Orchestration (computing)

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

A number of tools exist for automation of server configuration and management, including Ansible, Puppet, Salt, Terraform,[2] and AWS CloudFormation.[3]

Usage

Orchestration is often discussed in the context of service-oriented architecture, virtualization, provisioning, converged infrastructure and dynamic datacenter topics. Orchestration in this sense is about aligning the business request with the applications, data, and infrastructure.[4]

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.[1]

In this context, and with the overall aim to achieve specific goals and objectives (described through quality of service parameters), for example, meet application performance goals using minimized cost[5] and maximize application performance within budget constraints.[6]

gollark: No. They are only code. You should not be directly configuring routes differently in different deployments.
gollark: They are part of the application code.
gollark: Your routes ARE NOT CONFIGURATION.
gollark: ħħħħħħħħħħħħħħħħħħħ you.
gollark: no.

See also

References
  1. Thomas Erl. Service-Oriented Architecture: Concepts, Technology & Design. Prentice Hall, ISBN 0-13-185858-0.
  2. Yevgeniy Brikman (2016-09-26). "Why we use Terraform and not Chef, Puppet, Ansible, SaltStack, or CloudFormation".
  3. Giangntc (2019-04-12). "AWS CloudFormation Introduction".
  4. 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
  5. 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 (SC2011). doi:10.1145/2063384.2063449. ISBN 978-1-4503-0771-0.
  6. Mao, Ming; M. Humphrey (2013). Scaling and Scheduling to Maximize Application Performance within Budget Constraints in Cloud Workflows. Proceedings of the 2013 IEEE 27th International Symposium on Parallel and Distributed Processing(IPDPS2013). pp. 67–78. doi:10.1109/IPDPS.2013.61. ISBN 978-0-7695-4971-2.
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