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Optimizing network performance and quality of service with AI-driven solutions for future telecommunications
Abstract
This paper investigates the application of AI-driven solutions to enhance network performance and Quality of Service (QoS) in future telecommunications. As the demand for higher bandwidth and seamless connectivity grows, traditional network management approaches face significant challenges in meeting these requirements. The study aims to address these challenges by leveraging artificial intelligence (AI) technologies, such as machine learning, neural networks, and predictive analytics. The research methodology involves a comprehensive review of current literature, case studies, and experimental analysis of AI implementations in telecommunications. We explore various AI techniques for network optimization, including traffic prediction, anomaly detection, resource allocation, and automated network maintenance. Through these methods, the study identifies the key benefits and potential risks associated with AI-driven network management. Key findings highlight the significant improvements in network efficiency, reduced latency, enhanced fault detection, and overall better QoS achieved through AI integration. AI-driven solutions enable dynamic and adaptive network configurations, ensuring optimal performance even under varying traffic conditions and unexpected disruptions. Additionally, the predictive capabilities of AI help in preemptively addressing network issues before they impact users, thus maintaining high QoS standards. The paper concludes that AI-driven solutions present a promising avenue for the future of telecommunications, offering substantial enhancements in network performance and QoS. However, it also emphasizes the need for robust AI models, continuous monitoring, and ethical considerations to mitigate potential risks. The findings underscore the transformative potential of AI in shaping the next generation of telecommunications infrastructure, ensuring reliable and high-quality connectivity for users.
... Infrastructure preparation (4.5 months), system deployment (5.2 months), and optimization (3.8 months) are described in the research under a three-phase implementation approach. Businesses using this strategy see a 56% increase in system acceptance rates and a 41% decrease in integration problems [10]. Projects involving the application of artificial intelligence depend on data governance and quality control as main successes. ...
... Three stages-controlled testing (45 days), restricted deployment (75 days), and fullscale implementation (120 days)-form the validation process the paper describes. Following these guidelines helps companies to increase model dependability across several network situations and report a 62% decrease in post-deployment problems [10]. Implementing success depends much on organizational adaption and change management techniques. ...
... Training and Change Management Impact Metrics[9,10] ...
Emphasizing the change from reactive to proactive maintenance techniques, this article investigates the transforming effect of artificial intelligence-driven predictive maintenance systems in the telecommunications sector. By means of a review of present implementations and industry practices, it is examined how artificial intelligence algorithms interpret network operational data to forecast possible failures, optimize maintenance schedules, and improve network dependability. The integration of machine learning models for pattern detection in network performance measurements, equipment sensor readings, and historical maintenance data is investigated in this work. This article shows that predictive maintenance driven by artificial intelligence greatly lowers running costs, causes less disturbance of services, and increases equipment lifetime. Although stressing the advantages, this article also covers implementation issues, including organizational adaptation needs and data quality issues. The article ends with looking at new developments in predictive maintenance, including edge computing integration and autonomous maintenance systems, so offering ideas on the future direction of telecom network management.
This article comprehensively analyzes artificial intelligence-driven approaches to traffic prediction and congestion control in Cloud-Native Functions (CNFs) and Virtual Network Functions (VNFs) based networks. This article examines recent advancements in predictive analytics and dynamic resource allocation, focusing on implementing deep learning frameworks such as Long Short-Term Memory (LSTM) networks for traffic pattern forecasting. This article demonstrates how continuous learning models and real-time telemetry data integration enable adaptive network responses to fluctuating traffic conditions. This article indicates that AI-enhanced load balancing and traffic shaping techniques significantly improve network performance, achieving a more efficient distribution of resources across network nodes while maintaining consistent Quality of Service (QoS). This article highlights the transformative potential of these innovations in meeting the demanding requirements of 5G networks and beyond, offering insights into cost-effective resource management strategies and scalable network solutions. Experimental results show substantial improvements in latency reduction and resource utilization efficiency, presenting a promising direction for next-generation telecom service optimization.
This study delves into the integration of Artificial Intelligence (AI) in cybersecurity measures within smart cities, aiming to uncover both the challenges and opportunities this fusion presents. With the burgeoning reliance on interconnected digital infrastructures and the vast data ecosystems within urban environments, smart cities are increasingly susceptible to sophisticated cyber threats. Through a systematic literature review and content analysis, this research identifies the unique cybersecurity vulnerabilities faced by smart cities and evaluates how AI technologies can fortify urban cybersecurity frameworks. The methodology encompasses a comprehensive review of recent scholarly articles, industry reports, and case studies to assess the role of AI in enhancing threat detection, response, and prevention mechanisms. Key findings reveal that AI-driven cybersecurity solutions significantly enhance the resilience of smart cities against cyber threats by providing advanced analytical capabilities and real-time threat intelligence. However, the study also highlights the critical need for robust ethical and privacy considerations in the deployment of AI technologies. Strategic recommendations are provided for policymakers, urban planners, and technology leaders, emphasizing the importance of integrating secure AI-enabled infrastructure and fostering public-private partnerships. The study concludes with suggestions for future research directions, focusing on the ethical implications of AI in cybersecurity and the development of scalable AI solutions for diverse urban contexts. Keywords: Artificial Intelligence, Cybersecurity, Smart Cities, Urban Resilience.
In the landscape of modern software development, the demand for scalability and resilience has become paramount, particularly with the rapid growth of online services and applications. Cloud-native technologies have emerged as a transformative force in addressing these challenges, offering dynamic scalability and robust resilience through innovative architectural approaches. This paper presents a comprehensive review of leveraging cloud-native technologies to enhance scalability and resilience in software development. The review begins by examining the foundational concepts of cloud-native architecture, emphasizing its core principles such as containerization, microservices, and declarative APIs. These principles enable developers to build and deploy applications that can dynamically scale based on demand while maintaining high availability and fault tolerance. Furthermore, the review explores the key components of cloud-native ecosystems, including container orchestration platforms like Kubernetes, which provide automated management and scaling of containerized applications. Additionally, it discusses the role of service meshes in enhancing resilience by facilitating secure and reliable communication between microservices. Moreover, the paper delves into best practices and patterns for designing scalable and resilient cloud-native applications, covering topics such as distributed tracing, circuit breaking, and chaos engineering. These practices empower developers to proactively identify and mitigate potential failure points, thereby improving the overall robustness of their systems. This review underscores the significance of cloud-native technologies in enabling software developers to build scalable and resilient applications. By embracing cloud-native principles and adopting appropriate tools and practices, organizations can effectively meet the evolving demands of modern software development in an increasingly dynamic and competitive landscape.
The concept of smart grid has been introduced as a new vision of the conventional power grid to figure out an efficient way of integrating green and renewable energy technologies. In this way, Internet-connected smart grid, also called energy Internet, is also emerging as an innovative approach to ensure the energy from anywhere at any time. The ultimate goal of these developments is to build a sustainable society. However, integrating and coordinating a large number of growing connections can be a challenging issue for the traditional centralized grid system. Consequently, the smart grid is undergoing a transformation to the decentralized topology from its centralized form. On the other hand, blockchain has some excellent features which make it a promising application for smart grid paradigm. In this paper, we aim to provide a comprehensive survey on application of blockchain in smart grid. As such, we identify the significant security challenges of smart grid scenarios that can be addressed by blockchain. Then, we present a number of blockchain-based recent research works presented in different literatures addressing security issues in the area of smart grid. We also summarize several related practical projects, trials, and products that have been emerged recently. Finally, we discuss essential research challenges and future directions of applying blockchain to smart grid security issues.
In October 2016, the White House, the European Parliament, and the UK House of Commons each issued a report outlining their visions on how to prepare society for the widespread use of artificial intelligence (AI). In this article, we provide a comparative assessment of these three reports in order to facilitate the design of policies favourable to the development of a 'good AI society'. To do so, we examine how each report addresses the following three topics: (a) the development of a 'good AI society'; (b) the role and responsibility of the government, the private sector, and the research community (including academia) in pursuing such a development; and (c) where the recommendations to support such a development may be in need of improvement. Our analysis concludes that the reports address adequately various ethical, social, and economic topics, but come short of providing an overarching political vision and long-term strategy for the development of a 'good AI society'. In order to contribute to fill this gap, in the conclusion we suggest a two-pronged approach.
Next-generation wireless networks are expected to support extremely high data rates and radically new applications, which require a new wireless radio technology paradigm. The challenge is that of assisting the radio in intelligent adaptive learning and decision making, so that the diverse requirements of next-generation wireless networks can be satisfied. Machine learning is one of the most promising artificial intelligence tools, conceived to support smart radio terminals. Future smart 5G mobile terminals are expected to autonomously access the most meritorious spectral bands with the aid of sophisticated spectral efficiency learning and inference, in order to control the transmission power, while relying on energy efficiency learning/inference and simultaneously adjusting the transmission protocols with the aid of quality of service learning/inference. Hence we briefly review the rudimentary concepts of machine learning and propose their employment in the compelling applications of 5G networks, including cognitive radios, massive MIMOs, femto/small cells, heterogeneous networks, smart grid, energy harvesting, device-todevice communications, and so on. Our goal is to assist the readers in refining the motivation, problem formulation, and methodology of powerful machine learning algorithms in the context of future networks in order to tap into hitherto unexplored applications and services.
In the near future, i.e., beyond 4G, some of the prime objectives or demands that need to be addressed are increased capacity, improved data rate, decreased latency, and better quality of service. To meet these demands, drastic improvements need to be made in cellular network architecture. This paper presents the results of a detailed survey on the fifth generation (5G) cellular network architecture and some of the key emerging technologies that are helpful in improving the architecture and meeting the demands of users. In this detailed survey, the prime focus is on the 5G cellular network architecture, massive multiple input multiple output technology, and device-to-device communication (D2D). Along with this, some of the emerging technologies that are addressed in this paper include interference management, spectrum sharing with cognitive radio, ultra-dense networks, multi-radio access technology association, full duplex radios, millimeter wave solutions for 5G cellular networks, and cloud technologies for 5G radio access networks and software defined networks. In this paper, a general probable 5G cellular network architecture is proposed, which shows that D2D, small cell access points, network cloud, and the Internet of Things can be a part of 5G cellular network architecture. A detailed survey is included regarding current research projects being conducted in different countries by research groups and institutions that are working on 5G technologies.
Telecom operators have recently faced the need for a radical shift from technical quality requirements to customer experience guarantees. This trend has emerged due to the constantly increasing amount of mobile devices and applications and the explosion of overall traffic demand, forming a new era: "the rise of the consumer". New terms have been coined in order to quantify, manage, and improve the experienced user quality, with QoE being the most dominant one. However, QoE has always been an afterthought for network providers, and thus numerous research questions need to be answered prior to a shift from conventional network- centric paradigms to more user-centric approaches. To this end, the scope of this article is to provide insights on the issue of networklevel QoE management, identifying the open issues and prerequisites toward acquiring QoE awareness and enabling QoE support in mobile cellular networks. A conceptual framework for achieving end-to-end QoE provisioning is proposed and described in detail in terms of its design, its constituents and their interactions, as well as the key implementation challenges. An evaluation study serves as a proof of concept for this framework, and demonstrates the potential benefits of implementing such a quality management scheme on top of current or future generations of mobile cellular networks.
With the growth of data volumes and variety of Internet applications, data centers (DCs) have become an efficient and promising infrastructure for supporting data storage, and providing the platform for the deployment of diversified network services and applications (e.g., video streaming, cloud computing). These applications and services often impose multifarious resource demands (storage, compute power, bandwidth, latency) on the underlying infrastructure. Existing data center architectures lack the flexibility to effectively support these applications, which results in poor support of QoS, deployability, manageability, and defence against security attacks. Data center network virtualization is a promising solution to address these problems. Virtualized data centers are envisioned to provide better management flexibility, lower cost, scalability, better resources utilization, and energy efficiency. In this paper, we present a survey of the current state-of-the-art in data center networks virtualization, and provide a detailed comparison of the surveyed proposals. We discuss the key research challenges for future research and point out some potential directions for tackling the problems related to data center design.
The authors express their gratitude to Sanyin Siang (Managing Director, Teradata Center for Customer Relationship Management at the Fuqua School of Business, Duke University); research assistants Sarwat Husain, Michael Kurima, and Emilio del Rio; and an anonymous wireless telephone carrier that provided the data for this study. The authors also thank participants in the Tuck School of Business, Dart-mouth College, Marketing Workshop, for comments and the two anony-mous JMR reviewers for their constructive suggestions. Finally, the authors express their appreciation to former editor Dick Wittink (posthumously) for his invaluable insights and guidance. This article provides a descriptive analysis of how methodological factors contribute to the accuracy of customer churn predictive models. The study is based on a tournament in which both academics and practitioners downloaded data from a publicly available Web site, estimated a model, and made predictions on two validation databases. The results suggest several important findings. First, methods do matter. The differences observed in predictive accuracy across submissions could change the profitability of a churn management campaign by hundreds of thousands of dollars. Second, models have staying power. They suffer very little decrease in performance if they are used to predict churn for a database compiled three months after the calibration data. Third, researchers use a variety of modeling "approaches," characterized by variables such as estimation technique, variable selection procedure, number of variables included, and time allocated to steps in the model-building process. The authors find important differences in performance among these approaches and discuss implications for both researchers and practitioners.
Blockchain is an emerging technology with a wide array of potential applications. This technology, which underpins cryptocurrency, provides an immutable, decentralised, and transparent distributed database of digital assets for use by firms in supply chains. However, not all firms are appropriately suited to adopt blockchain in the existing supply chain primarily due to their lack of knowledge on the benefits of this technology. Using Total Interpretive Structural Modelling (TISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC), this paper identifies the adoption barriers, examines the interrelationships between them to the adoption of blockchain technology, which has the potential to revolutionise supply chains. The TISM technique supports developing a contextual relationship-based structural model to identify the influential barriers. MICMAC classifies the barriers in blockchain adoption based on their strength and dependence. The results of this research indicate that the lack of business awareness and familiarity with blockchain technology on what it can deliver for future supply chains, are the most influential barriers that impede blockchain adoption. These barriers hinder and impact businesses decision to establish a blockchain-enabled supply chain and that other barriers act as secondary and linked variables in the adoption process.
Internet of Things: Technologies and Applications for a New Age of Intelligence outlines the background and overall vision for the Internet of Things (IoT) and Cyber-Physical Systems (CPS), as well as associated emerging technologies. Key technologies are described including device communication and interactions, connectivity of devices to cloud-based infrastructures, distributed and edge computing, data collection, and methods to derive information and knowledge from connected devices and systems using artificial intelligence and machine learning. Also included are system architectures and ways to integrate these with enterprise architectures, and considerations on potential business impacts and regulatory requirements.
Read more here: https://www.elsevier.com/books/internet-of-things/holler/978-0-12-814435-0
Network slicing is a new paradigm for future 5G networks where the network infrastructure is divided into slices devoted to different services and customized to their needs. With this paradigm, it is essential to allocate to each slice the needed resources, which requires the ability to forecast their respective demands. To this end, we present DeepCog, a novel data analytics tool for the cognitive management of resources in 5G systems. DeepCog forecasts the capacity needed to accommodate future traffic demands within individual network slices while accounting for the operator’s desired balance between resource overprovisioning (i.e., allocating resources exceeding the demand) and service request violations (i.e., allocating less resources than required). To achieve its objective, DeepCog hinges on a deep learning architecture that is explicitly designed for capacity forecasting. Comparative evaluations with real-world measurement data prove that DeepCog’s tight integration of machine learning into resource orchestration allows for sub- stantial (50% or above) reduction of operating expenses with respect to resource allocation solutions based on state-of-the- art mobile traffic predictors. Moreover, we leverage DeepCog to carry out an extensive first analysis of the trade-off between capacity overdimensioning and unserviced demands in adaptive, sliced networks and in presence of real-world traffic.
The power of quantum computing technologies is based on the fundamentals of quantum mechanics, such as quantum superposition, quantum entanglement, or the no-cloning theorem. Since these phenomena have no classical analogue, similar results cannot be achieved within the framework of traditional computing. The experimental insights of quantum computing technologies have already been demonstrated, and several studies are in progress. Here we review the most recent results of quantum computation technology and address the open problems of the field.
Deep learning, as one of the most currently remarkable machine learning techniques, has achieved great success in many applications such as image analysis, speech recognition and text understanding. It uses supervised and unsupervised strategies to learn multi-level representations and features in hierarchical architectures for the tasks of classification and pattern recognition. Recent development in sensor networks and communication technologies has enabled the collection of big data. Although big data provides great opportunities for a broad of areas including e-commerce, industrial control and smart medical, it poses many challenging issues on data mining and information processing due to its characteristics of large volume, large variety, large velocity and large veracity. In the past few years, deep learning has played an important role in big data analytic solutions. In this paper, we review the emerging researches of deep learning models for big data feature learning. Furthermore, we point out the remaining challenges of big data deep learning and discuss the future topics.
Technological evolution of mobile user equipments (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. Suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud (CC). Nevertheless, this option introduces significant execution delay consisting in delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such delay is inconvenient and make the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: i) decision on computation offloading, ii) allocation of computing resource within the MEC, and iii) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.
Industry investment and research interest in edge computing, in which computing and storage nodes are placed at the Internet's edge in close proximity to mobile devices or sensors, have grown dramatically in recent years. This emerging technology promises to deliver highly responsive cloud services for mobile computing, scalability and privacy-policy enforcement for the Internet of Things, and the ability to mask transient cloud outages. The web extra at www.youtube.com/playlist?list=PLmrZVvFtthdP3fwHPy-4d61oDvQY-RBgS includes a five-video playlist demonstrating proof-of-concept implementations for three tasks: assembling 2D Lego models, freehand sketching, and playing Ping-Pong.
The proliferation of Internet of Things and the success of rich cloud services have pushed the horizon of a new computing paradigm, Edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of Edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative Edge to materialize the concept of Edge computing. Finally, we present several challenges and opportunities in the field of Edge computing, and hope this paper will gain attention from the community and inspire more research in this direction.
Mobile-edge computing (MEC) is an emerging paradigm to meet the ever-increasing computation demands from mobile applications. By offloading the computationally intensive workloads to the MEC server, the quality of computation experience, e.g., the execution latency, could be greatly improved. Nevertheless, as the on-device battery capacities are limited, computation would be interrupted when the battery energy runs out. To provide satisfactory computation performance as well as achieving green computing, it is of significant importance to seek renewable energy sources to power mobile devices via energy harvesting (EH) technologies. In this paper, we will investigate a green MEC system with EH devices and develop an effective computation offloading strategy. The execution cost, which addresses both the execution latency and task failure, is adopted as the performance metric. A low-complexity online algorithm, namely, the Lyapunov optimization-based dynamic computation offloading (LODCO) algorithm is proposed, which jointly decides the offloading decision, the CPU-cycle frequencies for mobile execution, and the transmit power for computation offloading. A unique advantage of this algorithm is that the decisions depend only on the instantaneous side information without requiring distribution information of the computation task request, the wireless channel, and EH processes. The implementation of the algorithm only requires to solve a deterministic problem in each time slot, for which the optimal solution can be obtained either in closed form or by bisection search. Moreover, the proposed algorithm is shown to be asymptotically optimal via rigorous analysis. Sample simulation results shall be presented to verify the theoretical analysis as well as validate the effectiveness of the proposed algorithm.
Using the clickstream data recorded in Web server log files, the authors develop and estimate a model of the browsing behavior of visitors to a Web site. Two basic aspects of browsing behavior are examined: (1) the visitor's decisions to continue browsing (by submitting an additional page request) or to exit the site and (2) the length of time spent viewing each page. The authors propose a type II tobit model that captures both aspects of browsing behavior and handles the limitations of server log-file data. The authors fit the model to the individual-level browsing decisions of a random sample of 5000 visitors to the Web site of an Internet automotive company. Empirical results show that visitors' propensity to continue browsing changes dynamically as a function of the depth of a given site visit and the number of repeat visits to the site. The dynamics are consistent both with "within-site lock-in" or site "stickiness" and with learning that carries over repeat visits. In particular, repeat visits lead to reduced page-view propensities but not to reduced page-view durations. The results also reveal browsing patterns that may reflect visitors' time-saving strategies. Finally, the authors report that simple site metrics computed at the aggregate level diverge substantially from individual-level modeling results, which indicates the need for Web site analyses to control for cross-sectional heterogeneity.
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities
- R Boutaba
- M A Salahuddin
- N Limam
- S Ayoubi
- N Shahriar
- F Estrada-Solano
- O M Caicedo
Boutaba, R., Salahuddin, M.A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F. and Caicedo, O.M., 2018. A
comprehensive survey on machine learning for networking: evolution, applications and research opportunities.
Journal of Internet Services and Applications, 9(1), pp.1-99. https://doi.org/10.1016/j.jnca.2017.12.002