Accelerating DevOps with Artificial Intelligence Techniques

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Smart System Infrastructure and Applications".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 4124

Special Issue Editors


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Guest Editor
Intelligent Systems Group, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: machine learning; agent technology; cognitive bots; natural language processing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Intelligent Systems Group, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: agents; social simulation; machine learning; social networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

DevOps is a popular software development methodology that connects development, quality control, and technical operations to create a channel for the effective production of high-quality software systems. Although there are several DevOps technologies and tools, the application of DevOps requires software companies to compose and integrate their own set of tools, which is a major obstacle to the application of DevOps.

For instance, Big Data software systems are among the systems for which DevOps is extremely beneficial, as Big Data system architectures are too intricate and distributed to be managed and evolved through traditional software engineering methodologies. However, DevOps’ approaches and tools for Big Data systems still require a great amount of effort. Therefore, the aim of the Special Issue is to collect the most recent innovation in the application of artificial intelligent techniques for accelerating DevOps adoption in the industry. We would like to gather researchers from different disciplines and methodological backgrounds to discuss new ideas, research questions, recent results, and future challenges in this emerging area of research and public interest. Potential topics include but are not limited to:

  • Intelligent automation in DevOps;
  • Machine learning in DevOps;
  • Monitoring and optimization in DevOps;
  • Semantic orchestration in DevOps.

Dr. Álvaro Carrera Barroso
Dr. Carlos A. Iglesias
Dr. J. Fernando Sánchez-Rada
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • DevOps
  • Artificial Intelligence
  • machine learning
  • monitoring
  • automation
  • semantic

Published Papers (1 paper)

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Research

24 pages, 6095 KiB  
Article
FlockAI: A Testing Suite for ML-Driven Drone Applications
by Demetris Trihinas, Michalis Agathocleous, Karlen Avogian and Ioannis Katakis
Future Internet 2021, 13(12), 317; https://doi.org/10.3390/fi13120317 - 16 Dec 2021
Cited by 9 | Viewed by 3045
Abstract
Machine Learning (ML) is now becoming a key driver empowering the next generation of drone technology and extending its reach to applications never envisioned before. Examples include precision agriculture, crowd detection, and even aerial supply transportation. Testing drone projects before actual deployment is [...] Read more.
Machine Learning (ML) is now becoming a key driver empowering the next generation of drone technology and extending its reach to applications never envisioned before. Examples include precision agriculture, crowd detection, and even aerial supply transportation. Testing drone projects before actual deployment is usually performed via robotic simulators. However, extending testing to include the assessment of on-board ML algorithms is a daunting task. ML practitioners are now required to dedicate vast amounts of time for the development and configuration of the benchmarking infrastructure through a mixture of use-cases coded over the simulator to evaluate various key performance indicators. These indicators extend well beyond the accuracy of the ML algorithm and must capture drone-relevant data including flight performance, resource utilization, communication overhead and energy consumption. As most ML practitioners are not accustomed with all these demanding requirements, the evaluation of ML-driven drone applications can lead to sub-optimal, costly, and error-prone deployments. In this article we introduce FlockAI, an open and modular by design framework supporting ML practitioners with the rapid deployment and repeatable testing of ML-driven drone applications over the Webots simulator. To show the wide applicability of rapid testing with FlockAI, we introduce a proof-of-concept use-case encompassing different scenarios, ML algorithms and KPIs for pinpointing crowded areas in an urban environment. Full article
(This article belongs to the Special Issue Accelerating DevOps with Artificial Intelligence Techniques)
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