Jump to content

Orchestration (computing)

From Wikipedia, the free encyclopedia

In system administration, orchestration is the automated configuration, coordination,[1] deployment, development, and management of computer systems and software.[2]

Many tools exist to automate server configuration and management, including Airflow, Kubernetes, Ansible, Puppet, Salt, Terraform,[3] and AWS CloudFormation.[4]


Orchestration is often discussed in the context of service-oriented architecture, virtualization, provisioning, converged infrastructure and dynamic data center topics. Orchestration in this sense is about aligning the business request with the applications, data, and infrastructure.[5]

In the context of cloud computing, the main difference between workflow automation and orchestration is that workflows are processed and completed as processes within a single domain for automation purposes, whereas orchestration includes a workflow and provides a directed action towards larger goals and objectives.[2]

In this context, and with the overall aim to achieve specific goals and objectives (described through the quality of service parameters), for example, meet application performance goals using minimized cost[6] and maximize application performance within budget constraints,[7] cloud management solutions also encompass frameworks for workflow mapping and management.

See also[edit]


  1. ^ Sarma, Anita (11 Feb 2019). "Coordination Technologies". In Sungdeok Cha; Richard N. Taylor; Kyochul Kang (eds.). Handbook of Software Engineering. Springer Cham. ISBN 978-3-030-00262-6. Retrieved 15 July 2024.
  2. ^ a b Erl, Thomas (2005). Service-Oriented Architecture: Concepts, Technology & Design. Prentice Hall. ISBN 0-13-185858-0.
  3. ^ Brikman, Yevgeniy (2016-09-26). "Why we use Terraform and not Chef, Puppet, Ansible, SaltStack, or CloudFormation". Archived from the original on 2016-10-05. Retrieved 2018-12-17.
  4. ^ Giangntc (2019-04-12). "AWS CloudFormation Introduction". Archived from the original on 2013-01-03. Retrieved 2019-04-12.
  5. ^ Menychtas, Andreas; Gatzioura, Anna; Varvarigou, Theodora (2011). "A Business Resolution Engine for Cloud Marketplaces". 2011 IEEE Third International Conference on Cloud Computing Technology and Science. IEEE Third International Conference on Cloud Computing Technology and Science (CloudCom). IEEE. pp. 462–469. doi:10.1109/CloudCom.2011.68. ISBN 978-1-4673-0090-2. S2CID 14985590.
  6. ^ Mao, Ming; M. Humphrey (2011). "Auto-scaling to minimize cost and meet application deadlines in cloud workflows". Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis. pp. 1–12. doi:10.1145/2063384.2063449. ISBN 978-1-4503-0771-0. S2CID 11960822.
  7. ^ Mao, Ming; M. Humphrey (2013). "Scaling and Scheduling to Maximize Application Performance within Budget Constraints in Cloud Workflows". 2013 IEEE 27th International Symposium on Parallel and Distributed Processing. pp. 67–78. doi:10.1109/IPDPS.2013.61. ISBN 978-0-7695-4971-2. S2CID 5226147.