DevOps Principles Research Articles

Performance and Latency Efficiency Evaluation of Kubernetes Container Network Interfaces for Built-In and Custom Tuned Profiles

In the era of DevOps, developing new toolsets and frameworks that leverage DevOps principles is crucial. This paper demonstrates how Ansible’s powerful automation capabilities can be harnessed to manage the complexity of Kubernetes environments. This paper evaluates efficiency across various CNI (Container Network Interface) plugins by orchestrating performance analysis tools across multiple power profiles. Our performance evaluations across network interfaces with different theoretical bandwidths gave us a comprehensive understanding of CNI performance and overall efficiency, with performance efficiency coming well below expectations. Our research confirms that certain CNIs are better suited for specific use cases, mainly when tuning our environment for smaller or larger network packets and workload types, but also that there are configuration changes we can make to mitigate that. This paper also provides research into how to use performance tuning to optimize the performance and efficiency of our CNI infrastructure, with practical implications for improving the performance of Kubernetes environments in real-world scenarios, particularly in more demanding scenarios such as High-Performance Computing (HPC) and Artificial Intelligence (AI).

The Role of Leadership in Transforming Retail Technology Infrastructure with DevOps

In the fast changing retail technology market, DevOps principles are transforming how firms manage and improve their technological infrastructure. This study examines how leadership may use DevOps to improve retail technology and operations. As retail embraces digital transformation, technological strategies must fit with business goals. This change requires leaders to guide their firms through complicated infrastructure upgrade and operational efficiency. DevOps, which emphasizes communication between development and operations teams, continuous integration and delivery, and automation, has become essential for technological infrastructure agility and efficiency. DevOps benefits retail companies in a competitive world with quick customer behavior and market fluctuations. These include increased deployment frequency, time-to-market, dependability, and scalability. DevOps demands a fundamental change in corporate culture and leadership, not simply new technologies and procedures. This article discusses how successful leadership can promote collaboration, transformation, and DevOps alignment with strategic corporate objectives to achieve this cultural transition. Leadership helps overcome change resistance, foster cross-functional cooperation, and incorporate DevOps into the organization's technology and business operations. Leaders must also address talent shortages, resource limits, and outdated systems that might hinder DevOps rollout.

Efficiency and Cost-Effectiveness in Agile DevOps with Cloud Computing

Abstract This article explores how Agile methodology, DevOps principles, and Cloud Computing fit into the software industry. The synergy study focuses on Axes Software, the oldest logistics software company in Romania. The effects of the association of these technologies during the development cycle of a software product are explored. Both their impact in the operational activities and the transformation of the development mode, as well as the financial impact, are evaluated. In the first part, theoretical aspects of Agile and DevOps ways of working and cloud technologies are explored. The amplification of the potential of the association of the three concepts is examined. With the basis formed, the paper continues with a detailed case study of Axes Software. It looks at the adoption of new technologies and way of working. Challenges and successes encountered are also considered. Key performance indicators such as release frequency, bug incidence (dropped from 72 to 4), and development velocity are examined to validate the effectiveness of this integration. The financial aspects, including initial investment and savings, are also analysed, leading to an assessment of the return on investment. Notably, deployments of new software versions now take two days instead of eleven, while the market witnessed a 275% increase in clients preferring the company’s cloud-hosted solutions over on-premises options. Finally, the effects of the association for software developers are distinguished, emphasizing continuous learning, leadership vision and supporting digital transformation.

Why Startups Outpace Multinationals in Leveraging DevOps

Abstract This paper explores the differential impacts of DevOps across varying organizational structures, specifically analyzing why startups may outpace multinational companies in leveraging the benefits of DevOps methodologies. DevOps drives organizational efficiency and innovation. Comparative studies often examine its implementation in startups versus large-scale enterprises. The literature pinpoints the agility, culture, and size of an organization as key determinants influencing the adoption of DevOps principles. In response, this study employs an extensive questionnaire distributed to DevOps and related professionals to investigate the hypothesis that startups are uniquely positioned to harness DevOps for technological advancement and cultural synergy. The survey gathered quantitative and qualitative data reflecting experiences with DevOps adoption, challenges faced, and the perceived benefits drawn from such practices. The findings reveal that while startups and multinationals share common challenges, the former's organizational agility, innovative mindset, and less encumbered cultural practices offer startups a distinct advantage in the effective assimilation of DevOps practices. This paper contributes to the field by providing evidence supporting the notion that startups possess intrinsic advantages over multinationals in DevOps implementation, chiefly due to their flexibility and lean structures for rapid innovation. By mapping out the relative successes and impediments to DevOps adoption in diverse corporate environments, this research extends the understanding of how organizational characteristics can be leveraged to optimize DevOps strategies in the evolving landscape of software engineering.

DataOps: Bridging the Gap Between Legacy and Modern Systems for Seamless Data Orchestration

The modern enterprise operates in a complex data landscape, where legacy systems coexist with modern, event-driven microservices architectures. This heterogeneity poses significant challenges in data integration, management, and analysis. DataOps, a methodology that applies DevOps principles to the data lifecycle, offers a solution to these challenges. This paper explores the implementation of DataOps in such a hybrid environment, focusing on strategies for integrating and orchestrating data from diverse sources, ensuring data quality, and enabling efficient data-driven decision-making. The paper also highlights the crucial role of Data Lakes and Data Lake houses in facilitating seamless data orchestration, providing a scalable and flexible foundation for storing, processing, and analyzing data from both legacy and modern systems.

Investigating effectiveness and compliance to DevOps policies and practices for managing productivity and quality variability

The Mission Design and Navigation Software (MDN) Group at the Jet Propulsion Laboratory (JPL) develops and continuously maintains software systems critical for NASA deep space missions. Due to limited resources and tight schedules, there is always the temptation to prioritize productivity over quality. However, such quality and productivity variations may be manageable during development, but they are too risky for operational use when there is a time-critical need for repair or enhancement and high reliability is essential.As a result, our process must be both highly productive and maintain high quality (e.g., reliability, maintainability, usability). Inspired by the “quality is free” paradigm, we have instituted six policies established from DevOps principles and practices specifically to address maintenance risk caused by high variability in quality and productivity concerns we encountered in the development phase from quality-productivity tradeoffs.CMMI level 3 requirements mandate that process improvements are monitored and adjusted to ensure that policies are adhered to, practices are performed, and that they are effective in producing the desired results. This article presents a process improvement investigation as to whether the DevOps practices, as currently implemented, are effective in achieving the expected effects and impacts for managing variability in quality and productivity. Additionally, we investigate practical methods to assure compliance to the six policies to determine if any adjustments to policy or practice are needed.For this investigation we focus on our flagship system MONTE. We have over 15 years of reliable and accurate quality and productivity data for MONTE, a critical system currently in continual operation and maintenance. Time series cross-correlation analyses were used to compare process productivity and quality characteristics before and after the implementation of DevOps. From this, we found strong evidence that:• There is a continual maintenance risk due to variability in productivity and quality.• The majority of the DevOps policies and practices are being complied with.• The policies and practices have been effective in managing this risk.

Implementation of DevOps in healthcare systems

The integration of DevOps practices within healthcare systems has emerged as a promising approach to enhance agility, efficiency, and reliability in delivering healthcare services. This systematic review explores the implementation of DevOps methodologies within healthcare contexts, focusing on its impact on quality of care, operational efficiency, and overall system performance. Through a comprehensive analysis of existing literature, this review synthesizes key findings, challenges, and best practices associated with DevOps adoption in healthcare. The review highlights successful case studies, identifies common patterns in DevOps implementation, and examines the role of cultural transformation, automation, and collaboration in fostering successful DevOps practices within healthcare organizations. Additionally, this review discusses the potential benefits and limitations of applying DevOps principles in healthcare settings, offering insights for practitioners, researchers, and policymakers seeking to leverage DevOps to improve healthcare delivery.

Implementation of DevOps in healthcare systems

The integration of DevOps practices within healthcare systems has emerged as a promising approach to enhance agility, efficiency, and reliability in delivering healthcare services. This systematic review explores the implementation of DevOps methodologies within healthcare contexts, focusing on its impact on quality of care, operational efficiency, and overall system performance. Through a comprehensive analysis of existing literature, this review synthesizes key findings, challenges, and best practices associated with DevOps adoption in healthcare. The review highlights successful case studies, identifies common patterns in DevOps implementation, and examines the role of cultural transformation, automation, and collaboration in fostering successful DevOps practices within healthcare organizations. Additionally, this review discusses the potential benefits and limitations of applying DevOps principles in healthcare settings, offering insights for practitioners, researchers, and policymakers seeking to leverage DevOps to improve healthcare delivery.

Implementation of DevOps in healthcare systems

The integration of DevOps practices within healthcare systems has emerged as a promising approach to enhance agility, efficiency, and reliability in delivering healthcare services. This systematic review explores the implementation of DevOps methodologies within healthcare contexts, focusing on its impact on quality of care, operational efficiency, and overall system performance. Through a comprehensive analysis of existing literature, this review synthesizes key findings, challenges, and best practices associated with DevOps adoption in healthcare. The review highlights successful case studies, identifies common patterns in DevOps implementation, and examines the role of cultural transformation, automation, and collaboration in fostering successful DevOps practices within healthcare organizations. Additionally, this review discusses the potential benefits and limitations of applying DevOps principles in healthcare settings, offering insights for practitioners, researchers, and policymakers seeking to leverage DevOps to improve healthcare delivery.

Optimizing the Future: Unveiling the Significance of MLOps in Streamlining the Machine Learning Lifecycle

As we stand on the precipice of 2024, the technological landscape is abuzz with the convergence of two revolutionary forces: MLOps (Machine Learning Operations) and Generative AI. This potent cocktail promises to reshape the very fabric of artificial intelligence (AI), ushering in a new era of streamlined workflows, boundless innovation, and redefined value delivery. MLOps, a paradigm shift inspired by DevOps principles, emerges as the knight in shining armor, poised to vanquish the challenges plaguing the machine learning lifecycle. By fostering seamless collaboration, agile deployment, vigilant monitoring, and efficient management of models, MLOps lays the groundwork for robust organizational AI strategies. In this paper, we delve deep into the intricate world of MLOps, exploring its genesis, its potential to revolutionize business operations, and its pivotal role in shaping the future of AI. Keywords: MLOps, Machine Learning, Artificial Intelligence, DevOps, Automation, Collaboration, Deployment, Monitoring, Management, Generative AI, Innovation, Value Delivery, Business Optimization.

Advancing Smart Cyber Physical System with Self-Adaptive Software

The proposed integration of DevOps and Digital Twins presents a ground-breaking approach to enhancing the resilience and adaptability of Cyber-Physical Systems in the context of Industry 4.0 and the forthcoming Industry 5.0 era. This research explores the synergistic potential of DevOps principles in fostering collaborative and efficient development and maintenance processes for embedded systems and distributed control systems within CPS. Concurrently, the utilization of Digital Twins offers real-time monitoring and simulation capabilities, heralding a shift towards a human-centric approach to CPS operation. The emphasis on self-adaptive systems within CPS anticipates the ability to address dynamic conditions and unforeseen challenges, paving the way for the evolution of resilient and responsive industrial systems. This research explores the integration of DevOps and Digital Twins in the context of Industry 4.0 and anticipates the requirements of Industry 5.0, emphasizing the need for resilient and human-centric Cyber-Physical Systems. This study investigates the integration of DevOps and Digital Twins to enhance the resilience and adaptability of Cyber-Physical Systems in the context of Industry 4.0 and Industry 5.

Establishing Platform Engineering Paradigms: Unleashing a DevOps Metamorphosis

This paper presents a high-level overview of a company’s initiative to transform into a 'DevOps' organization, particularly focusing on the establishment of a Platform Engineering Group. In response to evolving industry dynamics, HealthEdge, a provider of healthcare payor software, embarked on a journey in 2018 to embrace DevOps principles. The Platform Engineering Group undertook the ambitious task of modernizing an antiquated technology stack, transitioning towards a contemporary, Software-as-a-Service (SaaS) paradigm. The article delves into the goals of reshaping an engineering focused organization and navigating the complexities of transitioning a flexible software stack into a self-serving, efficient, and scalable technological framework. While acknowledging the inherent variability in the implementation of such models, the article offers a simplified yet insightful perspective on HealthEdge's approach to addressing specific challenges in their DevOps transformation. Importantly, the paper emphasizes that the presented process/model is based on practical experiences and acquired knowledge rather than being an invented or rigidly adhered-to framework.

DataOps: Applying DevOps Principles to Data Engineering for Improved Data Management

DataOps: Applying DevOps Principles to Data Engineering for Improved Data Management

Evaluating the Impact of DevOps Practice in the U.S. Health Care Systems

The adoption of DevOps practices in the U.S. healthcare system has garnered significant attention in recent years, promising improved efficiency, collaboration, and quality of care delivery. This paper critically evaluates the impact of DevOps practices within the intricate landscape of healthcare systems in the United States. Through a systematic analysis of case studies, empirical research, and industry reports, this study assesses the extent to which DevOps principles have been implemented and their effects on various aspects of healthcare operations. Key metrics such as deployment frequency, lead time, and mean time to recovery are examined alongside qualitative indicators such as cultural shifts, stakeholder satisfaction, and regulatory compliance. Furthermore, challenges and barriers to DevOps adoption in healthcare settings are identified, offering insights into strategies for overcoming these obstacles. By synthesizing empirical evidence and expert insights, this paper provides a comprehensive evaluation of the impact of DevOps practices in enhancing the agility, reliability, and innovation capabilities of U.S. healthcare systems

Integrating Deep Learning with DevOps for Enhanced Predictive Maintenance in the Manufacturing Industry

This research paper explores the transformative integration of Deep Learning (DL) with DevOps methodologies to enhance predictive maintenance in the manufacturing industry. Traditional maintenance strategies often lead to inefficiencies, increased downtime, and operational disruptions. Leveraging the analytical capabilities of DL models and the agile principles of DevOps, our study introduces a comprehensive framework aimed at proactive identification and mitigation of equipment failures. The materials and methods encompass data collection from diverse sources, including sensor data and historical records, coupled with preprocessing techniques to ensure data quality. Selecting appropriate DL models, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), enables accurate predictions of equipment failures. The integration pipeline follows DevOps principles, encompassing continuous integration, automated testing, and continuous deployment. Real-time monitoring and feedback mechanisms ensure model adaptability to evolving operational conditions. Collaboration between data scientists, software engineers, and maintenance teams facilitates a holistic approach to system integration. Addressing challenges of collaboration, model drift, and security considerations, our framework lays the foundation for streamlined, efficient, and adaptive predictive maintenance systems. As manufacturing industries embrace digital transformation, the integration of DL with DevOps emerges as a cornerstone for operational excellence, optimizing asset reliability, and contributing to the sustainable evolution of manufacturing ecosystems.

From DevOps to MLOps: Overview and Application to Electricity Market Forecasting

In the Software Development Life Cycle (SDLC), Development and Operations (DevOps) has been proven to deliver reliable, scalable software within a shorter time. Due to the explosion of Machine Learning (ML) applications, the term Machine Learning Operations (MLOps) has gained significant interest among ML practitioners. This paper explains the DevOps and MLOps processes relevant to the implementation of MLOps. The contribution of this paper towards the MLOps framework is threefold: First, we review the state of the art in MLOps by analyzing the related work in MLOps. Second, we present an overview of the leading DevOps principles relevant to MLOps. Third, we derive an MLOps framework from the MLOps theory and apply it to a time-series forecasting application in the hourly day-ahead electricity market. The paper concludes with how MLOps could be generalized and applied to two more use cases with minor changes.

Information Technology Governance Design in DevOps-Based E-Marketplace Companies Using COBIT 2019 Framework

The E-Marketplace system is a digital place where sellers and buyers meet virtually, and transactions can be processed safely. E-Marketplace companies are oriented toward consumer-centric products and services and operate and have 24/7 non-stop transaction activities. E-Marketplace system development has become the digital transformation in Information Technology (IT) governance that meets the requirements of a system-based automated control with the application of DevOps principles. This study uses a mixed method approach through questionnaires, interviews, expert judgment, and relevant literature reviews related to the e-Marketplace system and IT governance concepts using the COBIT 2019 framework, which has 11 design factors with 40 IT processes to obtain the most appropriate governance model for e-Marketplace companies. The results were obtained in the form of 6 (six) methods in the IT governance model using the DevOps approach, namely the APO03 – Managed Enterprise Architecture, APO04 – Managed Innovation, BAI04 – Managed Availability and Capacity, BAI06 – Managed IT Changes, BAI11 – Managed Projects, and DSS03 – Managed Problems with a score ≥ 75 and the capability level target is at level 4.

An approach to agile management of virtual student teams in smart environment development

ABSTRACT This paper studies the problem of coordination and supervision of virtual teams and their capabilities. The goal is to develop a model suitable for managing virtual student teams specialized in the development of smart environments. The developed model is based on SAFe and DevOps, which when combined provide us with a framework for the evaluation of team capabilities in an academic environment. Additionally, DevOps principles can be more efficiently leveraged through an agile methodology to provide students with a better understanding of continuous value delivery. Through the application of the proposed model, virtual student teams gained practical experience in self-organization and virtual team management while being efficiently monitored and guided through the project lifecycle. Virtual student teams were likewise encouraged to be more agile, as this change in mindset is imperative in business, and as such must also be adopted in academic environments. By incorporating best practices of the corporate environments into the existing curriculum, we have proven that by adopting the proposed model these changes can be feasibly incorporated to the satisfaction of both the students and their future employers.

Decision-Making Taxonomy of DevOps Success Factors Using Preference Ranking Organization Method of Enrichment Evaluation

Due to multitudes factors like rapid change in technology, customer needs, and business trends, the software organizations are facing pressure to deliver quality software on time. To address this concern, the software industry is continually looking the solution to improve processing timeline. Thus, the Development and Operations (DevOps) has gained a wide popularity in recent era, and several organizations are adopting it, to leverage its perceived benefits. However, companies are facing several problems while executing the DevOps practices. The objective of this work is to identify the DevOps success factors that will help in DevOps process improvement. To accomplish this research firstly, a systematic literature review is conducted to identify the factors having positive influence on DevOps. Secondly, success factors were mapped with DevOps principles, i.e., culture, automation, measurement, and sharing. Thirdly, the identified success factors and their mapping were further verified with industry experts via questionnaire survey. In the last step, the PROMETHEE-II method has been adopted to prioritize and investigate logical relationship of success factors concerning their criticality for DevOps process. This study’s outcomes portray the taxonomy of the success factors, which help the experts design the new strategies that are effective for DevOps process improvement.

DevOps Enabled Agile: Combining Agile and DevOps Methodologies for Software Development

The Agile and DevOps software development methodologies have made revolutionary advancements in software engineering. These methodologies vastly improve software quality and also speed up the process of developing software products. However, several limitations have been discovered in the practical implementation of Agile and DevOps, including the lack of collaboration between the development, testing and delivery sectors of different software projects and high skill requirements. This paper presents a solution to bridge the existing gaps between Agile and DevOps methodologies by integrating DevOps principles into Agile to devise a hybrid DevOps Enabled Agile for software development. This study includes the development of a small-scale, experimental pilot project to demonstrate how software development teams can combine the advantages of Agile and DevOps methodologies to fulfill the gaps and provide further improvements to the speed and quality of software development process while maintaining feasible skill requirements.