IT Operations Research Articles

AIOps and DevOps : Catalysts of Digital Transformation in the Age of Automated Operations

This article examines the pivotal roles of Artificial Intelligence for IT Operations (AIOps) and Development Operations (DevOps) as key drivers of digital transformation in contemporary organizations. As businesses increasingly rely on complex, distributed IT systems, the integration of AIOps and DevOps emerges as a critical strategy for maintaining operational efficiency, scalability, and innovation. Through a comprehensive analysis of current literature and industry case studies, we explore how these technologies are reshaping the landscape of IT operations, accelerating software delivery cycles, and enabling more resilient and adaptive infrastructures. The article delves into the evolving nature of release engineering, where automation and machine learning are becoming central to ensuring agile and robust IT systems. Furthermore, we critically assess the ethical implications of automated decision-making in operations, including issues of transparency, bias, and job displacement. Our findings suggest that while AIOps and DevOps offer significant benefits in terms of operational optimization and cost reduction, their successful implementation requires careful consideration of organizational culture, skill development, and ethical guidelines. This article contributes to the growing body of knowledge on digital transformation strategies and provides insights for practitioners navigating the complex intersection of artificial intelligence, automation, and IT operations.

Agentic AI in Predictive AIOps: Enhancing IT Autonomy and Performance

The integration of Agentic Artificial Intelligence (AI) within Predictive AIOps (Artificial Intelligence for IT Operations) is revolutionizing the management of IT systems, significantly enhancing IT autonomy and performance (Smith & Johnson, 2023). This article explores the potential of Agentic AI to empower AIOps platforms in proactively predicting, identifying, and resolving system issues. By leveraging predictive analytics and machine learning, AIOps not only enhances operational efficiency but also minimizes downtime and supports autonomous decision-making in complex IT environments (Lee et al., 2022). We examine the key roles that Agentic AI plays in improving performance metrics, optimizing resource allocation, and reducing the reliance on human intervention in critical system operations (Garcia & Patel, 2024). Additionally, this study investigates the implications for IT infrastructure scalability, long-term resilience, and the evolution toward self-governing systems (Chen, 2023). The findings underscore the transformative impact of Agentic AI on future IT operations, showcasing its potential to foster higher levels of automation and operational intelligence.

From Monitoring to Observability: A Paradigm Shift in IT Operations

This article explores the paradigm shift from traditional monitoring to observability in IT operations, driven by the increasing complexity of modern distributed systems, data overload, evolving cybersecurity threats, and the need for proactive problem-solving. Observability platforms, built on the four pillars of events, metrics, traces, and logs, provide comprehensive insights into system behavior and performance. Adopting observability practices offers numerous benefits, including automated discovery of system components, dependency mapping, topology visualization, correlative intelligence, faster root cause diagnosis, and more accurate anomaly detection through auto-baselining. Organizations that embrace observability report significant improvements in incident response times, system reliability, and overall operational efficiency. The article guides successfully transitioning to an observability-focused approach by embracing automation, fostering a culture of observability, implementing the right tools, and leveraging AI and machine learning for advanced analytics and insights.

AIOps in Action: Automating AI Deployment and Management of Large Language Models for Scalable and Ethical Operations

This comprehensive article explores the transformative role of Artificial Intelligence for IT Operations (AIOps) in the deployment and management of Large Language Models (LLMs). It delves into the automation strategies that streamline LLM deployment, including data preparation, model training optimization, and continuous integration and deployment practices. The article addresses the unique challenges in LLM management, such as resource allocation complexities and latency issues, presenting AIOps-driven solutions that leverage predictive analytics and dynamic scaling techniques. A significant focus is placed on the synergies between AIOps and MLOps, highlighting how their integration enhances model versioning, governance, and performance monitoring. The article also examines the critical aspects of real-time monitoring and incident management, showcasing how AIOps enables sophisticated anomaly detection and automated incident response. Ethical considerations in AIOps-driven LLM deployment are thoroughly discussed, emphasizing the importance of bias mitigation, transparency, and accountability. Looking ahead, the article explores future trends in AIOps for LLM management, including advancements in automation technologies and their implications for operational scalability and efficiency. Through a combination of theoretical analysis and practical case studies, this article provides a comprehensive overview of how AIOps is revolutionizing the landscape of AI operations, offering insights into both the current state and future potential of automated, scalable, and ethically responsible LLM management.

Pengaruh Sumber Daya Manusia dan Pemanfaatan Teknologi Informasi Pada Sistem Keuangan Desa

The rapid development in the development of accounting information systems, such as Siskeudes, was initially not integrated online with the local government. However, it was later adapted to be integrated online. The purpose of this study was to determine the influence of human resources and the use of information technology on the village financial system. The population in this study were village heads, village secretaries, village treasurers, IT operators, BPD, LPM, and community leaders in the city of Denpasar who made financial reports, namely 27 villages. The sample collection used was the purposive sampling method. The sample in this study was determined as many as 4 respondents in each of the 27 villages, so that 102 respondents were obtained. The data analysis technique used was Descriptive Statistics analysis, classical assumption tests and multiple regression analysis. The results of the analysis showed that Human resources have a significant positive effect on the village financial system in Denpasar City, having an impact on improving the village financial system in Denpasar City. The second result of the use of information technology has a significant positive effect on the village financial system in Denpasar City. The increasing use of information technology will have an impact on improving the village financial system in Denpasar City. The implications of this study are that the villages in Denpasar City in terms of human resources are expected to be able to improve their human resources by providing technical training to improve skills in preparing financial reports so that village officials can carry out their duties and can rely on appropriate technological knowledge and will improve the village financial system. In terms of the use of information technology, it is expected to maximize the use of existing information technology, and use software that is in accordance with applicable government regulations so that it can facilitate existing work and ultimately maximize the village financial system in Denpasar City.

Industry-Specific Applications of Site Reliability Engineering

This article explores the application of Site Reliability Engineering (SRE) principles across various industries, demonstrating how SRE practices are being adapted beyond traditional tech companies to sectors such as healthcare, finance, and e-commerce. Originally developed by Google, SRE has evolved into a set of practices that bridge the gap between software development and IT operations, focusing on system reliability, performance optimization, and proactive problem-solving. The article examines specific case studies in each industry, highlighting the challenges faced during SRE implementation and the tangible benefits realized by organizations. By analyzing the impact of SRE on critical systems such as Electronic Health Records, high-frequency trading platforms, and e-commerce infrastructure, the article illustrates how SRE principles can be effectively applied to improve system reliability, enhance customer satisfaction, and increase operational efficiency across diverse business contexts.

Continuous Service Improvement in IT Operations through Predictive Analytics

In the evolving landscape of IT operations, continuous service improvement is essential for maintaining high performance, reliability, and customer satisfaction. Predictive analytics, leveraging advanced data analysis techniques and machine learning algorithms, offers a transformative approach to enhancing IT service management. This research paper explores the integration of predictive analytics into IT operations to drive continuous service improvement. It investigates how predictive models can forecast potential issues, optimize resource allocation, and enhance decision-making processes, ultimately leading to improved operational efficiency and service quality. The study begins with an overview of traditional IT service management practices and the limitations they face in adapting to dynamic and complex IT environments. Conventional approaches often rely on reactive problem-solving and periodic reviews, which can lead to inefficiencies and missed opportunities for proactive intervention. Predictive analytics offers a paradigm shift by utilizing historical data and real-time information to predict future outcomes, enabling organizations to address potential problems before they impact operations.

Proactive IT Operations: A Comparative Analysis of Event Management in ServiceNow and BMC Helix

Abstract: This research paper presents a comprehensive comparison of the Event Management applications offered by ServiceNow and BMC Helix. The study examines key features, event detection and correlation capabilities, automation and orchestration, integration with other ITSM processes, and analytics tools of both solutions. By evaluating their strengths and weaknesses in managing IT events, this paper aims to provide insights for organizations seeking to enhance their proactive IT operations and service delivery.

Continuous Service Improvement in IT Operations through Predictive Analytics

In the evolving landscape of IT operations, continuous service improvement is essential for maintaining high performance, reliability, and customer satisfaction. Predictive analytics, leveraging advanced data analysis techniques and machine learning algorithms, offers a transformative approach to enhancing IT service management. This research paper explores the integration of predictive analytics into IT operations to drive continuous service improvement. It investigates how predictive models can forecast potential issues, optimize resource allocation, and enhance decision-making processes, ultimately leading to improved operational efficiency and service quality. The study begins with an overview of traditional IT service management practices and the limitations they face in adapting to dynamic and complex IT environments. Conventional approaches often rely on reactive problem-solving and periodic reviews, which can lead to inefficiencies and missed opportunities for proactive intervention. Predictive analytics offers a paradigm shift by utilizing historical data and real-time information to predict future outcomes, enabling organizations to address potential problems before they impact operations.

Artificial intelligence-driven real-world battery diagnostics

Addressing real-world challenges in battery diagnostics, particularly under incomplete or inconsistent boundary conditions, has proven difficult with traditional methodologies such as first-principles and atomistic calculations. Despite advances in data assimilation techniques, the overwhelming volume and diversity of data, coupled with the lack of universally accepted models, underscore the limitations of these traditional approaches. Recently, deep learning has emerged as a highly effective tool in overcoming persistent issues in battery diagnostics by adeptly managing expansive design spaces and discerning intricate, multidimensional correlations. This approach resolves challenges previously deemed insurmountable, especially with lost, irregular, or noisy data through the design of specialized network architectures that adhere to physical invariants. However, gaps remain between academic advancements and their practical applications, including challenges in explainability and the computational costs associated with AI-driven solutions. Emerging technologies such as explainable artificial intelligence (XAI), AI for IT operations (AIOps), lifelong machine learning to mitigate catastrophic forgetting, and cloud-based digital twins open new opportunities for intelligent battery life-cycle assessment. In this perspective, we outline these challenges and opportunities, emphasizing the potential of innovative technologies to transform battery diagnostics, as demonstrated by our recent practice and the progress made in the field. This includes promising achievements in both academic and industry field demonstrations in modeling and forecasting the dynamics of multiphysics and multiscale battery systems. These systems feature inhomogeneous cascades of scales, informed by our physical, electrochemical, observational, empirical, and/or mathematical understanding of the battery system. Through data assimilation efforts, meticulous craftsmanship, and elaborate implementations—and by considering the wealth and spatio-temporal heterogeneity of available data—such AI-based intelligent learning philosophies have great potential to achieve better accuracy, faster training, and improved generalization.

Leadership in Technology: Strategies for Effective Global IT Operations Management

Leadership in Technology: Strategies for Effective Global IT Operations Management

Artificial intelligence-powered decision support system for operational decision-making in the ICT department of a selected African university

This paper aims to explore the effect of an artificial intelligence-enabled decision support system (AIDSS) on decision-making processes within a university setting, focusing on the ICT department of a selected South African university. The research objectives revolve around the effectiveness of AIDSS in enhancing decision-making in higher education institutions. Convenience sampling was employed in this study and a questionnaire administered to 28 participants. These methods were chosen to gather insights into the practical application of AI technology in the context of IT operational decision-making. The major findings of this research reveal that AIDSS has the potential to significantly improve decision-making processes in higher education. Despite these advancements, the research acknowledges the ethical and societal implications arising from AI integration, underscoring the importance of achieving a balance between technical advancement and the upholding of human values. As a major policy implication, this study underscores the importance of embracing AI technologies in higher education institutions to enhance decision-making processes, thus improving the efficiency and effectiveness of IT operations. The original contribution of this research lies in its exploration of the practical use of AIDSS in South African higher education, offering insights into how AI can be harnessed to advance decision-making.

Enhancing IT Infrastructure with Fiber Optics: Revolutionizing Data Flow and Beyond

Fiber optics technology has emerged as a transformative solution in modern IT infrastructure, significantly enhancing data flow and other critical aspects of digital communication. This article explores the latest advancements in fiber optics and how they can be leveraged to improve IT infrastructure. By delving into the benefits of fiber optics, such as increased bandwidth, reduced latency, and improved security, this paper highlights how these technologies can revolutionize data transmission and overall IT operations.

Design and implementation of real-time log analysis based on ELK

With the rapid development of the IT industry, the integrated solution of real-time log analysis has become a member of the new generation of IT operation and maintenance structure. The traditional manual log analysis system is no longer suitable for the rapid development of the operation and maintenance system. The log analysis software commonly used in enterprises includes Elasticsearch, Logstash, and Kibana systems. The advantage is that it is free and open source. "Design and implementation of real-time log analysis based on ELK" was born for system maintenance and developers to understand server software and hardware information through logs, and check the cumbersome operations in the configuration process. Because frequent analysis of the log can understand the load of the server, performance security, so as to take timely measures to correct errors. If you manage dozens or hundreds of servers, there is no centralized log management system to collect and summarize the logs on all servers, which causes great difficulties for operation and maintenance managers. "Design and implementation of real-time log analysis based on ELK" uses Elasticsearch, Logstash, and Kibana to achieve unified collection and classification of server logs, which can solve the defects of traditional log analysis platforms, improve the efficiency of server application positioning problems, and help enterprises solve data center problems running problems of related applications.

Towards Carbon Footprint Management in Hybrid Multicloud

Enterprises today aspire to optimize the operating costs and carbon footprint (CFP) of their IT operations jointly without compromising their business imperatives. This has given rise to a hybrid approach in which enterprises retain the dynamic choice to leverage private data centers and one or more public clouds in conjunction. While cloud service providers (CSPs) have long provided APIs for estimating, reconciling, and optimizing operating costs, they have only recently started exposing APIs related to CFP. Indeed, this is a step in the right direction. Nevertheless, our analyses of these APIs reveals many gaps that need to be addressed to facilitate sizing and placement decisions that can factor in carbon. First, there is a lack of standardized, transparent methodology for CFP quantification across different CSPs. Second, the coarse granularity of the CFP data provided today can help with post-facto reporting but is not suitable for proactive fine-grained optimization. Last, enterprises themselves are unable to independently compute the current CFP or estimate potential CFP savings since CSPs do not share the required power usage data. To address these gaps, enterprises have started developing their own carbon assessment methodologies and tools to estimate the CFP of workloads running on public clouds using the available user-facing APIs. These systems hold the promise for an independent and unbiased evaluation and estimation of relative savings between different deployment options by cloud users. We describe and analyze the details of CSP-native carbon-reporting tools and their quantification methodology, and the "outside-of-the-cloud" estimation approaches. Finally, we present opportunities for future research in the direction of trustworthy, fine-grained, public cloud workload CFP estimation, which is a prerequisite for meaningful realization of carbon optimization.

Anomaly detection for key performance indicators by fusing self-supervised spatio-temporal graph attention networks

With the development of Artificial Intelligence for IT Operations (AIOps), numerous software and services are monitored by Key Performance Indicators (KPIs) collection components. Multivariate KPIs, as a type of time series data, are essential for effective management of the entity's service quality. In recent years, deep learning methods have made great improvements in the anomaly detection of multivariate time series; however, existing methods have not fully considered how to explicitly capture the correlation between multivariate time series in the feature dimension and temporal dimension, resulting in inevitable abnormal false positives. Therefore, this paper proposes a self-supervised multivariate KPIs anomaly detection method MAD-STA that combines graph structure learning and spatio-temporal GAT (Graph Attention Network). In the feature dimension, MAD-STA introduces a node embedding mechanism for graph structure learning and then uses the feature-oriented GAT layer to compute the graph attention coefficient to obtain the correlation between different KPIs. In the temporal dimension, MAD-STA uses the time-oriented GAT layer to compute attention weights between correlated timestamps, and the GRU-based VAE encoder captures long-term dependence to extract more comprehensive temporal feature representations. Finally, MAD-STA uses GRU-based VAE decoder to reconstruct the captured high-level features and achieves efficient anomaly detection and localization by calculating the anomaly score of multiple KPIs. Compared with the baseline methods on multiple data sets, the experimental results show that the anomaly detection accuracy of MAD-STA is better than that of the baseline method. Especially on the KPI data sets of the two server clusters of SMD and CKM, MAD-STA improves the performance and the F1 comprehensive index compared with the best baseline method. In addition, MAD-STA performs well on anomaly false positive rate and has excellent interpretability, which can be used to assist anomaly diagnosis and root cause index analysis.

DevSecOps: Improving Software Development Life Cycle

In the rapidly evolving landscape of software development, the seamless integration of development, IT operations, and security teams is vital to ensuring the creation of secure and robust applications. Unfortunately, organizations currently face a significant challenge in effectively bridging the gap between these teams during the application development process. This disconnect results in critical security threats that pose substantial risks to the organization’s applications, data, and customers. This review paper examines existing literature to assist emerging researchers and students in revitalizing research papers.

A Comprehensive Survey on ServiceNow for IT Service Management

This paper presents a comprehensive survey on the utilization of ServiceNow in IT Service Management (ITSM), fo- cusing on its adoption, benefits, challenges, and future directions. The study employs a systematic literature review and industry reports to analyze the impact of ServiceNow on ITSM practices across various sectors. Key findings reveal that ServiceNow sig- nificantly enhances ITSM processes through automation, stream- lining workflows, and improving service delivery. The survey also identifies common challenges in implementing ServiceNow, such as resistance to change and integration difficulties with legacy systems, and proposes strategies for overcoming these obstacles. Furthermore, the paper explores emerging trends in ITSM, including the integration of artificial intelligence (AI) and machine learning (ML), and discusses how ServiceNow is positioned to leverage these technologies for future growth. The study concludes by emphasizing the critical role of ServiceNow in facilitating digital transformation and enhancing organizational efficiency in ITSM. Keywords —ITSM, ServiceNow, ITIL, Cloud Computing, Dig- ital Transformation, IT Operations Management.

Enterprise cloud resource optimization and management based on cloud operations

The so-called automated operation and maintenance refers to a large number of repetitive tasks in daily IT operations (from simple daily checks, configuration changes and software installation to organizational scheduling of the entire change process) from manual execution in the past to standardized, streamlined and automated operations. This article delves into the realm of enterprise cloud resource optimization and management, leveraging automated operations (autoOps) as a fundamental strategy. As industries like banking witness exponential growth and innovation in IT systems, the complexity of managing resources escalates. Automated operations have emerged as a critical component, transitioning from manual interventions to encompass standardization, workflow optimization, and architectural enhancements. Through real-world deployments and theoretical frameworks, it elucidates effective strategies for optimizing and governing enterprise cloud resources, thereby enhancing efficiency, security, and resilience in IT operations.

Enterprise cloud resource optimization and management based on cloud operations

The so-called automated operation and maintenance refers to a large number of repetitive tasks in daily IT operations (from simple daily checks, configuration changes and software installation to organizational scheduling of the entire change process) from manual execution in the past to standardized, streamlined and automated operations. This article delves into the realm of enterprise cloud resource optimization and management, leveraging automated operations (autoOps) as a fundamental strategy. As industries like banking witness exponential growth and innovation in IT systems, the complexity of managing resources escalates. Automated operations have emerged as a critical component, transitioning from manual interventions to encompass standardization, workflow optimization, and architectural enhancements. Through real-world deployments and theoretical frameworks, it elucidates effective strategies for optimizing and governing enterprise cloud resources, thereby enhancing efficiency, security, and resilience in IT operations.