DevOps
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DevOps

DevOps is a set of practices that combines software development (Dev) and IT operations (Ops). It aims to shorten the systems development life cycle and provide continuous delivery with high software quality.[1] DevOps is complementary with Agile software development; several DevOps aspects came from the Agile methodology.

Definition

Other than it being a cross-functional combination of the terms and concepts for "development" and "operations," academics and practitioners have not developed a unique definition for the term "DevOps".[a][b][c][d] The idea behind this practice is to make delivery teams responsible for the production issues and fixes, whether legacy or new. In traditional practices, delivery would only be responsible for the changes put in by them, within the warranty period.

From an academic perspective, Len Bass, Ingo Weber, and Liming Zhu--three computer science researchers from the CSIRO and the Software Engineering Institute--suggested defining DevOps as "a set of practices intended to reduce the time between committing a change to a system and the change being placed into normal production, while ensuring high quality".[5]

History

In 1993 the Telecommunications Information Networking Architecture Consortium (TINA-C) defined a Model of a Service Lifecycle that combined software development with (telecom) service operations.[6] Some say that DevOps emerged in part as a reaction to the "top-down" proscriptive approach of ITIL in the 1990s. DevOps, as a "bottom-up" approach, gained traction and persisted because it was created by software engineers for software engineers, and is a flexible practice rather than a rigid framework.

In 2014, Lisa Crispin and Janet Gregory wrote the book More Agile Testing, containing a chapter on testing and DevOps.[7]

Taxonomy

The DevOps taxonomy was refined and made publicly available in 2021 through the release of a white paper titled, "The DevOps: A Concise Understanding to the DevOps Philosophy and Science".[8] The DevOps taxonomy consists of Philosophy and Science, their respective branches (Epistemology and Ontology; Social and Applied) and sub-domains (Belief, Knowledge, and Truth; Sociology, Psychology, Economics, and Engineering) with provided instantiations (Agile, Quality Assurance, Lean; Culture Transformation, Faster Time to Market, and Automation).

Toolchains

As DevOps is intended to be a cross-functional mode of working, those who practice the methodology use different sets of tools--referred to as "toolchains"--rather than a single one.[9] These toolchains are expected to fit into one or more of the following categories, reflective of key aspects of the development and delivery process.

  1. Coding - code development and review, source code management tools, code merging.
  2. Building - continuous integration tools, build status.
  3. Testing - continuous testing tools that provide quick and timely feedback on business risks.
  4. Packaging - artifact repository, application pre-deployment staging.
  5. Releasing - change management, release approvals, release automation.
  6. Configuring - infrastructure configuration and management, infrastructure as code tools.
  7. Monitoring - applications performance monitoring, end-user experience.

Relationship to other approaches

Many of the ideas fundamental to DevOps practices are inspired by, or mirror, other well known practices such as Lean and Deming's Plan-Do-Check-Act cycle, through to The Toyota Way and the Agile approach of breaking down components and batch sizes.[8]

Agile

The motivations for what has become modern DevOps and several standard DevOps practices such as automated build and test, continuous integration, and continuous delivery originated in the Agile world, which dates (informally) to the 1990s, and formally to 2001. Agile development teams using methods such as Extreme Programming couldn't "satisfy the customer through early and continuous delivery of valuable software"[10] unless they subsumed the operations / infrastructure responsibilities associated with their applications, many of which they automated. Because Scrum emerged as the dominant Agile framework in the early 2000s and it omitted the engineering practices that were part of many Agile teams, the movement to automate operations / infrastructure functions splintered from Agile and expanded into what has become modern DevOps. Today, DevOps focuses on the deployment of developed software, whether it is developed via Agile or other methodologies.

ArchOps

ArchOps presents an extension for DevOps practice, starting from software architecture artifacts, instead of source code, for operation deployment.[11] ArchOps states that architectural models are first-class entities in software development, deployment, and operations.

Continuous delivery

Continuous delivery and DevOps have common goals and are often used in conjunction, but there are subtle differences.[12]

While continuous delivery is focused on automating the processes in software delivery, DevOps also focuses on the organizational change to support great collaboration between the many functions involved.[12]

DevOps and continuous delivery share a common background in agile methods and lean thinking: small and frequent changes with focused value to the end customer. Lean management and continuous delivery are fundamental to delivering value faster, in a sustainable way. Continuous delivery focuses on making sure the software is always in a releasable state throughout its lifecycle.

Improved collaboration and communication both between and within organizational teams can help achieve faster time to market, with reduced risks.[13]

DataOps

The application of continuous delivery and DevOps to data analytics has been termed DataOps. DataOps seeks to integrate data engineering, data integration, data quality, data security, and data privacy with operations. It applies principles from DevOps, Agile Development and the statistical process control, used in lean manufacturing, to improve the cycle time of extracting value from data analytics.

Site-reliability engineering

In 2003, Google developed site reliability engineering (SRE), an approach for releasing new features continuously into large-scale high-availability systems while maintaining high-quality end-user experience.[14] While SRE predates the development of DevOps, they are generally viewed as being related to each other.


DevSecOps, Shifting Security Left

DevSecOps is an augmentation of DevOps to allow for security practices to be integrated into the DevOps approach. The traditional centralized security team model must adopt a federated model allowing each delivery team the ability to factor in the correct security controls into their DevOps practices. Shifting security left is an approach to software security whereby security practices and testing are performed earlier in the development lifecycle.

BizOps

BizOps is contrasted with DevOps because of its more integrated approach. While DevOps is more focused on IT and software development, BizOps integrates technology into daily organizational decisions and business operations.

Cultural change

DevOps initiatives can create cultural changes in companies[15] by transforming the way operations, developers, and testers collaborate during the development and delivery processes.[1] Getting these groups to work cohesively is a critical challenge in enterprise DevOps adoption.[16][17] DevOps is as much about culture, as it is about the toolchain.[18]


Building a DevOps culture

Organizational culture is a strong predictor of IT and organizational performance. Cultural practices such as information flow, collaboration, shared responsibilities, learning from failures and new ideas are central to DevOps. Team-building and other employee engagement activities are often used to create an environment that fosters this communication and cultural change within an organization. DevOps as a service approach allows developers and operations teams to take greater control of their applications and infrastructure without hindering speed. It also transfers the onus of owning a problem on to the development team, making them much more careful in their stride.

The 2015 State of DevOps Report discovered that the top seven measures with the strongest correlation to organizational culture are:

1. Organizational investment

2. Team leaders' experience and effectiveness

3. Continuous delivery

4. The ability of different disciplines (development, operations, and infosec) to achieve win-win outcomes

5. Organizational performance

6. Deployment pain

7. Lean management practices

Deployment

Companies with very frequent releases may require knowledge on DevOps.[] For example, the company that operates image hosting website Flickr developed a DevOps approach to support ten deployments a day. Daily deployment cycles would be much higher at organizations producing multi-focus or multi-function applications.[] Daily deployment is referred to as continuous deployment

Architecturally significant requirements

To practice DevOps effectively, software applications have to meet a set of architecturally significant requirements (ASRs), such as: deployability, modifiability, testability, and monitor-ability.

Microservices

Although in principle it is possible to practice DevOps with any architectural style, the microservices architectural style is becoming the standard for building continuously deployed systems. Small size service allows the architecture of an individual service to emerge through continuous refactoring,[19].

DevOps automation

DevOps automation can be achieved by repackaging platforms, systems, and applications into reusable building blocks through the use of technologies such as virtual machines and containerization.

Implementation of DevOps automation in the IT-organization is heavily dependent on tools, which are to cover different areas of the systems development lifecycle (SDLC):

  1. Infrastructure as code
  2. CI/CD
  3. Test automation
  4. Containerization
  5. Orchestration
  6. Software deployment
  7. Software measurement

Adoption

DevOps practices and adoption

DevOps practices, and their dependencies include a dependency network which connects potential benefits to an ordered chain of practices. Using this network organizations can choose a path that enables fulfillment of their goals.

Adoption of DevOps is being driven by many factors - including:

  1. Use of agile and other development processes and methods;
  2. Demand for an increased rate of production releases - from application and business unit stakeholders;
  3. Wide availability of virtualized and cloud infrastructure - from internal and external providers;
  4. Increased usage of data center automation and configuration management tools;
  5. Increased focus on test automation and continuous integration methods;
  6. A critical mass of publicly available best practices.

See also

Notes

  1. ^ Dyck et. al (2015) "To our knowledge, there is no uniform definition for the terms release engineering and DevOps. As a consequence, many people use their own definitions or rely on others, which results in confusion about those terms."[2]
  2. ^ Jabbari et. al (2016) "The research results of this study showed the need for a definition as individual studies do not consistently define DevOps."[3]
  3. ^ Erich et. al (2017) "We noticed that there are various gaps in the study of DevOps: There is no consensus of what concepts DevOps covers, nor how DevOps is defined."[4]
  4. ^ Erich et. al (2017) "We discovered that there exists little agreement about the characteristics of DevOps in the academic literature."[4]

References

  1. ^ a b Loukides, Mike (7 June 2012). "What is DevOps?". O'Reilly Media.
  2. ^ Dyck, Andrej; Penners, Ralf; Lichter, Horst (19 May 2015). "Towards Definitions for Release Engineering and DevOps". Proceedings of the 2015 IEEE/ACM 3rd International Workshop on Release Engineering. IEEE: 3. doi:10.1109/RELENG.2015.10. ISBN 978-1-4673-7070-7. S2CID 4659735.
  3. ^ Jabbari, Ramtin; bin Ali, Nauman; Petersen, Kai; Tanveer, Binish (May 2016). "What is DevOps?: A Systematic Mapping Study on Definitions and Practices". Proceedings of the 2016 Scientific Workshop. Association for Computing Machinery.
  4. ^ a b Erich, F.M.A.; Amrit, C.; Daneva, M. (June 2017). "A Qualitative Study of DevOps Usage in Practice". Journal of Software: Evolution and Process. 29 (6): e1885. doi:10.1002/smr.1885. S2CID 35914007.
  5. ^ Bass, Len; Weber, Ingo; Zhu, Liming (2015). DevOps: A Software Architect's Perspective. ISBN 978-0134049847.
  6. ^ Chapman, M., Gatti, N: A model of a service life cycle, Proceedings of TINA '93, pp. I-205-I-215, Sep., 1993.
  7. ^ Crispin, Lisa; Gregory, Janet (October 2014). More Agile Testing. ISBN 9780133749571. Retrieved 2019.
  8. ^ a b Klein, Brandon Thorin (1 May 2021). "The DevOps: A Concise Understanding to the DevOps Philosophy and Science". doi:10.2172/1785164. Cite journal requires |journal= (help)
  9. ^ Gartner Market Trends: DevOps - Not a Market, but Tool-Centric Philosophy That supports a Continuous Delivery Value Chain (Report). Gartner. 18 February 2015.
  10. ^ "Principles behind the Agile Manifesto". agilemanifesto.org. Retrieved 2020.
  11. ^ Castellanos, Camilo; Correal, Dario (15 September 2018). Executing Architectural Models for Big Data Analytics. Lecture Notes in Computer Science. 11048. pp. 364-371. doi:10.1007/978-3-030-00761-4_24. ISBN 978-3-030-00760-7.
  12. ^ a b Humble, Jez; Farley, David (2011). Continuous Delivery: reliable software releases through build, test, and deployment automation. Pearson Education Inc. ISBN 978-0-321-60191-9.
  13. ^ Chen, Lianping (2015). "Continuous Delivery: Huge Benefits, but Challenges Too". IEEE Software. 32 (2): 50-54. doi:10.1109/MS.2015.27. S2CID 1241241.
  14. ^ Beyer, Betsy; Jones, Chris; Petoff, Jennifer; Murphy, Niall Richard (April 2016). Site Reliability Engineering. O'Reilly Media. ISBN 978-1-4919-2909-4.
  15. ^ Emerging Technology Analysis: DevOps a Culture Shift, Not a Technology (Report). Gartner.
  16. ^ "Gartner IT Glossary - devops". Gartner. Retrieved 2015.
  17. ^ Jones, Stephen; Noppen, Joost; Lettice, Fiona (21 July 2016). Proceedings of the 2nd International Workshop on Quality-Aware Dev Ops - QUDOS 2016 (PDF). pp. 7-11. doi:10.1145/2945408.2945410. ISBN 9781450344111. S2CID 515140.
  18. ^ Mandi Walls (25 September 2015). "Building a DevOps culture". O'Reilly.
  19. ^ Chen, Lianping; Ali Babar, Muhammad (2014). "Towards an Evidence-Based Understanding of Emergence of Architecture through Continuous Refactoring in Agile Software Development". The 11th Working IEEE/IFIP Conference on Software Architecture(WICSA 2014). IEEE. doi:10.1109/WICSA.2014.45.

Further reading

  • Davis, Jennifer; Daniels, Ryn (30 May 2016). Effective DevOps : building a culture of collaboration, affinity, and tooling at scale. Sebastopol, CA: O'Reilly. ISBN 9781491926437. OCLC 951434424.
  • Kim, Gene; Debois, Patrick; Willis, John; Humble, Jez; Allspaw, John (7 October 2015). The DevOps handbook : how to create world-class agility, reliability, and security in technology organizations (First ed.). Portland, OR. ISBN 9781942788003. OCLC 907166314.
  • Forsgren, Nicole; Humble, Jez; Kim, Gene (27 March 2018). Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations (First ed.). IT Revolution Press. ISBN 9781942788331.

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