Devops

Facial Recognition Technology (FRT) emerged as a solution to address several contemporary needs of identifying and verifying an individual's identity. It fulfills the biometric system requirements, which tries to recognize the status of an individual by using features distinctive from the body and functionalities that are more familiar with the operation of visual surveillance. This report develops an analysis that connects the socio-scientific literature with the technology on FTR and addresses the concerns and challenges attached to the development, evolutional, and the operational usage that are specific, the contexts, and goals. It highlights the problematic, potentials, and the limitations of the technology. The report also identifies the tasks that the FRT seems to be ready to deploy, the areas with specified obstacles, and how to overcome them by the developments of future technology and operating procedures of sound. It also addresses specific issues that appear to interact with technology. It is also concerned with the ethical considerations on the extent of efficacy. The report's findings are further broken down into different categories to understand further the evaluation, performance, operation, policy concerns, and political and moral operations. So far, the technology has been implemented in several fields to enable monitoring and surveillance. In this background, the report also addresses the FRT alterations on the nature of the authoritarian and lines that are oppressive in the United States as the primary focus.

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The development to operations model, known as DevOps, refers to a framework that integrates the development and operation phases of organizational applications. DevOps bridges the gap between these two processes that traditionally prolonged the implementation of these applications. This framework aims at enhancing service delivery and releasing subsequent updates based on real-time customer feedback. Consumers in this modern era prefer tailor-made services, and a slight downtime from one supplier can quickly shift their preference to the company's competitors. However, DevOps involves sophisticated coding activities, and there is a need for someone to oversee these processes to ensure developers are not overwhelmed. The objective of this survey is to clarify the significance of a lead person to monitor the DevOps pipeline and report any failure detected in each development stage. The study utilized meta-analysis of verified tertiary resources from other researchers that took a deep dive into the DevOps model and how it works towards organizational goals in various firms. All institutions involved in multiple business platforms should take advantage of new technologies to scale up their service delivery and net profit. If adopted and appropriately monitored, DevOps can revolutionize the capabilities of most business applications by minimizing downtimes and providing uninterrupted services.

The IoT continuous to restructure scientific, industrial, commercial, and engineering endeavors in impulsive ways. This paper discusses the practice of Agile Method in IoT projects. The agile method and its best practices can be applied involving observing, warning, and prevention. But a need for version control software at the device level, demands a different kind of software design for IoT. It changes the way IoT device development works. This paper highlights the key software design considerations and discusses the key software design decisions made for IoT.

The enterprises follow Agile Software Development methodology to because business requirements changes frequently. In Agile Software Development methodology, it is essential to continuously integrate the component into a main trunk of a project to test the new component of the system. Then test all the component of the project; this happens frequently so it needs to stream line processes to orchestrate the tests. So it is difficult to manage the software development life cycle for those changes and maintain the software code quality. To maintain the product quality it is essential to integrate the product component and need to deploy a product on pre-production environment and test the product. Hence the need for Continuous Integration and Continuous Delivery process for software product. The popularization of DevOps, and cloud computing has revolutionized the software delivery process- making it faster and affordable for business to release their software continuously. Hence Enterprises need for reliable and predictable delivery process of software. The objective of the paper is to design an effective framework for automated testing and deployment to help to automate the code analysis, test selection, test scheduling, environment provisioning, test execution, results analysis and deployment pipeline. Test orchestration framework typically very complicated to develop such pipeline to make software reliable, and bug free. For environment provisioning can be provided through virtualization and cloud computing.

Cloud has become an advanced technology in the modern field of information technology and the need to meet the growing demand of customers. Pressure on information technology organizations is constantly increasing to apply for a client in a private cloud. This change has already occurred as a large number of customers have begun direct contact with cloud vendors through this support. In addition, DevOps teams are at a very focused level now because they are responsible for automation and universal delivery and client programming. This paper focuses on the automation of customer application from the provision of environment to the delivery of the system.

This research paper explored how machine learning can be leveraged in improving CI/CD Pipeline and DevOps Processes. As a result of the intrinsic complexity of software creation and maintenance - not just
in terms of technical complexity, but also from a human standpoint - some obstacles may be handled as learning problems. Software processes and products may benefit from machine learning approaches by gaining insight into tactics that can lead to better quality [1]. An ongoing study area predicts how likely something will be to fail due to a
defect. Companies may increase their company value by
operating in an agile mode, according to DevOps rules, which allows for more rapid communication, decisionmaking, and problem-solving [1]. It is described in this article how La Redoute's IT department is doing continuing research into the use of machine learning methods to
increase the performance of tools and methodologies inside the DevOps pipeline.

The advancements in cloud computing and leveraging the benefits from cloud computing to the service providers have increased the deployment of traditional applications to the cloud. The applications once deployed on the cloud, due to various reasons, need migration from development infrastructure to operational infrastructure, one operational instance to other operational instances due to load balancing and the cycle continues due to the use of DevOps as development strategies for cloud computing applications. Advocates of hybrid and public clouds observe cloud computing makes it possible for organizations to avert or minimize upfront IT infrastructure expenses. Proponents also assert that cloud computing systems permit businesses to receive their software up and running faster, using improved manageability and less maintenance, so it empowers IT teams to rapidly adapt tools to meet the varying and unpredictable requirements. DevOps is a lot of practices that mechanizes the procedures between programming improvement and IT groups, all together that they can fabricate, test, and discharge programming quicker and even more dependably. The idea of DevOps is established on building a culture of a joint effort between groups that generally worked in relative siloes. The guaranteed advantages incorporate expanded trust, quicker programming discharges, capacity to explain basic issues rapidly and better oversee impromptu work. Thus, this work identifies the need for providing multiple security protocols during the complete life cycle of cloud application development and deployment. This work proposes a novel framework for automatic selection and deployment of the security protocols during cloud service deployments. The framework identifies the need for security aspects and selects the appropriate security algorithms for virtual machines. The proposed framework demonstrates nearly 80% improvement over the security policy deployment time.

The main purpose of this paper is to explore DevOps and its applications in Cloud development andTesting. There's no denying it: DevOps and cloud go hand in hand. This trend will only continue since the bulk of
cloud development projects now use DevOps. The advantages of utilizing DevOps with cloud applications are increasingly becoming evident. Competing well in the market necessitates a company's ability to supply services and applications at a rapid rate. To be effective,
management procedures and tools need a model that is both swift and dependable. Because of this, we must automate the DevOps processes utilizing cloud and noncloud DevOps automation technologies while designing cloud-native apps. The purpose of this article is to discuss
how to migrate DevOps to the cloud and improve software development and operational agility. Likewise, this project will examine ways to expand such DevOps processes and automation to public and/or private clouds. If one is interested in learning more about how the emerging field of DevOps is changing the IT industry, read this paper. Understanding how DevOps and the Cloud work together to aid organizations in transforming themselves is the
ultimate objective.

The ecosystem of the Internet of Things is a set of physical devices such as sensors and actuators. It includes a set of servers and gateways that provide connectivity. These devices can be installed at three levels: the edge level, the fog level, and the cloud level. Applications that monitor things and collect data from sensors are deployed at the edge, fog, and cloud levels. These applications can be containerized and deployed in different devices by Docker, which takes advantage of its advantage to create executable containers that are isolated from each other. This paper presents a generic Meta-model of the Internet of Things ecosystem based on microservices and supported by Docker as a containerization tool, Ansible as a monitoring tool and Kubernetes as scaling tool. This Meta-model allows us to generate a system of connected objects that can be deployed on three levels: Fog, Edge, Cloud. This Meta-model can be used in different types of domains such as Smart Home-Smart City-Smart Vehicle-Smart Health-Smart Farm-Smart Factory. The Meta-model proposed in this article is a fusion between 5 Meta-models: Internet of Things-Microservices-Ansible-Docker-Kubernetes.

Businesses today need to respond to customer needs at unprecedented speeds. Driven by this need for speed, many companies are rushing to the DevOps movement and implementing Continuous Delivery (CD). I had been implementing DevOps and CD for Paddy Power, a multi-billion-euro betting and gaming company, for four years. I had found that software architecture can be a key barrier. To address the architectural challenges, we tried an emerging architectural style called Microservices. I have observed increased deployability, modifiability, and resilience to design erosion. At the same time, I also observed new challenges associated with the increased number of services, evolving contracts among services, technology diversity, and testing. I share the practical strategies that can be employed to address these new challenges, discuss situations for which Microservices may not be a good choice, and outline areas that require further research.

NOTE: Book is published by The Open Group Press, freely available on line at http://pubs.opengroup.org/opengrouppress/managing-digital/. PDF can be downloaded from https://publications.opengroup.org/g183. The book is also the basis for a forthcoming (Q4 2019) standard, The Digital Practitioner Body of Knowledge (DPBoK). These publications are developed using a collaborative, git-based approach. An open source governance process is under development with the sponsorship of The Open Group. Contact the author.

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The first general survey of IT management written from an Agile and Lean IT perspective. Suitable for an MIS or IT introductory course. Covers Cloud-native and DevOps practices and their implications for higher-order IT management concerns (operations, portfolio, process management, governance, architecture, etc). Based on a unique "emergence model," a thought experiment taking the student through individual human/computer interaction, to small team, to "team of teams," to enterprise.

DevSecOps is nothing but involving next level security to the DevOps process since Security is the key priority given to any application or data irrespective of any organization. DevSecOps not only provide security but also provides the same speed and agility which DevOps tends to provide any data or Application. DevSecOps will also tend to keep integrating, developing and updating of the latest security methods that tend to give adequate, proficient and productive results in a secured manner. Therefore, to demonstrate the DevSecOps we intend to use the open source tools which can be freely downloaded and used to demonstrate the data security. In order to run the frequent process of integrating and testing for the security of data, we require more resource and time respectively. To overcome this problem here we implementing and integrating security as part of the pipeline process and hence can achieve faster response time and fend off attacks beforehand and accordingly create a secure environment and more protected system.

DevOps is an approach based on lean and agile principles in which business, development, operations, and quality teams cooperate to deliver software continuously aiming at reducing time to market, and receiving constant feedback from customers. However, implementing DevOps can be a complex and challenging mission due it requires significant paradigm shift. Consequently, many failures and misconceptions can occur about DevOps adoption by organizations, despite its numerous benefits. This work identifies, describes, and compares different perspectives related to DevOps adoption in academy and industry. The perspectives can be understood as factors or variables that influence or help to understand the DevOps journey. We employed a sequential multi-method research approach, including Systematic Literature Review (SLR) and Case Study. As a result, eight perspectives were found: concepts, models, principles, practices, difficulties, challenges, benefits, and strategies. More specifically,...

The main purpose of this paper is to explore the best practices that can be adopted to ensure security in DevOps. Among the paper's key topics are the definitions of both DevOps and DevSecOps. The day when
developers created code and operations built and managed it is long gone. To put it another way, the DevOps approach has altered how firms build software. Companies benefit from speedier releases and staying on top of the ever-changing market thanks to the integration of both teams. Security teams, on the other hand, oftenfell behind, unable to keep up with the rapid development cycles [1]. To identify and address security issues early in the development cycle, DevSecOps incorporates security policies throughout the DevOps process.

A new discipline at the intersection of the development and operation of software systems known as DevOps has seen
significant growth recently. Among the wide range of tasks of DevOps professionals, we focus on that of selecting
appropriate cloud deployments for distributed applications. Despite the advent of automated software deployment
and management frameworks, reasoning about good deployments still requires interaction with experts, often
through discussions on online technical forums and social networks.
Current social networking technologies offer basic ways to communicate. Within the DevOps community,
communication on application structure and cloud deployment tradeoffs could become more effective by using
knowledge present in global community-sourced information repositories. In this paper we argue for the benefits of
tapping into such knowledge and for seamlessly feeding it back into the social networking platform.
The social networking platform presented in this paper integrates social networking with automated deployment of
applications on multi-clouds and with knowledge drawn from community-sourced information repositories. The
implementation leverages two such repositories, the PaaSage repository and Chef Supermarket. Our user evaluation
experiments demonstrate the value created for DevOps professionals.

When to adopt DevOps?

DevOps should be used for large distributed applications such as eCommerce sites or applications hosted on a cloud platform.
When not to adopt DevOps?

It should not be used in a mission-critical application like bank, power and other sensitive data sites. Such applications need strict access controls on the production environment, a detailed change management policy, access control policy to the data centers.
DevOps Lifecycle

DevOps is deep integration between development and operations. Understanding DevOps is not possible without knowing DevOps lifecycle.

Here is a brief information about the Continuous DevOps life-cycle:

1. Development

In this DevOps stage the development of software takes place constantly. In this phase, the entire development process is separated into small development cycles. This benefits DevOps team to speed up software development and delivery process.

2. Testing

QA team use tools like Selenium to identify and fix bugs in the new piece of code.

3. Integration

In this stage, new functionality is integrated with the prevailing code, and testing takes place. Continuous development is only possible due to continuous integration and testing.

4. Deployment

In this phase, the deployment process takes place continuously. It is performed in such a manner that any changes made any time in the code, should not affect the functioning of high traffic website.

5. Monitoring

In this phase, operation team will take care of the inappropriate system behavior or bugs which are found in production.
DevOps Work Flow

Workflows provide a visual overview of the sequence in which input is provided. It also tells about actions are performed, and output is generated for an operations process.

Software Reliability has just passed the 50-year milestone as a technical discipline along with Software Engineering. This paper traces the roots of Software Reliability Engineering (SRE) from its pre-software history to the beginnings of the field with the first software reliability model in 1967 through its maturation in the 1980s to the current challenges in proving application reliability on smartphones and in other areas. This history began as a thesis proposal for a History of Science research program and includes multiple previously unpublished interviews with founders of the field. The project evolved to also provide a survey of the development of SRE from notable prior histories and from citations of new work in the field including reliability applications to Agile Methods. This history concludes at the modern-day providing bookends in the theory, models, literature, and practice of Software Reliability Engineering from 1968 to 2018 and pointing towards new opportunities to deepen and broaden the field. The paper also provides a timeline for the field stretching form 1816 to 2017.

The concept of DevOps is a hot topic in recent years. DevOps is a collection of methods for developers and collaborates to deliver software and services rapidly, reliably and with higher quality. The concept of 'lean' defines a kind of the operation in the Toyota Production System. Continuous Integration (CI) and Continuous Delivery (CD) are one of the important practices in the software development. CI is one of the agile software development practice that involves automation and frequent integration of the software changes. DevOps and CD are another subset of the agile. DevOps could be extended with agile methods and by using patterns and practices it helps to improve collaboration between development and operation teams. Both development models of agile and lean are similar to in terms of goals by focusing on the customers and responding to their needs in a rapid method. Agile software development focuses on the software development function, but Lean Thinking has a very explicit focus on the end-to-end process. In this review article, the principle and practices of DevOps, reasons for its usage, challenges of DevOps, the relationship of DevOps with agile are studied.

Current IT strategies depend largely on the ability of companies to push changes/features/fixes in an agile and error-free manner. The need for automation increases dramatically as technology advances. Waterfall software development techniques have been replaced by agile software development approaches in the last decades. In this shift, companies, operations, and software developers are unquestionably relatively close to the shared goals. To promote this merger, the industry offers a large range of open source and proprietary solutions. Technologies have increased themselves and thus new techniques have been developed, namely continuous integration (CI), continuous delivery (CDE) and permanent deployment (CD), assisted by all of the above-mentioned open source and proprietary solution. Automation is the primary requirement for DevOps, and DevOps's main concept is "Automate everything." DevOps automation commences from the coding on the developer machine until the code is run and then the application and system are controlled in production. The whole DevOps pipeline includes continuous integration, ongoing testing, and continuous deployment, including live application monitoring. The main focus of DevOps practice is the design and configuration of automated infrastructure and software delivery. DevOps practice depends heavily on automation, to implement for a few hours, and to execute frequently across different platforms. Automation in DevOps, therefore, promotes speed, greater precision, consistent, and reliable delivery rates. In the end, DevOps automation captures everything from design, delivery, and supervision.

Digital transformation requires new approaches to curricula and instruction. New delivery approaches such as Agile development, Lean Product Management, Site Reliability Engineering, DevOps, and related trends require an updated educational response. A survey of educational and business professionals on workforce needs related to Agile, DevOps, and other topics inform this report, which identifies five competency areas IT, IS, and computing educators should consider in designing next-generation curricula:
-  Dynamic infrastructure and operations
-  Continuous delivery
- Product management
- Resource and execution management
- Organization and culture
These areas are elaborated with definitions, categories, competencies, and sample learning objectives, with references. Recommendations for adapting existing courses and practical guidance on developing digital labs and simulations are also included.

[EDIT: Update October 28th 11:47 UTC: Yesterdays 5-10% drop in numbers has been fixed for me, within 24 hours! Kudos to Academia.edu Support, and good luck to everyone else]

A summary of events that have unfolded in between March 11th and 24th 2021. Until today, May 27th, problems continue to be worked on

Updated June 6th

In this paper, author Alvar Lumberg surveyed software development practices used and problems encountered by Estonian startup companies. Based on the results, ties were identified between the encountered issues and ignored practices.

At first the author researched existing literature to find software development practices most suitable for achieving the goals of software startups. After that a questionnaire was prepared and 14 startup companies were surveyed in terms of their practices and problems. The companies differed in team size, age, development phase and, whether the team was co-located or distributed between multiple locations. The interviews were conducted in person to achieve consistent answers.
The gathered responses were analyzed, to compile an overview of good practices used and problems encountered, and to find correlations between them. The author investigated if problems present in startups could be related with ignoring a best practice described in literature. He presented an overview of usage of the practices and encountered problems, as well as conclusions on what problems could be linked to not using specific practices based on the survey results.
Most pronounced links were found between quality and test automation. Companies that have problems with development quality in general do not use neither unit tests nor continous integration. Just as well, companies having problems with testing quality or regression do not use automated acceptance tests. The opposite is valid as well - companies using unit and/or automated acceptance tests are satisfied with the quality of their systems.

The author also presented a few other practices, identified by the companies as beneficial or helpful in lowering the risks. These practices included refactoring to promote sustainability of development, code reviews to ensure quality and avoid regression, and daily standups to improve communication.

With this paper, the author wishes to support the development of Estonian startups and offer support to starting teams in selecting beneficial practices. In his future he plans to conduct additional research into the matter. By reviewing the existing literature, software development in startups has not been thoroughly researched and there is room for additional exploration in the field.

One possible direction of further research lies in explaining, what is special about software development in startups. The author sees a deeper, more qualitative study of early phase startups as a second avenue of further research, to analyze the changes in needs and prob- lems during their first development stages. Thirdly, compiling a list of suitable practices for a specific software startup, applying these practices and assessing the results is also seen as a subject matter for deeper investigation.

Agility means the capability to swiftly and efficiently respond to internal and environmental organization changes. For IT, it is the ability to provide new services and IT solutions to support the innovative business processes. In the ever-changing business world of today, improving agility is the best way to face future challenges. IT service management is the capacity to collect data, analyze, report and implement agile improvements. Successful IT must be efficient and agile to promote the traditional company transformation to a digital enterprise. The propose of this work is a holistic and practical strategic framework to improve ITSM service management processes with the additions of two drivers Agility management based on DevOps, and an agility Process Maturity Framework (APMF). This research will enable decision-makers to improve and measure agility enhancements and hence compare the agility of Information Systems before and after APMF deploying.

Jenkins é uma poderosa ferramenta de código aberto destinado a executar Integração Contínua criada com a linguagem Java o que está associada a sua portabilidade para os mais diversos sistemas operacionais. Permite executar uma lista predefinida de etapas (denominado de pipeline), como por exemplo baixar o código-fonte de um repositório na Web, compilar conforme comandos da linguagem e construir um executável a partir das classes resultantes e publicá-la em um servidor definido. Jenkins para muitos analistas representam medo, para mim representa uma forma de gerar muitas tarefas repetidas de forma totalmente automática e ganhar algo que não consigo dar, vender ou emprestar: Tempo. Descubra nesta como facilmente começar a criar seu ambiente profissional.

The present essey discusses the DevOps movement that has been applied at a
considerable pace in the production of software. DevOps is not a framework or
methodology, its concept and mission are based on building a culture of mutual
collaboration between teams that have historically been inserted in different contexts.
It puts an end to the lack of communication between infrastructure and development,
contributing to the delivery of software in production in an agile, safe and stable way.
Software development does not stop with the release of the code, but with the closing
of the feedback loop between those who write the code and those who use it. DevOps
is an organizational and human issue, which seeks to use means of automating the
development cycle to deliver quickly. As well as the construction of the software, the
availability can also occur in an automated way. The automation of processes helps to
improve the quality of the software, streamlining release cycles of versions, keeping
the application active in the production environment. Continuous integration, a
software development practice in which each team member integrates their work at
least once a day, is a fundamental requirement for DevOps. With the help of
automation, at each commit, the code is compiled and unit and integration tests are
performed. By making use of continuous integration, the process can be extended to
a continuous delivery or continuous deployment model. Continuous delivery is the
natural evolution when there is an interest in expanding the benefits of continuous
integration. It is the ability to provide changes safely and quickly, ensuring that the code
is always ready for deployment. One of the main objectives of continuous delivery is
to reduce the time between the decision to change a software, be it correction or new
functionality, and the moment when this change is available to the user. In the
continuous delivery model, the generated application is automatically made available
in an approval environment, which after undergoing an evaluation by the quality team
is made available in production through a simple button click. Continuous deployment
goes a step beyond continuous delivery, and is applied primarily to web applications.
After executing all the steps of the deployment pipeline and making the version
available, all usability and acceptance tests are performed automatically, and in the
end, being the result satisfactory, the deployment in the production environment occurs
immediately, without human intervention at all the process. The idea is to make small
changes in production available more frequently, in addition to carrying out tests in
environments with similar characteristics to production. If the entire chain runs
smoothly, the new version of the application is available. If inconsistencies are
identified during the execution of the tests, the construction is interrupted preventing
the creation of a failed application. It is important to note that throughout the process,
feedback to team members is constant, making them aware of the new version
released or the flaws that prevented the release.

Context: Continuous Delivery and Deployment (CD) practices aim to deliver software features more frequently and reliably. While some efforts have been made to study different aspects of CD practices, a little empirical work has been reported on the impact of CD on team structures, collaboration and team members' responsibilities. Goal: Our goal is to empirically investigate how Development (Dev) and Operations (Ops) teams are organized in software industry for adopting CD practices. Furthermore, we explore the potential impact of practicing CD on collaboration and team members' responsibilities. Method: We conducted a mixed-method empirical study, which collected data from 21 in-depth, semi-structured interviews in 19 organizations and a survey with 93 software practitioners. Results: There are four common types of team structures (i.e., (1) separate Dev and Ops teams with higher collaboration; (2) separate Dev and Ops teams with facilitator(s) in the middle; (3) small Ops team with more responsibilities for Dev team; (4) no visible Ops team) for organizing Dev and Ops teams to effectively initiate and adopt CD practices. Our study also provides insights into how software organizations actually improve collaboration among teams and team members for practicing CD. Furthermore, we highlight new responsibilities and skills (e.g., monitoring and logging skills), which are needed in this regard.

A complex project involves complex development effort and it involves more manual work and more time. It is very difficult to reduce the time involved. In the development cycle the use of the concurrent engineering concept only increases the problem by increasing the number of people and department involved at any point in the development effort. DevOps techniques can be used in these projects to control the use of project elements. Using DevOps Technique many companies involved in complex projects are able to reduce both development time and manual work involved in projects. This project solves issues that arise due to manual work on a project that involve more than one member in a team. The usual way of building a project in which code are distributed among team members leads to many issues like integration and compatible problems. The existing version control system gives us integration but that need to be tuned for more effective way of using that tool. This project builds an automation system that handles all the integration and deployment phrases automatically. This also reduces the time of compilation of huge codes. Manager need not wait for the results from team members since the results are stored automatically in Git.

DevOps is a culture which promotes collaboration between Development and Operations Team to deploy code to production faster in an automated & repeatable way. The word 'DevOps' is a combination of two words 'development' and 'operations.' DevOps helps to increases an organization's speed to deliver applications and services. It allows organizations to serve their customers better and compete more strongly in the market. In simple words, DevOps can be defined as an alignment of development and IT operations with better communication and collaboration.

The ecosystem of the Internet of Things is a set of physical devices such as sensors and actuators. It includes a set of servers and gateways that provide connectivity. These devices can be installed at three levels: the edge level, the fog level, and the cloud level. Applications that monitor things and collect data from sensors are deployed at the edge, fog, and cloud levels. These applications can be containerized and deployed in different devices by Docker, which takes advantage of its advantage to create executable containers that are isolated from each other. This paper presents a generic Meta-model of the Internet of Things ecosystem based on microservices and supported by Docker as a containerization tool, Ansible as a monitoring tool and Kubernetes as scaling tool. This Meta-model allows us to generate a system of connected objects that can be deployed on three levels: Fog, Edge, Cloud. This Meta-model can be used in different types of domains such as Smart Home-Smart City-Smart Vehicle-Smart Health-Smart Farm-Smart Factory. The Meta-model proposed in this article is a fusion between 5 Meta-models: Internet of Things-Microservices-Ansible-Docker-Kubernetes.

Este artigo partiu de uma ideia com base na publicação 50+ Useful DevOps Tools de Agustin Romano e não é minha intenção tomar a autoria de outra obra. O que fiz neste trabalho foi realizar uma tradução e em muitas oportunidades a complementação de dados faltantes nos assuntos abordados e atualização para a realidade. Nesta versão constam 65 ferramentas.

System and application availability continues to be a fundamental characteristic of IT services. In recent years our IT Operations team  has placed special focus on this area. Using a combination of goals, metrics, processes, organizational models, communication methods, corrective maintenance, root cause analysis, preventative engineering, automated alerting, and workflow automation significant progress has been made in meeting availability SLAs (Service Level Agreements). This paper presents the background of this work, approach, details of its implementation, and results. A special focus is provided on the use of a classical ITIL view as operationalized in an Agile and DevOps environment.

The main purpose of this paper is to explore how AI and DevOps work together in information technology and also examine its significance to the United States. The research discovered that without artificial intelligence and DevOps it might be very challenging to handle and control the information technology systems, especially in the U.S. In information technology, these two components work together to increase efficiency while doing different operations. The growing quantity of data accessible makes it challenging for DevOps teams to swiftly ingest and assimilate new information while still being able to solve issues for customers [1]. DevOps has become a vital component of information technology once the automation trend has exceeded IT, teams, and other areas. DevOps improves productivity by facilitating software delivery and allowing firms to get the software to market faster, resulting in a more reliable product. In essence, AI-powered technology may be used to address a variety of vital concerns (such as national security). According to the findings, enterprises should shift their DevOps environment to a more AI-led deployment. Because computers can only manage a certain quantity of data at any one moment, artificial intelligence has emerged as a solution for storing, processing, and analyzing massive volumes of data. To comprehend the function AI might play in DevOps, it's necessary to first comprehend the link between AI and DevOps, as well as how that interaction influences AI [1]. Many businesses see the value of Artificial Intelligence and Machine Learning right away. However, since they haven't yet taught the essential understanding needed to effectively harness these technologies, this awareness may be held back. In terms of software development, DevOps software has several inherent flaws that are difficult to solve without the computational capabilities that artificial intelligence systems provide [1]. They are critical to the digital transformation's success.

This paper builds on a presentation provided as part of a recent panel session on ITSM (IT Service Management) Business Value at the NYC itSMF (Service Management Forum) Local Interest Group meeting. The panel presentation explored the definition of Business Value and how ITSM itself could be measured to produce business value. While ITSM and ITIL have been in use for years it often remains a challenge to demonstrate the business value of these methods or even to understand business value itself. This paper expands on the panel discussion on what is meant by business value and how it can be found-if at all-in the context of ITSM development and process improvement.

Index Terms—IT Service Management, ITSM, ITIL, ITIL 4, DevOps, IT Operations, Process Engineering, Process Improvement, Quality, Business Value, Metrics, GQM, Lean, Cost of Quality, Capability Maturity Models.

DevOps Training program is uniquely designed to help you become a certified practitioner by providing hands on training on DevOps tools. This training is highly beneficial for the Continuous Integration, Deployment and Monitoring of the software when it goes under the life cycle development.

This paper traces the history of Maturity Models and their impact on Process Improvement from the early work of Shewhart to their current usage with DevOps. The history of modern process improvement can be traced at least to Shewhart. From his foundational process contributions and those of other innovators a variety of methods and tools to aid in process quality advancement were developed. This paper begins by reviewing those early steps and then focuses on the emergence of Maturity Models in the 1970s with Nolan's initial approach. The broad adoption of Maturity Models that followed through the success of the CMM and then the CMMI approaches is detailed. This then leads to a general survey of additional models developed for such areas as IT Service Management, ITIL, Project Management, Agile Development, DevOps, CERT, and MDM among others. Finally, this paper discusses the application of these models in the support of process improvement and their limitations. Readers of this paper can expect to gain an appreciation for the origins of these models and surrounding methods as well as an ability to conduct comparative analysis of such models to aid in their selection and application.