AIOps: A Multivocal Literature Review

Rijal, Laxmi; Colomo‐Palacios, Ricardo; Sánchez‐Gordón, Mary

doi:10.1007/978-3-030-80821-1_2

Cited by 11 publications

(6 citation statements)

References 35 publications

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“…In this respect, Rijal et al (2022) examined the benefits and challenges an organization can expect when adopting AIOps for IT operations, including network management. Findings indicate that AIOps contributed to task monitoring, task time saving, collaborative work, failure prevention, and mean time to repair (MTTR) reduction.…”

Section: Network Operations and Aimentioning

confidence: 99%

Adopting Artificial Intelligence Technology for Network Operations in Digital Transformation

Min,

Kim

2024

Administrative Sciences

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This study aims to define factors that affect Artificial Intelligence (AI) technology introduction to network operations and analyze the relative importance of such factors. Based on this analysis of critical factors, a rational decision-making framework is suggested to promote network operations with AI technology. As affecting factors were derived based on related previous studies, the study model was designed to consist of 22 attribute factors under 6 key factors: relative advantage, compatibility, top management support, organizational readiness, competitive pressure, and cooperative relation. The questionnaire was designed and analyzed using the Delphi method and Analytics Hierarchy Process (AHP) method based on the Technology–Organization–Environment (TOE) framework. To collect data, a survey was conducted among 30 experts in network operations and AI. The importance of attribute factors was in the order of ‘goals and strategies’, ‘commitment of resources’, ‘leadership competency’, ‘financial readiness’, and ‘technology readiness’. As the importance of factors was analyzed comparatively between the demander group and provider group, organizational factors were important in the demander group. In contrast, technological factors were important in the provider group. In conclusion, there was a difference in perspectives between demanders and providers regarding adopting AI technology to network operations.

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Section: Network Operations and Aimentioning

confidence: 99%

Adopting Artificial Intelligence Technology for Network Operations in Digital Transformation

Min,

Kim

2024

Administrative Sciences

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“…16 In its first version, the acronym was referring to Algorithmic IT Operations, which was later on changed to Artificial intelligence for IT operations. 58…”

Section: Acknowledgmentsmentioning

confidence: 99%

“…Acknowledging the importance and difficulty of maintaining highly available applications in a complex and dynamic environment, the term AIOps was coined by Gartner in 2016 16 . In its first version, the acronym was referring to Algorithmic IT Operations , which was later on changed to Artificial intelligence for IT operations 58 …”

Section: Introductionmentioning

confidence: 99%

Towards AIOps enabled services in continuously evolving software‐intensive embedded systems

Dakkak

Bosch

Olsson

2023

J Software Evolu Process

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Continuous deployment has been practiced for many years by companies developing web‐ and cloud‐based applications. To succeed with continuous deployment, these companies have a strong collaboration culture between the operations and development teams. In addition, these companies use AI, analytics, and big data to assist with time‐consuming postdeployment activities such as continuous monitoring and fault identification. Thus, the term AIOps has evolved to highlight the importance and difficulty of maintaining highly available applications in a complex and dynamic environment. In contrast, software‐intensive embedded systems often provide customer product‐related services, such as maintenance, optimization, and support. These services are critical for these companies as they provide significant revenue and increase customer satisfaction. Therefore, the objective of our study is to gain an in‐depth understanding of the impact of continuous deployment on product‐related services provided by software‐intensive embedded systems companies. In addition, we aim to understand how AIOps can support continuous deployment in the context of software‐intensive embedded systems. To address this objective, we conducted a case study at a large and multinational telecommunications systems provider focusing on the radio access network (RAN) systems for 4G and 5G networks. The company provides RAN products and three complementing services: rollout, optimization, and customer support. The results from the case study show that the boundaries between product‐related services become blurry with continuous deployment. In addition, product‐related services, which were conducted in sequence by independent projects, converge with continuous deployment and become part of the same project. Further, AIOps platforms play an important role in reducing costs and increasing postdeployment activities' efficiency and speed. These results show that continuous deployment has a profound impact on the software‐intensive system's provider service organization. The service organization becomes the connection between the R&D organization and the customer. In order to cope with the increased speed of releases, deployment and postdeployment activities need to be largely automated. AIOps platforms are seen as a critical enabler in managing the increasing complexity without increasing human involvement.

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“…AIOps helps SRE, DevOps, and operations teams improve the quality and reliability of IT services by utilizing intelligent algorithms and monitoring infrastructure to provide a large amount of data [12,13]. AIOps relies on data-driven methods, fully utilizing technologies such as machine learning, big data, data mining, analysis, and visualization to observe the operational status of infrastructure [14], minimize the impact of daily failures, and actively manage the allocation of computer resources [15].…”

Section: Introductionmentioning

confidence: 99%

Learning to Diagnose: Meta-Learning for Efficient Adaptation in Few-Shot AIOps Scenarios

Duan,

Bao,

Bai

et al. 2024

Electronics

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With the advancement of technologies like 5G, cloud computing, and microservices, the complexity of network management systems and the variety of technical components have greatly increased. This rise in complexity has rendered traditional operations and maintenance methods inadequate for current monitoring and maintenance demands. Consequently, artificial intelligence for IT operations (AIOps), which harnesses AI and big data technologies, has emerged as a solution. AIOps plays a crucial role in enhancing service quality and customer satisfaction, boosting engineering productivity, and reducing operational costs. This article delves into the primary tasks involved in AIOps, such as anomaly detection, and log fault analysis and classification. A significant challenge identified in many AIOps tasks is the scarcity of fault sample data, indicating a natural alignment of these tasks with few-shot learning. Inspired by model-agnostic meta-learning (MAML), we propose a new anomaly detector, MAML-KAD, for application in various AIOps tasks. Observations confirm that meta-learning algorithms effectively enhance AIOps tasks, showcasing the wide-ranging application prospects of meta-learning algorithms in the field of AIOps. Moreover, we introduced an AIOps platform that embeds meta-learning within its diagnostic core and features streamlined log collection, caching, and alerting to automate the AIOps workflow.

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AIOps: A Multivocal Literature Review

Cited by 11 publications

References 35 publications

Adopting Artificial Intelligence Technology for Network Operations in Digital Transformation

Adopting Artificial Intelligence Technology for Network Operations in Digital Transformation

Towards AIOps enabled services in continuously evolving software‐intensive embedded systems

Learning to Diagnose: Meta-Learning for Efficient Adaptation in Few-Shot AIOps Scenarios

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