Hybrid Model Deployment: Balancing Edge and Cloud Computation

The cloud computing is the new technique used most today for computation and storage. Edge computing is also a new computing domain that gives computation of tasks and storage of data nearer to the data sources. Various applications of edge computing are IOT, healthcare, retail, manufacturing etc. Virtualization technologies have enabled cloud platforms to provide various services such as virtual machines to the users. Task scheduling is the major issues faced in both cloud and edge domains. Since the usage of machines is high, it is difficult to assign tasks manually. So, an efficient and optimized algorithm is required in the cloud as well as the edge environment. Here, a task scheduling is implemented based on Heuristic Optimization Technique to examine the performance measures using cloudsim. Task scheduling and optimization techniques dynamically allocate resources in the cloud environment. Similarly, in edge computing, scheduling the task is performed with different optimization techniques such as random fit, and max fit algorithms. These task scheduling methods are allocated to the VM based on the resources that are available at the VM. These algorithms are implemented, and it increased the VM utilization by 4.82%. The processing time taken is reduced by 2.67% and the percentage of failed tasks is reduced by 3.68%. The experiment result shows the importance of the scheduling in the cloud and edge environment, in terms of processing time, failed task and increase in VM utilization in edge devices.

Cloud computing alters the way organizations manage and deploy their IT resource. It provides an organization with scalable, inexpensive, and flexible options. The complexity and dynamic nature of cloud environments pose a challenge to maintaining high availability at all times, especially when the system fails or a disaster arises. The legacy techniques of disaster recovery, fault tolerance, and high availability leave behind much to be desired. These techniques are mostly static, slow to respond, and have a dismal ability to adapt to continuously changing conditions in contemporary cloud systems. Such techniques largely depend on manual configurations and predefined policies; resulting in lots of inefficiencies and increases in the risk of service downtime. This research investigates the way Artificial Intelligence (AI) changes the paradigm on cloud resilience to promote adoption of intelligent systems for guaranteed disaster recovery, better fault-tolerant behavior, or transparent high availability. With machine learning algorithms, AI-based cloud services utilize large data volumes to reveal patterns within system logs, performance metrics, and user behavior data; thereby offering real-time anomaly detection and predictive failure analysis. For example, techniques like predictive analytics help cloud providers predict likely system outages, optimize the resources to be used, and automate failover processes (Xu et al., 2021; Lee & Kumar, 2022). AI-aided disaster recovery techniques employ complex algorithms to produce an adaptive backup mechanism, thereby minimizing loss and reducing restoration time. Fault tolerance in AI cloud systems comes from intelligent error correction, automatic isolation of faults, and self-healing features, i.e. repair of faults without the need for human supervision (Chen et al., 2020). Besides that, AI also contributes to high availability through intelligent load balancing, which ensures that at any given time, resources are optimally distributed throughout the network to sustain continuous service even during peak demand or unanticipated failures (Patel & Zhang, 2023). The approach is a comprehensive review of the various existing literature on the topic, empirical analysis of the current AI-driven cloud solutions available in the market, and case studies for comparison analysis on the different AI systems. The study scenario reveals that AI-driven solutions noticeably reduce downtimes, lead to improved recovery times, and contribute to overall system reliability as compared to traditional methods. However, other areas include model bias, data privacy, and continuous training of AI models. This study expands the trends of AI in the field of cloud computing by documenting the significance of intelligent systems in bridging traditional weaknesses of resilience strategies. It further signifies the need for AI into predictive maintenance, automated disaster response, and proactive fault management of rapidly changing dynamic cloud environments. Future studies will focus on AI integration along with edge computing and blockchain technologies for even more robust and secure services in cloud operations.

The emergence of the Internet of Things (IoT) introduced new classes of applications whose latency and bandwidth requirements could not be satisfied by the traditional Cloud Computing model. Consequently, the Internet Technology community promoted the cooperation of two paradigms, Cloud Computing and Edge Computing, combining large-scale computing power and real-time processing capabilities. A significant management challenge in such complex infrastructure concerns the development of efficient maintenance strategies to preserve the environment’s performance and security. While the abundant resources from the academic literature could support the design of novel maintenance solutions, extracting actionable insights from the existing approaches is challenging, given the massive number of published papers. Furthermore, existing review papers, which could help summarize the state-of-the-art, scope their investigations to the maintenance of certain components in particular scenarios. This work fills this gap with a broader literature analysis that covers maintenance strategies targeting physical and logical components in cloud, edge, and IoT environments. First, we introduce a taxonomy that organizes existing solutions according to several characteristics. Then, we review the literature following the taxonomy structure to facilitate the understanding of the research landscape and the comparison between existing works. Finally, we shed light on open challenges that represent promising research directions.

The article investigated the concept of IT infrastructure of higher educational institution. The article described models of deploying of cloud technologies in IT infrastructure. The hybrid model is most recent for higher educational institution. The unified authentication is an important component of IT infrastructure. The author suggests the public (Google Apps, Office 365) and private (Cloudstack, Eucalyptus, OpenStack) cloud platforms to deploying in IT infrastructure of higher educational institution.The purpose of article is to designing individual components of the IT infrastructure of a higher education institution using cloud technology.Task of the research: to analyze the concept of "IT infrastructure", to determine the characteristics of deployment of the cloud technologies and to offer the some components of the IT infrastructure of universities in the traditional and cloud context aspect.The object of the research is IT infrastructure of higher education.The subject of the research is cloud and traditional services as components of the IT infrastructure of the university.Methods: analysis of literature studying the functioning of the IT infrastructure of the university, modeling and design of IT infrastructure.Results: analyzed the concept of "IT infrastructure", "cloud technology", defined characteristics and models of cloud technology deployment, proposed separate components of the IT infrastructure of higher education institutions in the context of "traditional" and "cloud" aspects.Conclusions. The use of cloud technologies in the design of IT infrastructure of universities is an actual and need of the development. The hybrid model is the most appropriate in the deployment of cloud infrastructure of higher educational institution. It is reasonable to use the public (Google Apps and Microsoft Office 365) and private (Cloudstack, Eucalyptus, OpenStack) cloud platform.

Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users' requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This paper provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions.

Dew Computing (DC) is a comparatively modern field with a wide range of applications. By examining how technological advances such as fog, edge and Dew computing, and distributed intelligence force us to reconsider traditional Cloud Computing (CC) to serve the Internet of Things. A new dew estimation theory is presented in this article. The revised definition is as follows: DC is a software and hardware cloud-based company. On-premises servers provide autonomy and collaborate with cloud networks. Dew Calculation aims to enhance the capabilities of on-premises and cloud-based applications. These categories can result in the development of new applications. In the world, there has been rapid growth in Information and Communication Technology (ICT), starting with Grid Computing (GC), CC, Fog Computing (FC), and the latest Edge Computing (EC) technology. DC technologies, infrastructure, and applications are described. We'll go through the newest developments in fog networking, QoE, cloud at the edge, platforms, security, and privacy. The dew-cloud architecture is an option concerning the current client-server architecture, where two servers are located at opposite ends. In the absence of an Internet connection, a dew server helps users browse and track their details. Data are primarily stored as a local copy on the dew server that starts the Internet and is synchronized with the cloud master copy. The local dew pages, a local online version of the current website, can be browsed, read, written, or added to the users. Mapping between different Local Dew sites has been made possible using the dew domain name scheme and dew domain redirection.

The booming cloud computing, together with edge computing, lays the computing resource foundation for larger and more granular simulation. To achieve better execution of the simulation in cloud and edge environments, it is necessary to assign simulation components to cloud computing centers and edge computing nodes appropriately. This is an optimization problem and a particle swarm optimization(PSO) algorithm is proposed for this purpose. However, deployment problems are a kind of discrete optimization problem and PSO algorithm was originally designed to solve continuous optimization problems, thus, various discretization methods had been proposed to modify original PSO algorithm to handle discrete problems. Unsatisfactorily, many discrete PSO algorithms suffer from random movements of particles due to improper use of the guidance information obtained from the global best particle and personal best particle. To counter this challenge, we proposed a selection-based discrete PSO algorithm(SPSO) and conducted simulation experiments on MATLAB platform. The results showed that SPSO got better performance in simulation components allocation problems compared with NSGA-II and other pure discrete PSO algorithms.

Abstract: The rapid adoption of artificial intelligence (AI) in cloud and edge computing environments has transformed industries by enabling large-scale automation, real-time analytics, and intelligent decision-making. However, the increasing reliance on AI-powered infrastructures introduces significant cybersecurity challenges, including adversarial attacks, data privacy risks, and vulnerabilities in AI model supply chains. This research explores advanced cybersecurity frameworks tailored to protect AI-driven cloud and edge computing environments. It investigates AI-specific security threats, such as adversarial machine learning, model poisoning, and API exploitation, while analyzing AI-powered cybersecurity techniques for threat detection, anomaly prediction, and zero-trust security. The study also examines the role of cryptographic solutions, including homomorphic encryption, federated learning security, and post-quantum cryptography, in safeguarding AI models and data integrity. By integrating AI with cutting-edge cybersecurity strategies, this research aims to enhance resilience, compliance, and trust in AI-driven infrastructures. Future advancements in AI security, blockchain-based authentication, and quantum-enhanced cryptographic solutions will be critical in securing next-generation AI applications in cloud and edge environments. Keywords: AI security, adversarial machine learning, cloud computing security, edge computing security, zero-trust AI, homomorphic encryption, federated learning security, post-quantum cryptography, blockchain for AI security, AI-driven threat detection, model poisoning attacks, anomaly prediction, cyber resilience, decentralized AI security, secure multi-party computation (SMPC).

Technologies such as cloud, fog, and edge computing offer exceptional solutions for many technical problems in the Internet of Things (IoT). Cloud computing offers enormous flexibility in terms of resource availability and demand. In this paper, the role of cloud, edge, and fog computing in IoT was discussed along with the emergence of numerous IoT smart scenarios which are difficult to implement. This research is important in guiding future network paradigms that require faster processing with less delay and disruption, and the advanced technologies in them can address the challenges of IoT. It also reviews cloud, fog, and edge computing and their relevance to the IoT.

Cloud workload evolution has progressed because end-users need real-time applications such as autonomous systems, industrial IoT, and innovative healthcare. Traditional cloud computing systems cause substantial latency because they process information centrally while sending and receiving data. Artificial intelligence and Edge computing unite to provide an effective solution through network edge-based computations distribution, enabling fast real-time data processing. This research analyzes important strategies used in edge computing and artificial intelligence technology to minimize delays in cloud computing operations. The paper introduces basic principles of edge computing with AI capabilities for cloud workload management. We examine three main challenges: network bottleneck, processing capacity limitations, and security threat considerations. The proposed solution incorporates edge AI accelerators with hardware implementation and lightweight AI models, federated learning and reinforcement learning as software approaches, and 5G technology and edge caching as network optimization methods. The document demonstrates real-world applications based on case studies and experimental outcomes from autonomous vehicles, the healthcare sector, and industrial IoT implementations. The paper conducts a systematic evaluation that compares edge-based AI systems against traditional cloud computing based on their latency, power efficiency, and flexibility performance. It examines upcoming technologies that involve AI-driven self-adjusting edge networks and combinations of cloud and edge AI platforms. The discussion grows in technical depth through the addition of graphs, flowcharts, pseudocode, and system diagrams. Our research confirms that AI integration with edge computing lowers end-to-end latency while improving real-time decisions and maximizing cloud-based resource performance. The study establishes a framework for developing next-generation cloud architectural infrastructure that uses low-latency artificial intelligence.

This Nowadays system hear that CLOUD computing is ubiquitous. Most of tech giants have converted their owned in-house data centers to using CLOUD technology and outsourcing the part of their services to public cloud and to opt for hybrid cloud architecture. There is no doubt that CLOUDs have the potential for being the next generation model computational services, but with that potential comes the risk of theft and concerns related to security and privacy. A lot of development and progress has already been made in CLOUD technologies, there still remains a wide range of concerns this article system identify and classify the main security concerns and solutions in cloud computing, and propose a method of security in cloud environment. In this paper Row Column Diagonal (RCD) based engine has been proposed. Along with the cloud sub-cloud. Data Security using engine in Environment is also explained. Further, engine system has been proposed in Central Cloud and RCD Supervisor modes in environment along with the situation of uploading and downloading. Validation and analysis have been performed for Engine and indicates based engine is much powerful and secure. As Algorithm is much more robust and easy to implement with considering all the aspects of main and sub cloud using supervisor as the central body.

The digital transformation, the growing number of connected devices, and the demand for real-time decision-making are driving unprecedented growth in data-intensive workloads and the increasing latency sensitivity of workloads in enterprises across various industries. The existing cloud computing, although it is very robust regarding scalability and centralized resource control, is in most cases unable to address the low latency and high reliability issues required by mission-critical applications. The concept of edge computing has become a supporting paradigm, placing computation within closer proximity to the sources of data; however, the lack of resource capacity is not easily manageable in large-scale workloads of enterprises. A combination of artificial intelligence (AI) and the cloud and edge systems provides the avenue towards scalable, adaptive, and low-latency computing. This article discusses the intersection of AI and cloud technology in supporting a scalable edge computing architecture that can meet the needs of an enterprise. We discuss the use of AI-based orchestration to optimize workload delivery between cloud and edge to improve efficiency and responsiveness. Some of the architectural models are discussed, and the trade-offs between cloud-centric, edge-centric, and hybrid deployments are identified. The performance aspects, such as the latency, scalability, and fault tolerance, are considered, keeping in mind the enterprise scale requirements. Moreover, the paper also discusses the most significant challenges, including data security, privacy, and compliance with regulations in AI-enabled edge environments, which are the main determinants of the scale of adoption. We emphasize that the combination of AI, cloud, and edge computing provides a synergistic platform that can fulfill the requirements of enterprises in real-time intelligence, scalability, and operational efficiency. We show that hybrid AI-cloud-edge architectures can be deployed to minimize latency, besides offering a resilient and cost-effective platform in various industries such as healthcare, finance, and manufacturing. Lastly, the paper also recognizes persistent issues like interoperability, the complexity of orchestration, and security weaknesses, and suggests future research directions. The contribution of this work is the holistic look at how enterprises can use AI-cloud-edge integration to develop sustainable and high-performance computing infrastructures.

Summary The internet of things has brought better connectivity among devices in various industries including the oil and gas industry. With this improved connectivity comes improved applications, especially in the area of real-time data processing and analytics. A major requirement for real-time application is minimized latency. Cloud computing, despite its many benefits, is often limited in that area. A lag often starts and worsens when the realtime data has to go back and forth between field locations and data centers. On the other hand, edge computing enables faster processing speeds. Edge computing applied to real-time well engineering is presented and discussed. It has the potential to be an adequate approach for applications dependent on real-time processing such as geosteering, which requires instantaneous processing to drastically enhance the economic profitability of a well. With edge computing, some sensor data can be cleaned and processed instantaneously to automatically identify the rig state, which is a key step in the real-time well engineering workflow during drilling.

Producción Científica

In this chapter, an overview of cloud computing is presented. We try to define the term, describe its main characteristics, outline the types of services provided by cloud technology, and detail how it is related to new concepts such as edge and fog computing. To this end, we check various definitions presented by companies, academics, and analyst firms and study the historical evolution of cloud computing. Cloud computing describes a model for on-demand delivery of computing power based on pay-per-use business models. Virtualization and dynamic scalability on demand are the main features of the cloud. Here, software, platform, and infrastructure are often provided in as a service manner, for which multitenancy and resource pooling, on-demand usage, elasticity, broad network access, measured usage, and resiliency are the main common characteristics. Infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) are three constitutive layers of a cloud. There are four deployment models for cloud computing: public, private, hybrid, and community. In this chapter, the main advantages and disadvantages of this technology are given, and the motivations of organizations for acquiring cloud computing services are discussed. Finally, the relationship between cloud computing and several close technologies such as grid, edge, and fog computing are presented and discussed.