3.1 KiB
Operator requirements
operator-requirements-sla.rst operator-requirements-logging-monitoring.rst operator-requirements-network-design.rst operator-requirements-licensing.rst operator-requirements-support-maintenance.rst operator-requirements-ops-access.rst operator-requirements-quota-management.rst operator-requirements-policy-management.rst operator-requirements-hardware-selection.rst operator-requirements-software-selection.rst operator-requirements-external-idp.rst operator-requirements-upgrades.rst operator-requirements-bleeding-edge.rst operator-requirements-skills-training.rst
Several operational factors affect the design choices for a general purpose cloud. Operations staff receive tasks regarding the maintenance of cloud environments, including:
- Maintenance tasks
-
Operating system patching, hardware/firmware upgrades, and datacenter related changes, as well as minor and release upgrades to OpenStack components are all ongoing operational tasks. In particular, the six monthly release cycle of the OpenStack projects needs to be considered as part of the cost of ongoing maintenance. The solution should take into account storage and network maintenance and the impact on underlying workloads.
- Reliability and availability
-
Reliability and availability depend on the many supporting components' availability and on the level of precautions taken by the service provider. This includes network, storage systems, datacenter, and operating systems.
In order to run efficiently, automate as many of the operational processes as possible. Automation includes the configuration of provisioning, monitoring and alerting systems. Part of the automation process includes the capability to determine when human intervention is required and who should act. The objective is to increase the ratio of operational staff to running systems as much as possible in order to reduce maintenance costs. In a massively scaled environment, it is very difficult for staff to give each system individual care.
Configuration management tools such as Ansible, Puppet, and Chef enable operations staff to categorize systems into groups based on their roles and thus create configurations and system states that the provisioning system enforces. Systems that fall out of the defined state due to errors or failures are quickly removed from the pool of active nodes and replaced.
At large scale, the resource cost of diagnosing failed individual systems is far greater than the cost of replacement. It is more economical to replace the failed system with a new system, provisioning and configuring it automatically and adding it to the pool of active nodes. By automating tasks that are labor-intensive, repetitive, and critical to operations, cloud operations teams can work more efficiently because fewer resources are required for these common tasks. Administrators are then free to tackle tasks that are not easy to automate and that have longer-term impact on the business, for example, capacity planning.