AI Techniques in the Microservices Life-Cycle: A Systematic Mapping Study

• URL: http://arxiv.org/abs/2305.16092v2
• Date: Mon, 20 Jan 2025 08:52:28 GMT
• Title: AI Techniques in the Microservices Life-Cycle: A Systematic Mapping Study
• Authors: Sergio Moreschini, Shahrzad Pour, Ivan Lanese, Daniel Balouek-Thomert, Justus Bogner, Xiaozhou Li, Fabiano Pecorelli, Jacopo Soldani, Eddy Truyen, Davide Taibi,
• Abstract summary: The use of AI in (MSs) is an emerging field as indicated by a substantial number of surveys. We take an exhaustive approach to reveal all possible connections between the use of AI techniques for improving any quality attribute (QA) of MSs during the DevOps phases.
• Score: 8.026381963838272
• License:
• Abstract: The use of AI in microservices (MSs) is an emerging field as indicated by a substantial number of surveys. However these surveys focus on a specific problem using specific AI techniques, therefore not fully capturing the growth of research and the rise and disappearance of trends. In our systematic mapping study, we take an exhaustive approach to reveal all possible connections between the use of AI techniques for improving any quality attribute (QA) of MSs during the DevOps phases. Our results include 16 research themes that connect to the intersection of particular QAs, AI domains and DevOps phases. Moreover by mapping identified future research challenges and relevant industry domains, we can show that many studies aim to deliver prototypes to be automated at a later stage, aiming at providing exploitable products in a number of key industry domains.

Related papers

• Survey on AI-Generated Media Detection: From Non-MLLM to MLLM [51.91311158085973]
  Methods for detecting AI-generated media have evolved rapidly. General-purpose detectors based on MLLMs integrate authenticity verification, explainability, and localization capabilities. Ethical and security considerations have emerged as critical global concerns.
  arXiv  Detail & Related papers  (2025-02-07T12:18:20Z)
• Collaborative AI in Sentiment Analysis: System Architecture, Data Prediction and Deployment Strategies [3.3374611485861116]
  Large language model (LLM) based artificial intelligence technologies have been a game-changer, particularly in sentiment analysis. However, integrating diverse AI models for processing complex multimodal data and the associated high costs of feature extraction presents significant challenges. This study introduces a collaborative AI framework designed to efficiently distribute and resolve tasks across various AI systems.
  arXiv  Detail & Related papers  (2024-10-17T06:14:34Z)
• How Mature is Requirements Engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directions [5.6818729232602205]
  It is unclear if existing RE methods are sufficient or if new ones are needed to address these challenges. Existing RE4AI research focuses mainly on requirements analysis and elicitation, with most practices applied in these areas. We identified requirements specification, explainability, and the gap between machine learning engineers and end-users as the most prevalent challenges.
  arXiv  Detail & Related papers  (2024-09-11T11:28:16Z)
• Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI [129.08019405056262]
  Embodied Artificial Intelligence (Embodied AI) is crucial for achieving Artificial Intelligence (AGI) MLMs andWMs have attracted significant attention due to their remarkable perception, interaction, and reasoning capabilities. In this survey, we give a comprehensive exploration of the latest advancements in Embodied AI.
  arXiv  Detail & Related papers  (2024-07-09T14:14:47Z)
• A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning [51.7818820745221]
  Underwater image enhancement (UIE) presents a significant challenge within computer vision research. Despite the development of numerous UIE algorithms, a thorough and systematic review is still absent.
  arXiv  Detail & Related papers  (2024-05-30T04:46:40Z)
• Leveraging Multi-AI Agents for Cross-Domain Knowledge Discovery [0.0]
  This study introduces a novel approach to cross-domain knowledge discovery through the deployment of multi-AI agents. Our findings demonstrate the superior capability of domain specific multi-AI agent system in identifying and bridging knowledge gaps.
  arXiv  Detail & Related papers  (2024-04-12T14:50:41Z)
• Bridging MDE and AI: A Systematic Review of Domain-Specific Languages and Model-Driven Practices in AI Software Systems Engineering [1.4853133497896698]
  This study aims to investigate the existing model-driven approaches relying on DSL in support of the engineering of AI software systems. The use of MDE for AI is still in its early stages, and there is no single tool or method that is widely used.
  arXiv  Detail & Related papers  (2023-07-10T14:38:38Z)
• AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges [60.56413461109281]
  Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
  arXiv  Detail & Related papers  (2023-04-10T15:38:12Z)
• Towards Automated Process Planning and Mining [77.34726150561087]
  We present a research project in which researchers from the AI and BPM field work jointly together. We discuss the overall research problem, the relevant fields of research and our overall research framework to automatically derive process models.
  arXiv  Detail & Related papers  (2022-08-18T16:41:22Z)
• Systematic Mapping Study on the Machine Learning Lifecycle [4.4090257489826845]
  The study yields 405 publications published from 2005 to 2020, mapped in 5 different main research topics, and 31 sub-topics. We observe that only a minority of publications focus on data management and model production problems, and that more studies should address the AI lifecycle from a holistic perspective.
  arXiv  Detail & Related papers  (2021-03-11T11:44:23Z)
• Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
  Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations. Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
  arXiv  Detail & Related papers  (2021-01-15T10:43:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.