integrate transparently with existing application and infrastructure management processes and tools to maintain end-to-end service levels and IT operations productivity.

Enterprise container strategies will mature rapidly

Early users of container technologies have tended to operate in small development teams or specialized proof-of-concept teams. However, as enterprises increasingly adopt containers to support mission-critical applications and digital innovation, management strategies are rapidly evolving to provide automated support for large-scale container deployments. IDC expects enterprise IT operations and DevOps teams will need to invest aggressively in container management automation and discovery to maintain service levels and optimize infrastructure cost and utilization.

Guest author Mary Johnston Turner is the vice president of research at research firm IDC.

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