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Compute intensive workloads are defined by their high utilization of CPU, RAM, or both. User requirements will determine if a cloud must be built to accommodate anticipated performance demands. Cost Cost is not generally a primary concern for a compute-focused cloud, however some organizations might be concerned with cost avoidance. Repurposing existing resources to tackle compute-intensive tasks instead of needing to acquire additional resources may offer cost reduction opportunities. Time to market Compute-focused clouds can be used to deliver products more quickly, for example, speeding up a company's software development life cycle (SDLC) for building products and applications. Revenue opportunity Companies that are interested in building services or products that rely on the power of the compute resources will benefit from a compute-focused cloud. Examples include the analysis of large data sets (via Hadoop or Cassandra) or completing computational intensive tasks such as rendering, scientific computation, or simulations.

Many jurisdictions have legislative and regulatory requirements governing the storage and management of data in cloud environments. Common areas of regulation include: Data retention policies ensuring storage of persistent data and records management to meet data archival requirements. Data ownership policies governing the possession and responsibility for data. Data sovereignty policies governing the storage of data in foreign countries or otherwise separate jurisdictions. Data compliance—certain types of information needs to reside in certain locations due to regular issues—and more important cannot reside in other locations for the same reason. Examples of such legal frameworks include the data protection framework of the European Union and the requirements of the Financial Industry Regulatory Authority in the United States. Consult a local regulatory body for more information.

The following are some technical requirements that need to be incorporated into the architecture design. Performance If a primary technical concern is for the environment to deliver high performance capability, then a compute-focused design is an obvious choice because it is specifically designed to host compute-intensive workloads. Workload persistence Workloads can be either short-lived or long running. Short-lived workloads might include continuous integration and continuous deployment (CI-CD) jobs, where large numbers of compute instances are created simultaneously to perform a set of compute-intensive tasks. The results or artifacts are then copied from the instance into long-term storage before the instance is destroyed. Long-running workloads, like a Hadoop or high-performance computing (HPC) cluster, typically ingest large data sets, perform the computational work on those data sets, then push the results into long term storage. Unlike short-lived workloads, when the computational work is completed, they will remain idle until the next job is pushed to them. Long-running workloads are often larger and more complex, so the effort of building them is mitigated by keeping them active between jobs. Another example of long running workloads is legacy applications that typically are persistent over time. Storage Workloads targeted for a compute-focused OpenStack cloud generally do not require any persistent block storage (although some usages of Hadoop with HDFS may dictate the use of persistent block storage). A shared filesystem or object store will maintain the initial data set(s) and serve as the destination for saving the computational results. By avoiding the input-output (IO) overhead, workload performance is significantly enhanced. Depending on the size of the data set(s), it might be necessary to scale the object store or shared file system to match the storage demand. User interface Like any other cloud architecture, a compute-focused OpenStack cloud requires an on-demand and self-service user interface. End users must be able to provision computing power, storage, networks and software simply and flexibly. This includes scaling the infrastructure up to a substantial level without disrupting host operations. Security Security is going to be highly dependent on the business requirements. For example, a computationally intense drug discovery application will obviously have much higher security requirements than a cloud that is designed for processing market data for a retailer. As a general start, the security recommendations and guidelines provided in the OpenStack Security Guide are applicable.

The compute intensive cloud from the operational perspective is similar to the requirements for the general-purpose cloud. More details on operational requirements can be found in the general-purpose design section.