Abstract: The Internet of Medical Things (IoMT) has begun functioning like this: improved patient monitoring and an easily accessible digital data warehouse. Despite that, this methodology of the internet will potentially have a counter balance which risks for patient data might involve hacking, data theft, and unauthorized access that may contain great consequences for patient privacy and safety. This article examines the possibility of utilizing new AI technology, including inter alia deep learning, unsupervised learning, and ensemble learning to further boost anomaly detection and threat management in connected cloud medical systems. Many old rules and approaches based on statistics lose relevancy versus the dynamics and unpredictability of modern cyberattacks. Identification of anomalies in cyber security is nearly unavoidable, and it should be the first and the last reaction for detecting irregularities in behavior that may indicate undesirable acts or attacks. The paper aims at understanding how AI/ML approaches can give more sophisticated and versatile interventions for finding out anomalies in cloud-attached medical machines. Moreover, this research details robust AI/ML methods such as the adversarial machine learning and reinforcement learning for a perfect threat mitigation. These techniques which activates machine learning models to learn from data continuing to adjust to new evolving threats and then to establish intelligent and proactive threat response systems. The data experiment, which focuses on relevant data sets, reveals that it is the AI/ML techniques that possess the upper hand over traditional methods when it comes to identifying anomalies and defending against threats for cloud- connected medical devices. Such finding expresses much significance for the healthcare industry, as it gives room for the inclusion of AI/ML techniques into the security systems of the medical devices, which are all connected to the cloud. Through the employment of these strategies, healthcare units will become better able to detect and halt any form of threat and as a consequence patients’ data will be protected, devices will continue operating effectively, and eventually patients’ safety and healthcare units will benefit and gain trust from patients.

Abstract: Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, fine-tuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGC-driven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks.

Abstract: The combination of artificial intelligence, 5G technology, and cloud computing has altered energy conversion processes, most notably the use of agricultural waste for sustainable energy generation. This book chapter digs into AI, 5G, and cloud computing research and development for efficient energy conversion, environmental concerns, and the viability of agricultural waste as a renewable energy resource. AI technologies provide real-time monitoring and control, while cloud computing enables data analytics and optimization. The synergistic method increases the efficiency of energy conversion, predictability, flexibility, optimization, grid integration, energy storage, and cost reduction. Compatibility, data security, and financial sustainability, on the other hand, must be addressed. The chapter emphasises the importance of this integrated strategy in addressing global energy and environmental challenges.

Abstract: Cyber security has become a very critical concern that needs the attention of researchers, academicians, and organizations to confidentially ensure the protection and security of information systems. Due to the increasing demand for digitalization, every individual and organization faces continually shifting cyber threats. This article provides an overview of the state of the art in cyber security, challenges, and tactics, current conditions, and global trends of cyber security. To stay ahead of the curve in cyber security, we conducted a systematic review to uncover the latest trends, challenges, and state-of-the-art in cyber security. Moreover, we address the future direction of cyber security, presenting the possible strategies and approaches to addressing the increasing cyber security threat landscapes, the emerging trends, and innovations like Artificial Intelligence (AI) and machine learning (ML) to detect and automate cyber threat responses. Additionally, this article underlines the importance of ongoing adoption along with collaboration among stakeholders in the cyber ecosystem.

Abstract: This research paper examines the role of cloud computing in promoting sustainable development. Cloud Computing (CC) has emerged as a transformative technology that has the potential to reduce the environmental impact of businesses and organizations, while also enabling economic growth and social development. The paper explores the environmental benefits of cloud computing, including energy efficiency, reduced carbon emissions, and the potential for renewable energy integration. It also examines the economic and social benefits of cloud computing, including cost savings, increased productivity, and improved access to technology. The paper concludes by highlighting the potential challenges and opportunities associated with the adoption of cloud computing for sustainable development and identifies key areas for future research and policy action. Overall, the paper argues that cloud computing has the potential to play a critical role in promoting sustainable development, and that further research and policy action are needed to realize its full potential.

Abstract: This chapter emphasizes the importance of sustainability in wireless systems and networks, focusing on energy and battery management. Cloud computing enhances sustainability through power-efficient hardware design, energy harvesting techniques, dynamic power management strategies, and battery optimization. Battery management aspects include adaptive voltage scaling, load balancing, battery lifetime prediction, and recycling. Cloud computing influences sustainability through resource consolidation, virtualization, energy-efficient data centers, green cloud initiatives, and edge computing benefits. The chapter integrates energy and battery management with cloud computing through task scheduling, battery monitoring, data transmission optimization, and analytics. Collaboration between wireless, energy management, and cloud computing is crucial for achieving sustainable wireless systems in the cloud era.

Abstract: Majorbio Cloud (https://cloud.majorbio.com/) is a one‐stop online analytic platform aiming at promoting the development of bioinformatics services, narrowing the gap between wet and dry experiments, and accelerating the discoveries for the life sciences community. In 2024, three single‐omics workflows, two multiomics workflows, and extensions were newly released to facilitate omics data mining and interpretation.

Abstract: Edge/fog computing, as a distributed computing paradigm, satisfies the low-latency requirements of ever-increasing number of IoT applications and has become the mainstream computing paradigm behind IoT applications. However, because large number of IoT applications require execution on the edge/fog resources, the servers may be overloaded. Hence, it may disrupt the edge/fog servers and also negatively affect IoT applications' response time. Moreover, many IoT applications are composed of dependent components incurring extra constraints for their execution. Besides, edge/fog computing environments and IoT applications are inherently dynamic and stochastic. Thus, efficient and adaptive scheduling of IoT applications in heterogeneous edge/fog computing environments is of paramount importance. However, limited computational resources on edge/fog servers imposes an extra burden for applying optimal but computationally demanding techniques. To overcome these challenges, we propose a Deep Reinforcement Learning-based IoT application Scheduling algorithm, called DRLIS to adaptively and efficiently optimize the response time of heterogeneous IoT applications and balance the load of the edge/fog servers. We implemented DRLIS as a practical scheduler in the FogBus2 function-as-a-service framework for creating an edge-fog-cloud integrated serverless computing environment. Results obtained from extensive experiments show that DRLIS significantly reduces the execution cost of IoT applications by up to 55%, 37%, and 50% in terms of load balancing, response time, and weighted cost, respectively, compared with metaheuristic algorithms and other reinforcement learning techniques.

Abstract: This study explores the comparative effectiveness of AI-driven user behavior analysis and traditional security measures in cloud computing environments. It specifically examines their accuracy, speed, and predictive capabilities in detecting and responding to cyber threats. As reliance on cloud-based solutions intensifies, the integration of Artificial Intelligence (AI) and machine learning into cloud security has become increasingly vital. The research focuses on how AI-driven security systems, with their advanced pattern recognition and anomaly detection, compare to traditional methods in identifying deviations from standard user behaviors in cloud settings. Employing a quantitative approach, the study utilizes a detailed survey strategy, targeting cybersecurity professionals across multiple industries, including finance, healthcare, information technology, retail, and government sectors. The survey, comprising both closed-ended and Likert-scale questions, is designed to elicit nuanced responses on the perceptions and experiences of these professionals regarding AI-driven versus traditional security methods in cloud environments. The data, collected from a purposive sample of 243 cybersecurity personnel, is analyzed using multiple regression analysis. This analysis facilitates an understanding of the impact of different security systems on the efficacy of threat detection and response in cloud contexts. The results indicate that while both AI-driven and traditional methods significantly improve threat detection accuracy, traditional methods show a slight edge. Conversely, AI-driven systems demonstrate notably superior predictive capabilities and overall enhanced security performance. These findings suggest the necessity of a hybrid security strategy in cloud computing. Such an approach would combine the advanced capabilities of AI, particularly in predictive analytics and adaptability, with the rapid and reliable responses of traditional methods. This integrated strategy is proposed to effectively address the unique challenges posed by the dynamic and complex nature of cloud-based cyber threats. This study provides valuable insights for both businesses and IT professionals on the effective integration of AI-driven security measures in cloud environments. It highlights the evolving role of AI in cloud security and the importance of maintaining a balance between innovative AI approaches and established traditional methods to create a robust, comprehensive cloud security framework.

Abstract: The healthcare industry is significantly impacted by the next generation of internet of things (NGIoT), particularly in terms of digital connectivity infrastructures. This chapter of the book examines the role that cutting-edge technology and human-centered techniques are playing in the development of NGIoT in healthcare. The subjects addressed include digital connection infrastructures including interoperability, communication protocols, privacy, and security, as well as sensor technologies, edge computing, artificial intelligence, blockchain, and cloud computing. The chapter focuses on user-centric design concepts, user experience, ethical considerations, and stakeholder involvement to emphasise the need of a human-centered approach. In this chapter, case studies and best practises for adopting the NGIoT are described along with lessons learned and challenges faced. The chapter summarises the key conclusions and looks at the ramifications.

Abstract: Ensuring the reliability and availability of cloud services necessitates efficient root cause analysis (RCA) for cloud incidents. Traditional RCA methods, which rely on manual investigations of data sources such as logs and traces, are often laborious, error-prone, and challenging for on-call engineers. In this paper, we introduce RCACopilot, an innovative on-call system empowered by the large language model for automating RCA of cloud incidents. RCACopilot matches incoming incidents to corresponding incident handlers based on their alert types, aggregates the critical runtime diagnostic information, predicts the incident's root cause category, and provides an explanatory narrative. We evaluate RCACopilot using a real-world dataset consisting of a year's worth of incidents from Microsoft. Our evaluation demonstrates that RCACopilot achieves RCA accuracy up to 0.766. Furthermore, the diagnostic information collection component of RCACopilot has been successfully in use at Microsoft for over four years.

Abstract: Metaverse is a virtual environment where users are represented by their avatars to navigate a virtual world having strong links with its physical counterpart. The state-of-the-art Metaverse architectures rely on a cloud-based approach for avatar physics emulation and graphics rendering computation. The current centralized architecture of such systems is unfavorable as it suffers from several drawbacks caused by the long latency of cloud access, such as low-quality visualization. To this end, we propose a Fog-Edge hybrid computing architecture for Metaverse applications that leverage an edge-enabled distributed computing paradigm. Metaverse applications leverage edge devices' computing power to perform the required computations for heavy tasks, such as collision detection in the virtual universe and high-computational 3D physics in virtual simulations. The computational costs of a Metaverse entity, such as collision detection or physics emulation, are performed at the device of the associated physical entity. To validate the effectiveness of the proposed architecture, we simulate a distributed social Metaverse application. The simulation results show that the proposed architecture can reduce the latency by 50% when compared with cloud-based Metaverse applications.

Abstract: Abstract Cloud environment is a virtual, online, and distributed computing environment that provides users with large-scale services. And cloud monitoring plays an integral role in protecting infrastructures in the cloud environment. Cloud monitoring systems need to closely monitor various KPIs of cloud resources, to accurately detect anomalies. However, due to the complexity and highly dynamic nature of the cloud environment, anomaly detection for these KPIs with various patterns and data quality is a huge challenge, especially those massive unlabeled data. Besides, it’s also difficult to improve the accuracy of the existing anomaly detection methods. To solve these problems, we propose a novel Dynamic Graph Transformer based Parallel Framework (DGT-PF) for efficiently detect system anomalies in cloud infrastructures, which utilizes Transformer with anomaly attention mechanism and Graph Neural Network (GNN) to learn the spatio-temporal features of KPIs to improve the accuracy and timeliness of model anomaly detection. Specifically, we propose an effective dynamic relationship embedding strategy to dynamically learn spatio-temporal features and adaptively generate adjacency matrices, and soft cluster each GNN layer through Diffpooling module. In addition, we also use nonlinear neural network model and AR-MLP model in parallel to obtain better detection accuracy and improve detection performance. The experiment shows that the DGT-PF framework have achieved the highest F1-Score on 5 public datasets, with an average improvement of 21.6% compared to 11 anomaly detection models.

Abstract: Unmanned Aerial Vehicles (UAVs) gather data efficiently for power line inspection. Anomaly detection is essential for power infrastructure dependability and security. It proposes a Cloud-Enabled Isolation Forest (CEIF) method for UAV-based power line inspection. It improves the isolation forest algorithm's efficiency and scalability in cloud computing. It can process huge UAV inspection datasets by dispersing cloud computing. The technique, which effectively isolates anomalies, is applied to the cloud for fast power line inspection and anomaly identification. It describes the CEIF system's cloud service integration and distributed computing algorithm optimization. Real-world UAV-based power line inspection datasets show it can accurately detect abnormalities with low false-positive rates. It is scalable and robust for improving power infrastructure dependability and security. It allows cloud services to deploy real-world settings to implement different inspection scales.

Abstract: This research study presents a novel method for managing grain storage facilities by combining adaptive silo networks, cloud computing, and reinforcement learning (RL). The suggested system's goal is to improve grain storage efficiency by automatically modifying storage parameters in response to changes in the surrounding environment and real-time data. The adaptive silo network uses cloud computing capabilities to keep track of things like temperature, humidity, and grain quality in real-time. The proposed system is able to learn and adapt to new circumstances due to the use of RL algorithms. Interacting with environmental elements such as weather patterns, grain kinds, and demand changes, the model learns optimum storage solutions. The proposed approach improves storage efficiency with the use of advanced algorithms, adaptive control mechanisms, and real-time monitoring. Contributing to the long-term viability and financial success of agricultural supply chains, it optimizes grain storage systems to reduce losses and provide responsive management. With cloud computing, scalability and dispersed data processing, analysis, and decision-making are improved. The device enhances storage efficiency and gives advanced warning of impending problems, allowing for preventative maintenance and protection. To increase the responsiveness and efficiency of grain storage facilities, suggest using cloud computing and RL to create adaptive silo networks. By integrating advanced technology to overcome challenges in grain storage and management it advances the development of smart agriculture practices.

Abstract: CONTEXT: Food security is highly reliant on agricultural activity to drive the world economy. However, this activity is in great danger due to climatic changes and improper use of irrigation techniques. Consequently, the lives of numerous individuals worldwide are in jeopardy. In light, this paper investigates the promise of smart irrigation systems based on new technology. OBJECTIVE: To meet the growing demand for water in agriculture, this study presents an intelligent irrigation system that uses cutting-edge technologies of 1) cloud computing, 2) embedded systems, and 3) Internet-of-Things (IoT). The main objective is to demonstrate how this innovative strategy can effectively manage water resources, supporting food security through cutting-edge agricultural technology. METHODS: This paper proposes a smart irrigation system based on cutting-edge technologies like the embedded system, Internet of Things (IoT), and cloud computing as a groundbreaking strategy to improve food security through the implementation of advanced agricultural technology. This system supervises real-time monitoring of crucial environmental factors such as 1) moisture, 2) humidity, 3) temperature, and 4) water levels, in smart agriculture practices. In addition, this system employs the latest sensors, including the module (DHT22), water level sensor, and moisture sensors, which are connected to the widely used embedded system (ESP32). The system uses the ThingSpeak cloud and ThingView app to enable wireless communication between the device and the farm owner, enhancing their interaction. The automated control of the two water pumps is based on the readings of various environmental factors. Moreover, this will also present a mathematical-driven function known as linear interpolation to calibrate the water level sensor in percentage. This system was created using the V-model software development approach. RESULTS AND CONCLUSION: Farmers can access comprehensive farm data from anywhere in the world as the sensor data is transmitted in real-time to both the ThingSpeak cloud and the ThingView. This capability allows for more precise crop irrigation and increased production. The study's findings demonstrate a striking 70% reduction in water consumption for soil irrigation when utilizing the proposed smart irrigation system. This paper underscores the significant promise of smart irrigation systems, driven by IoT, embedded systems, and cloud computing, to conserve water resources and advance food security. SIGNIFICANCE: This article proposes an innovative solution that reduces soil irrigation water consumption by 70% compared to traditional methods. It explores how smart irrigation can improve the sustainability of agriculture and positively influence food security.

Abstract: This chapter delves into the intricate relationship between innovation and security in the digital age. It highlights the challenges of the digital age and the transformative impact of cloud computing, emphasizing its role in driving innovation. The chapter also delves into cloud service and deployment models, discussing the benefits and challenges of cloud security. It also discusses the role of innovation in driving progress through case studies and addressing challenges organizations face. The chapter also discusses risk assessment and mitigation in the cloud, compliance, regulatory challenges, legal and ethical considerations, and best practices. It also explores emerging technologies like AI and machine learning, Zero Trust security, and future directions.

Abstract: In recent years, cloud computing has been widely used. Cloud computing refers to the centralized computing resources, users through the access to the centralized resources to complete the calculation, the cloud computing center will return the results of the program processing to the user. Cloud computing is not only for individual users, but also for enterprise users. By purchasing a cloud server, users do not have to buy a large number of computers, saving computing costs. According to a report by China Economic News Network, the scale of cloud computing in China has reached 209.1 billion yuan.Rational allocation of resources plays a crucial role in cloud computing. In the resource allocation of cloud computing, the cloud computing center has limited cloud resources, and users arrive in sequence. Each user requests the cloud computing center to use a certain number of cloud resources at a specific time.

Abstract: Vehicular Edge Computing (VEC) supports latency-sensitive and computation-intensive vehicular applications by providing caching and computing services in vehicle proximity, reducing congestion and transmission latency. However, VEC faces implementation challenges due to high vehicle mobility and unpredictable network dynamics, posing difficulties to network resource allocation. Most existing VEC network resource management solutions consider edge-cloud collaboration and ignore collaborative computing between edge nodes. A reasonable collaboration between Roadside Units (RSUs) or small cells eNodeB can improve VEC network performance. Our proposed framework aims to improve VEC network performance by integrating Digital Twin (DT) technology which creates virtual replicas of network nodes to estimate, predict, and evaluate their real-time conditions. A DT is constructed centrally to maintain and simulate the VEC network, thus realizing real-time computing and communication resources information availability. We employ channel state information (CSI) for RSUs selection, and vehicles communicate with RSUs through a non-orthogonal multiple access (NOMA) protocol. We aim to maximize the VEC system computation rate and minimize task completion delay by jointly optimizing offloading decisions, subchannel allocation, and RSU association. In view of the resulting optimization problem complexity (NP-hard), we model it as a Markov Decision Process (MDP) and apply Advantage Actor-Critic (A2C) algorithm to solve it. Validated via simulations, our scheme shows superiority to the benchmarks in reducing task completion delay and improving VEC system computation rates.

Abstract: The green power sector is revolutionizing energy production, grid management, and sustainability by integrating cloud computing and machine learning techniques. This chapter explores data handling processes, including data sources, collection methods, preprocessing, and cloud computing. It discusses machine learning algorithms for predictive modeling and real-time monitoring. Key benefits, challenges, and considerations are discussed, along with case studies of successful cloud adoption in green power projects. The chapter also emphasizes data governance, security, integration techniques, and warehousing solutions for handling growing data requirements. The sector offers efficiency, reliability, and environmental responsibility, but faces challenges like data privacy, scalability, and regulatory compliance.

Abstract: Cloud computing (CC) offers a wide range of on-demand resources and services over the internet. However, due to the inherent vulnerability of the cloud's dispersed architecture, guaranteeing the privacy and security of cloud assets and resources remains a tough task. Data and service protection in cloud systems is a continuous and serious concern. This article presents a fresh solution to this problem by utilizing Deep Learning (DL) techniques for intrusion detection in cloud computing systems. The present research analyses the strategies used by several Intrusion Detection Systems (IDS). As network infrastructures expand, so do security risks, increasing the need for dependable intrusion detection. Developers have created numerous intrusion detection systems (IDS) in response to the expanding security and privacy issues that saturate modern computer networks. Improving the datasets used for training and testing these security solutions requires equal focus as building defense systems. Improved datasets significantly enhance the detection capabilities of both offline and online intrusion detection models. This essay contributes to the growing corpus of research on CC security by conducting a thorough examination of publicly available network-based IDS datasets. It emphasizes the need to incorporate cutting-edge DL approaches into IDS for increased security. As cloud computing continues to transform the digital world, the findings of this study have major implications for safeguarding sensitive data and key services in cloud-based ecosystems. Furthermore, they provide solutions to the current anomaly detection issues caused by insufficient normal patterns in training data. The detection of accuracy information for two, five, and twenty-three classes and soft-max regression (SMR) feature learning perform similarly for 5-class and 23-class categorization, and it was also found that STL completed all categorization categories with above 98% accuracy.

Abstract: Cloud computing offers scalable resources and services over the internet, enabling businesses to reduce infrastructure costs while improving flexibility. However, its widespread adoption also brings significant security concerns, including data privacy, regulatory compliance, and threat prevention. This research paper aims to identify and analyze the primary security challenges in cloud computing, propose solutions, and discuss future research directions

Abstract: Cloud computing has revolutionized IT infrastructure deployment and management, but it also presents security and resilience challenges. The study delves into the principles and strategies of cloud security to safeguard cloud environments and guarantee business continuity. It explains the concepts of infrastructure as a service (IaaS) and platform as a service (PaaS), their benefits and challenges, and the complex web of security principles within the cloud, including the shared responsibility model, best practices, and identity and access management. The guide explores cloud threats, focusing on common threats and emerging trends. It covers data security, network security measures, and security monitoring. It emphasizes integrating security into DevOps, securing CI/CD pipelines, and infrastructure as code (IaC) security. It covers disaster recovery, business continuity, cloud backup strategies, high availability, and cloud-based solutions, enabling organizations to effectively manage cloud security and resilience.

Abstract: Cloud computing has revolutionized business operations by providing scalable, on-demand access to computing resources. This paper examines the impact of cloud computing on modern enterprises, focusing on its benefits, challenges, and future trends. Through a review of existing literature and case studies, we analyse how cloud computing enhances operational efficiency, enables innovation, and introduces security and privacy concerns. The findings highlight the need for strategic implementation to fully leverage cloud technologies.

Abstract: Abstract The volume of data transferred across communication infrastructures has recently increased due to technological advancements in cloud computing, the Internet of Things (IoT), and automobile networks. The network systems transmit diverse and heterogeneous data in dispersed environments as communication technology develops. The communications using these networks and daily interactions depend on network security systems to provide secure and reliable information. On the other hand, attackers have increased their efforts to render systems on networks susceptible. An efficient intrusion detection system is essential since technological advancements embark on new kinds of attacks and security limitations. This paper implements a hybrid model for Intrusion Detection (ID) with Machine Learning (ML) and Deep Learning (DL) techniques to tackle these limitations. The proposed model makes use of Extreme Gradient Boosting (XGBoost) and convolutional neural networks (CNN) for feature extraction and then combines each of these with long short-term memory networks (LSTM) for classification. Four benchmark datasets CIC IDS 2017, UNSW NB15, NSL KDD, and WSN DS were used to train the model for binary and multi-class classification. With the increase in feature dimensions, current intrusion detection systems have trouble identifying new threats due to low test accuracy scores. To narrow down each dataset’s feature space, XGBoost, and CNN feature selection algorithms are used in this work for each separate model. The experimental findings demonstrate a high detection rate and good accuracy with a relatively low False Acceptance Rate (FAR) to prove the usefulness of the proposed hybrid model.

Abstract: NSOs are responsible for a vast array of data that are used for official statistical production and for that purpose only. The statistical business process depends on data about individuals, households, enterprises, municipalities, etc. These different entities/statistical units trust the NSO to keep their data safe. There are also legal obligations, data protection and statistical acts that NSOs must adhere to and are meant to further ensure the security and privacy of data used for official statistical production.

Abstract: For the energy-efficient deployment of cell-free massive MIMO functionality in a practical wireless network, the end-to-end (from radio site to the cloud) energy-aware operation is essential. In line with the cloudification and virtualization in the open radio access networks (O-RAN), it is indisputable to envision prospective cell-free infrastructure on top of the O-RAN architecture. In this paper, we explore the performance and power consumption of cell-free massive MIMO technology in comparison with traditional small-cell systems, in the virtualized O-RAN architecture. We compare two different functional split options and different resource orchestration mechanisms. In the end-to-end orchestration scheme, we aim to minimize the end-to-end power consumption by jointly allocating the radio, optical fronthaul, and virtualized cloud processing resources. We compare end-to-end orchestration with two other schemes: i) “radio-only” where radio resources are optimized independently from the cloud and ii) “local cloud coordination” where orchestration is only allowed among a local cluster of radio units. We develop several algorithms to solve the end-to-end power minimization and sum spectral efficiency maximization problems. The numerical results demonstrate that end-to-end resource allocation with fully virtualized fronthaul and cloud resources provides a substantial additional power saving than the other resource orchestration schemes.

Abstract: This study uses cloud computing platform to process multi-modal data, and constructs a traffic flow prediction model based on LSTM neural network by integrating data from multiple dimensions such as traffic flow, occupancy and speed. In the process of model construction, we fully consider the hourly characteristics and hysteresis characteristics, and carry out fine scaling and splitting of the data to improve the accuracy and generalization ability of the model. The experimental results show that our model outperforms the baseline on both the training set and the test set, which verifies its effectiveness in traffic flow prediction. By keeping our models in a cloud environment, we provide reliable tools and support for future real-time data analysis and traffic management decisions. This study provides an important reference for the development of traffic management system based on cloud computing, and also provides new ideas and methods for other fields to solve practical problems by using multi-modal data.