Factsheet (Doc #IDC_P39965 / Aug 13, 2020)
Infrastructure Trends and Strategies: Enterprise Workloads

IDC's Infrastructure Trends and Strategies: Enterprise Workloads program takes a top-down approach of infrastructure adoption, trends, and usage by workloads. This program serves as a companion program to the Server and Storage Workloads Tracker® and will carry the qualitative color (via primary research) that guides data in the trackers.

Markets and Subjects Analyzed

  • Current and next-gen workloads
  • Traditional, cloud-native, and microservices-based apps
  • SQL, NoSQL, and NewSQL databases
  • Service mesh architectures
  • FaaS/serverless architectures
  • Use of file and object storage for cloud-native apps
  • Private, public, and hybrid cloud stacks for next-gen workloads (e.g., Kubernetes, Docker, GKE, PKS)
  • Cloud on-premises stacks (e.g., AWS Outposts)
  • AI/ML apps and workloads
  • Cloud migration tools
  • Development methodologies and paradigms

Core Research

  • Worldwide Server and Storage Workloads Forecast, 2020–2024
  • Public Cloud On-Premises (Anthos, IBM Cloud Private, and Azure Stack) Value Proposition for Current and Next-Gen Workloads
  • Best Practices for Workload Deployments: Choosing Location and Deployment Type
  • Pathways to the Cloud — Application Transformation Options and Implications for Choice of Infrastructure

In addition to the insight provided in this service, IDC may conduct research on specific topics or emerging market segments via research offerings that require additional IDC funding and client investment.

Key Questions Answered

  1. How are workload deployments shifting in terms of on premises, cloud, and edge? What changes do they go through when they shift?
  2. What kind of enabling infrastructure stacks are used when workloads get refactored, repackaged, or replatformed?
  3. What best practices are other organizations using to evaluate, modernize, and transform their infrastructure for new/next-gen/cloud-native workloads?
  4. How should organizations embrace algorithm shifts (e.g., AI/ML workloads) that require the introduction of new hardware in their datacenters?
  5. What consumption models and deployment location should be employed for superior business outcomes?
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