441 research outputs found
AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges
Artificial Intelligence for IT operations (AIOps) aims to combine the power
of AI with the big data generated by IT Operations processes, particularly in
cloud infrastructures, to provide actionable insights with the primary goal of
maximizing availability. There are a wide variety of problems to address, and
multiple use-cases, where AI capabilities can be leveraged to enhance
operational efficiency. Here we provide a review of the AIOps vision, trends
challenges and opportunities, specifically focusing on the underlying AI
techniques. 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. We discuss the problem
formulation for each task, and then present a taxonomy of techniques to solve
these problems. We also identify relatively under explored topics, especially
those that could significantly benefit from advances in AI literature. We also
provide insights into the trends in this field, and what are the key investment
opportunities
A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics
A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques
Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G
The next wave of wireless technologies is proliferating in connecting things
among themselves as well as to humans. In the era of the Internet of things
(IoT), billions of sensors, machines, vehicles, drones, and robots will be
connected, making the world around us smarter. The IoT will encompass devices
that must wirelessly communicate a diverse set of data gathered from the
environment for myriad new applications. The ultimate goal is to extract
insights from this data and develop solutions that improve quality of life and
generate new revenue. Providing large-scale, long-lasting, reliable, and near
real-time connectivity is the major challenge in enabling a smart connected
world. This paper provides a comprehensive survey on existing and emerging
communication solutions for serving IoT applications in the context of
cellular, wide-area, as well as non-terrestrial networks. Specifically,
wireless technology enhancements for providing IoT access in fifth-generation
(5G) and beyond cellular networks, and communication networks over the
unlicensed spectrum are presented. Aligned with the main key performance
indicators of 5G and beyond 5G networks, we investigate solutions and standards
that enable energy efficiency, reliability, low latency, and scalability
(connection density) of current and future IoT networks. The solutions include
grant-free access and channel coding for short-packet communications,
non-orthogonal multiple access, and on-device intelligence. Further, a vision
of new paradigm shifts in communication networks in the 2030s is provided, and
the integration of the associated new technologies like artificial
intelligence, non-terrestrial networks, and new spectra is elaborated. Finally,
future research directions toward beyond 5G IoT networks are pointed out.Comment: Submitted for review to IEEE CS&
5G-PPP Software Network Working Group:Network Applications: Opening up 5G and beyond networks 5G-PPP projects analysis, Version 2
It is expected that the communication fabric and the way network services are consumed will evolve towards 6G, building on and extending capabilities of 5G and Beyond networks. Service APIs, Operation APIs, Network APIs are different aspects of the network exposure, which provides the communication service providers a way to monetize the network capabilities. Allowing the developer community to use network capabilities via APIs is an emerging area for network monetization. Thus, it is important that network exposure caters for the needs of developers serving different markets, e.g., different vertical industry segments. The concept of “Network Applications” is introduced following this idea. It is defined as a set of services that provides certain functionalities to verticals and their associated use cases. The Network Applications is more than the introduction of new vertical applications that have interaction capabilities. It refers to the need for a separate middleware layer to simplify the implementation and deployment of vertical systems on a large scale. Specifically, third parties or network operators can contribute to Network Applications, depending on the level of interaction and trust. In practice, a Network Application uses the exposed APIs from the network and can either be integrated with (part of) a vertical application or expose its APIs (e.g., service APIs) for further consumption by vertical applications. This paper builds on the findings of the white paper released in 2022. It targets to go into details about the implementations of the two major Network Applications class: “aaS” and hybrid models. It introduces the Network Applications marketplace and put the light on technological solution like CAMARA project, as part of the standard landscape. <br/
Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review
This systematic literature review comprehensively examines the application of
Large Language Models (LLMs) in forecasting and anomaly detection, highlighting
the current state of research, inherent challenges, and prospective future
directions. LLMs have demonstrated significant potential in parsing and
analyzing extensive datasets to identify patterns, predict future events, and
detect anomalous behavior across various domains. However, this review
identifies several critical challenges that impede their broader adoption and
effectiveness, including the reliance on vast historical datasets, issues with
generalizability across different contexts, the phenomenon of model
hallucinations, limitations within the models' knowledge boundaries, and the
substantial computational resources required. Through detailed analysis, this
review discusses potential solutions and strategies to overcome these
obstacles, such as integrating multimodal data, advancements in learning
methodologies, and emphasizing model explainability and computational
efficiency. Moreover, this review outlines critical trends that are likely to
shape the evolution of LLMs in these fields, including the push toward
real-time processing, the importance of sustainable modeling practices, and the
value of interdisciplinary collaboration. Conclusively, this review underscores
the transformative impact LLMs could have on forecasting and anomaly detection
while emphasizing the need for continuous innovation, ethical considerations,
and practical solutions to realize their full potential
Towards Deterministic Communications in 6G Networks: State of the Art, Open Challenges and the Way Forward
Over the last decade, society and industries are undergoing rapid
digitization that is expected to lead to the evolution of the cyber-physical
continuum. End-to-end deterministic communications infrastructure is the
essential glue that will bridge the digital and physical worlds of the
continuum. We describe the state of the art and open challenges with respect to
contemporary deterministic communications and compute technologies: 3GPP 5G,
IEEE Time-Sensitive Networking, IETF DetNet, OPC UA as well as edge computing.
While these technologies represent significant technological advancements
towards networking Cyber-Physical Systems (CPS), we argue in this paper that
they rather represent a first generation of systems which are still limited in
different dimensions. In contrast, realizing future deterministic communication
systems requires, firstly, seamless convergence between these technologies and,
secondly, scalability to support heterogeneous (time-varying requirements)
arising from diverse CPS applications. In addition, future deterministic
communication networks will have to provide such characteristics end-to-end,
which for CPS refers to the entire communication and computation loop, from
sensors to actuators. In this paper, we discuss the state of the art regarding
the main challenges towards these goals: predictability, end-to-end technology
integration, end-to-end security, and scalable vertical application
interfacing. We then present our vision regarding viable approaches and
technological enablers to overcome these four central challenges. Key
approaches to leverage in that regard are 6G system evolutions, wireless
friendly integration of 6G into TSN and DetNet, novel end-to-end security
approaches, efficient edge-cloud integrations, data-driven approaches for
stochastic characterization and prediction, as well as leveraging digital twins
towards system awareness.Comment: 22 pages, 8 figure
Public and non-public network integration for 5Growth Industry 4.0 use cases
5G is playing a paramount role in the digital transformation of the industrial sector, offering high-bandwidth, reliable, and low-latency wireless connectivity to meet the stringent and critical performance requirements of manufacturing processes. This work analyzes the applicability of 5G technologies as key enablers to support, enhance, and even enable novel advances in Industry 4.0. It proposes a complete 5G solution for two real-world Industry 4.0 use cases related to metrology and quality control. This solution uses 5Growth to ease and automate the management of vertical services over a soft-ware-defined network and network function virtualization based 5G mobile transport and computing infrastructure, and to aid the integration of the verticals' private 5G network with the public network. Finally, a validation campaign assesses the applicability of the proposed solution to support the performance requirements (especially latency and user data rate) of the selected use cases, and evaluates its efficiency regarding vertical service setup time across different domains in less than three minutes.This work has been partially supported by the EC H2020 5GPPP 5Growth project (Grant 856709) and the H2020 5G-EVE project (Grant 815074)
Cloud Based IoT Architecture
The Internet of Things (IoT) and cloud computing have grown in popularity over the past decade as the internet becomes faster and more ubiquitous. Cloud platforms are well suited to handle IoT systems as they are accessible and resilient, and they provide a scalable solution to store and analyze large amounts of IoT data. IoT applications are complex software systems and software developers need to have a thorough understanding of the capabilities, limitations, architecture, and design patterns of cloud platforms and cloud-based IoT tools to build an efficient, maintainable, and customizable IoT application. As the IoT landscape is constantly changing, research into cloud-based IoT platforms is either lacking or out of date. The goal of this thesis is to describe the basic components and requirements for a cloud-based IoT platform, to provide useful insights and experiences in implementing a cloud-based IoT solution using Microsoft Azure, and to discuss some of the shortcomings when combining IoT with a cloud platform
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