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The Internet of Things (IoT) has seen remarkable advancements in recent years, leading to a paradigm shift in the digital landscape. However, these technological strides have introduced new challenges, particularly in cybersecurity. IoT devices, inherently connected to the internet, are susceptible to various forms of attacks. Moreover, IoT services often handle sensitive user data, which could be exploited by malicious actors or unauthorized service providers. As IoT ecosystems expand, the convergence of traditional and cloud-based systems presents unique security threats in the absence of uniform regulations. Cloud-based IoT systems, enabled by Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) models, offer flexibility and scalability but also pose additional security risks. The intricate interaction between these systems and traditional IoT devices demands comprehensive strategies to protect data integrity and user privacy. This paper highlights the pressing security concerns associated with the widespread adoption of IoT devices and services. We propose viable solutions to bridge the existing security gaps while anticipating and preparing for future challenges. This paper provides a detailed survey of the key security challenges that IoT services are currently facing. We also suggest proactive strategies to mitigate these risks, thereby strengthening the overall security of IoT devices and services. [ABSTRACT FROM AUTHOR]

Problems with task distribution in cloud data centers persist despite earlier research in cloud computing (CC). Particularly in the infrastructure-as-a-service (IaaS) cloud paradigm. In cloud data centers, effective task allocation is essential due to the restricted availability of resources and virtual machines (VMs). IaaS is one of the main CC models since it controls the backend, which includes VMs and data centers. Cloud service providers can ensure satisfactory service delivery performance in these models by preventing situations of host underutilization or overloading. This is because both results increase network execution time and lead to VM failure. To solve these problems, an improved load balancing approaches was proposed in this work. Therefore, this paper suggested an enhanced load balancing approaches to address these issues. The Artificial Bee Colony (ABC) method and the Bat algorithm are combined to create the balancing technique known as the Hybrid BAT and ABC (HBABC) algorithm, which dynamically distributes resources. The suggested HBABC method was assessed using CloudSim and standard workload format (SWF) data sets, which had file sizes of 200 KB and 400 KB. The evaluation was conducted on even workloads ranging from 200 to 20,000, and the performance of the HBABC method was compared with other state-of-the-art algorithms. The implementation of the suggested HBABC method resulted in a reduction of the Makespan (energy level) within the data center and showed improved accuracy in task allocation for VMs in a cloud data center. The ANOVA comparison test revealed a 1.98 percent enhancement in VM accuracy and task distribution, as well as a 0.98 percent decrease in the Makespan or energy level of the cloud data center. The outcomes are in line with various services broker rules that are employed during process of simulating the suggested algorithm in a cloud datacenter. The suggested method will be employed in subsequent studies as a prediction strategy for the resource management system in cloud datacenters. [ABSTRACT FROM AUTHOR]

Security grids consisting of High-Definition (HD) Internet of Things (IoT) cameras are gaining popularity for organizational perimeter surveillance and security monitoring. Transmitting HD video data to cloud infrastructure requires high bandwidth and more storage space than text, audio, and image data. It becomes more challenging for large-scale organizations with massive security grids to minimize cloud network bandwidth and storage costs. This paper presents an application of Machine Vision at the IoT Edge (Mez) technology in association with a novel Grid Sensing (GRS) algorithm to optimize cloud Infrastructure as a Service (IaaS) resource allocation, leading to cost minimization. Experimental results demonstrated a 31.29% reduction in bandwidth and a 22.43% reduction in storage requirements. The Mez technology offers a network latency feedback module with knobs for transforming video frames to adjust to the latency sensitivity. The association of the GRS algorithm introduces its compatibility in the IoT camera-driven security grid by automatically ranking the existing bandwidth requirements by different IoT nodes. As a result, the proposed system minimizes the entire grid's throughput, contributing to significant cloud resource optimization. [ABSTRACT FROM AUTHOR]

This paper focuses on the portability of Cloud Computing (CC) services, specifically on the problems with the portability of Infrastructure as a Service (IaaS). We analyze the current state of CC with the intention of standardizing the portability of CC solutions. CC IaaS providers often use proprietary solutions, which leads to a problem known as "vendor lock-in". Another problem might appear during migration between two providers if huge scripts are written in a proprietary language. To solve the portability problem, we applied the Model-Driven Architecture (MDA) approach to propose the general IaaS reference architecture. Using a generic IaaS model, we are able to describe entities of the IaaS environment and then design necessary transformation rules for specific IaaS environments in a simplified but flexible way. Using this model, we continue designing transformation rules that define the transcript of IaaS services. The CC-portability problem is thus solved by transforming a specific IaaS service description from one description to another through the generic model. This approach is extensible and can be adopted for the evolution of CC services. Therefore, it can be used as a generic solution to IaaS-portability issues. Using this flexible approach, the introduction of a new CC environment requires only the design of a single transformation rule that prevents proprietary peer-to-peer full-mesh mappings. Thanks to the proposed model and the transformation rules described, we were able to experimentally confirm the functionality of the transfer of the environment description between three cloud providers. [ABSTRACT FROM AUTHOR]

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Cloud computing has gained popularity at a breakneck pace over the last few years. It has revolutionized the way businesses operate by providing a flexible and scalable infrastructure for their computing needs. Cloud providers offer a range of services with a variety of pricing schemes. Cloud pricing schemes are based on functional factors like CPU, RAM, and storage, combined with different payment options, such as pay-per-use, subscription-based, and non-functional aspects, such as scalability and availability. While cloud pricing can be complicated, it is critical for businesses to thoroughly assess and compare pricing policies along with technical requirements to ensure they design an investment strategy. This paper evaluates current pricing strategies for IaaS, CaaS, and PaaS cloud services and also focuses on the three leading cloud providers, Amazon, Microsoft, and Google. To compare pricing policies between different services and providers, a hedonic price index is constructed for each service type based on data collected in 2022. Using the hedonic price index, a comparative analysis between them becomes feasible. The results revealed that providers follow the very same pricing pattern for IaaS and CaaS, with CPU being the main driver of cloud pricing schemes, whereas PaaS pricing fluctuates among cloud providers. [ABSTRACT FROM AUTHOR]

Introducción: la higiene de manos (HM) constituye una herramienta esencial para evitar la transmisión de microorganismos a través del contacto entre un paciente y otro, disminuyendo la aparición de Infecciones Asociadas a la Atención de Salud (IAAS), lo que repercute en disminución de la morbilidad y mortalidad en pacientes hospitalizados. El objetivo del presente trabajo fue determinar la adherencia del personal de salud al cumplimiento de los 5 momentos de la HM en el área de hospitalización del Centro Médico Docente La Trinidad (CMDLT), evaluando cada uno de los 5 momentos de la Organización Mundial de la Salud. Metodología: se llevó a cabo un estudio observacional prospectivo de corte transversal, donde se evaluaron los espacios de observación para conocer su estado de funcionamiento y posteriormente se realizaron 100 observaciones directas encubierto de oportunidades HM en el personal de salud del área de hospitalización. Toda la información fue recogida en formularios, tabulada en Excel y posteriormente analizada mediante estadística descriptiva. Resultados: se obtuvo una adherencia del 25% en general, 36% en el personal médico y 15,91% en el personal de enfermería. En el 75% de las oportunidades se omitió la HM. El momento donde se realizaron más acciones de HM fue el 4to. Conclusión: se evidenció un aumento de la adherencia a la HM en el CMDLT en comparación a la obtenida en el año 2016. El grupo con mayor adherencia fue el personal médico seguido del personal de enfermería.

The article presents the results of a study on modeling cloud systems. In this research, the authors developed both analytical and simulation models. System analysis was conducted at the level of virtual machine support, corresponding to Infrastructure as a Service (IaaS). The models assumed that virtual machines of different sizes are offered as part of IaaS, reflecting the heterogeneous nature of modern systems. Additionally, it was assumed that due to limitations in access to physical server resources, only a portion of these resources could be used to create virtual machines. The model is based on Markov chain analysis for state-dependent systems. The system was divided into an external structure, represented by a collection of physical machines, and an internal structure, represented by a single physical machine. The authors developed a novel approach to determine the equivalent traffic, approximating the real traffic appearing at the input of a single physical machine under the assumptions of request distribution. As a result, it was possible to determine the actual request loss probability in the entire system. The results obtained from both models (simulation and analytical) were summarized in common graphs. The studies were related to the actual parameters of commercially offered physical and virtual machines. The conducted research confirmed the high accuracy of the analytical model and its independence from the number of different instances of virtual machines and the number of physical machines. Thus, the model can be used to dimension cloud systems. [ABSTRACT FROM AUTHOR]

The ICTs industry is witnessing the rise of a groundbreaking technology known as cloud computing. This innovation is built upon the Internet, offering a virtual infrastructure for computers. This virtual setup encompasses databases, servers, storage, software, networking, processing power, office applications, and artificial intelligence. What sets cloud computing apart is its capacity to deliver services without the need for physical proximity to the underlying computer hardware. This approach brings with it numerous advantages over traditional computing methods, including grid computing. Also Cloud Computing introducing new challenges for environmental protection. This paper offers an extensive survey, overview and analysis of the fundamental concepts, historical evolution, components of cloud computing, diverse cloud services and categories, cloud integration, the merits of this technology, its focal areas, security considerations, and the implications of adopting cloud computing. This comprehensive analysis is based on the examination of more than 26 research papers and articles. The findings of this survey shed light on the transformation that the IT industry undergoes before and after embracing cloud computing. [ABSTRACT FROM AUTHOR]

Cloud computing has become a major component of the modern IT ecosystem. A key contributor to this has been the development of Infrastructure as a Service (IaaS) architecture, in which users' virtual machines (VMs) are run on the service provider's physical infrastructure, making it possible to become independent of the need to purchase one's own physical machines (PMs). One of the main aspects to consider when designing such systems is achieving the optimal utilization of individual resources, such as processor, RAM, disk, and available bandwidth. In response to these challenges, the authors developed an analytical model (the ARU method) to determine the average utilization levels of the aforementioned resources. The effectiveness of the proposed analytical model was evaluated by comparing the results obtained by utilizing the model with those obtained by conducting a digital simulation of the operation of a cloud system according to the IaaS paradigm. The results show the effectiveness of the model regardless of the structure of the emerging requests, the variability of the capacity of individual resources, and the number of physical machines in the system. This translates into the applicability of the model in the design process of cloud systems. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to explain how enterprise resource planning (ERP) implementation evolves by cloud computing in different industries with different delivery models of cloud ERP. This paper also investigates infrastructure as a service (IaaS) as a delivery approach for cloud ERP. Case research on IaaS is rarely found in the literature. In addition, this paper intends to reveal how this transformation from on-premises to the cloud would influence the ERP implementation process. Design/methodology/approach: A multiple-case study is conducted to identify the different deployed models of cloud ERP systems in the implementation projects. The influences of emerging cloud computing technology on ERP implementation are investigated by interviewing consultants related to the projects. Findings: The findings illustrate that not only software as a service (SaaS) but also IaaS and platform as a service cloud computing services are widely applied in cloud ERP implementation. This study also indicates that certain technical limitations of cloud ERP might have a positive effect on the outcome of ERP implementation. Originality/value: This study investigates how cloud computing influences ERP implementation from different aspects. The result identifies both SaaS and IaaS as two different approaches widely adopted in cloud ERP implementation. Besides, this study has discussed in-depth and analyzed these two cloud ERP paradigms in five factors, including functionality, performance, portability, security, cost and customization. The classification and suggestions are original to the literature. [ABSTRACT FROM AUTHOR]

Abstract As infrastructure-as-a-service clouds quickly grow, an increasing number of businesses and people are moving their application development to the cloud. The purpose of the research is to solve the problem of identifying memory anomalies in cloud virtual machines and improve the accuracy of the model in detecting abnormal situations. This paper presents a model for detecting virtual machine anomalies in IaaS cloud platform. The model considers the unique properties of monitoring metrics as time-series data and proposes an approach based on four important virtual machine monitoring metrics. The study also develops an adaptive anomaly detection system based on deep Q-network algorithms and migration learning principles for the variety of VM monitoring data in the cloud. The testing findings reveal that utilizing a Zoom layer with a 2-kernel size can increase detection accuracy to 96.7%. This demonstrates that a portion of the experimental data can extract the temporal features using the Zoom layer and different kernel sizes. The research model for anomaly detection had a classification accuracy of 99.8%. The deep Q-network model’s final anomaly detection accuracy varies from 96.7 to 98.6%. The outcomes of the research improved the system’s security and dependability, showed the worth of the overall framework design, and significantly decreased the number of resources needed for system operation and maintenance.

As infrastructure-as-a-service clouds quickly grow, an increasing number of businesses and people are moving their application development to the cloud. The purpose of the research is to solve the problem of identifying memory anomalies in cloud virtual machines and improve the accuracy of the model in detecting abnormal situations. This paper presents a model for detecting virtual machine anomalies in IaaS cloud platform. The model considers the unique properties of monitoring metrics as time-series data and proposes an approach based on four important virtual machine monitoring metrics. The study also develops an adaptive anomaly detection system based on deep Q-network algorithms and migration learning principles for the variety of VM monitoring data in the cloud. The testing findings reveal that utilizing a Zoom layer with a 2-kernel size can increase detection accuracy to 96.7%. This demonstrates that a portion of the experimental data can extract the temporal features using the Zoom layer and different kernel sizes. The research model for anomaly detection had a classification accuracy of 99.8%. The deep Q-network model's final anomaly detection accuracy varies from 96.7 to 98.6%. The outcomes of the research improved the system's security and dependability, showed the worth of the overall framework design, and significantly decreased the number of resources needed for system operation and maintenance. [ABSTRACT FROM AUTHOR]

Orchestration methods at Infrastructure-as-a-Service (IaaS) level automate the deployment, scaling, and management of virtualized resources, typically across multiple hosts and data centres. While orchestration provides many advantages, itealso introduces several challenges in testing and debugging phases, particularly due to the distributed nature of the virtualized resources. Even the proper initial deployment of interdependent virtual machines (VM) may cause fatal errors since the unpredictable timing conditions may change the overall initialisation method, which can lead to abnormal behaviour, i.e. inecomplex, non-deterministic environments, the set of VM configurations can drift from their expected states (‘configuration drift’). The overall motivation of our research is to improve the reliability of cloud-based infrastructures with minimal user interactions and significantly automate the time-consuming debugging process. This paper focuses on the examination and behaviour of cloud-based infrastructures during their deployment phase. Weecontinued the adaption of a replay-active control based debugging technique, called macrostep, inethe field of cloud orchestration. Ineorder to provide efficient support for developers troubleshooting major deployment related errors, the fundamental macrostep mechanisms have been enriched and significantly extended including 1) the automated generation of collective breakpoint sets, 2) parallel and robust traversal method for such consistent global states with 3) automated evaluation of global predicates in each global state of VM set. Furthermore, the novel methods have been 4) generalized towards wider user scenarios by targeting the Terraform orchestration tool as well (besides the already supported Occopus). The paper describes the significantly enhanced approach, our design choices, and also the implementation of the experimental debugger tool with a use case for validation purposes by addressing the deployment of a SLURM (HPC) cluster.

This paper focuses on the portability of Cloud Computing (CC) services, specifically on the problems with the portability of Infrastructure as a Service (IaaS). We analyze the current state of CC with the intention of standardizing the portability of CC solutions. CC IaaS providers often use proprietary solutions, which leads to a problem known as “vendor lock-in”. Another problem might appear during migration between two providers if huge scripts are written in a proprietary language. To solve the portability problem, we applied the Model-Driven Architecture (MDA) approach to propose the general IaaS reference architecture. Using a generic IaaS model, we are able to describe entities of the IaaS environment and then design necessary transformation rules for specific IaaS environments in a simplified but flexible way. Using this model, we continue designing transformation rules that define the transcript of IaaS services. The CC-portability problem is thus solved by transforming a specific IaaS service description from one description to another through the generic model. This approach is extensible and can be adopted for the evolution of CC services. Therefore, it can be used as a generic solution to IaaS-portability issues. Using this flexible approach, the introduction of a new CC environment requires only the design of a single transformation rule that prevents proprietary peer-to-peer full-mesh mappings. Thanks to the proposed model and the transformation rules described, we were able to experimentally confirm the functionality of the transfer of the environment description between three cloud providers.

Cloud computing technologies have come a long way and are available to virtually any company today. However, which factors will cause the company to decide to implement these services? Based on existing research abroad, we compiled a Technology-Organization-Environment (TOE) framework and proposed questions that support individual factors in our model to address this problem. Small and medium-sized enterprises (SMEs) in the Czech Republic actively participated in the research, from which we received 99 valid responses. Our results show a significant influence of four factors. The first factor is relative advantage, and the second is competitive pressure. In our case, companies are convinced that thanks to cloud computing, they will gain a more advantageous position over competitors, especially in the area of costs, increased productivity and entry into new industries. At the same time, they are convinced that competing cloud computing companies are implementing and taking advantage of it. The third factor is compatibility. This factor may be the cause of the temporary expansion of only simple implementations. The fourth factor is industry. So, companies perceive pressure to implement cloud computing in their business area. To support the further expansion of cloud computing, it is necessary to continue highlighting the cost benefits of cloud computing. At the same time, it certainly makes sense to bring new applications with a simple billing model and simple integration between the most used applications.

The wide range of technological innovations took place in the post-millennium period and later became widely available have fundamentally reshaped the relationship between ICT infrastructure and business in the life of for-profit organizations. This is particularly true for the so-called primary (value-creating) processes. These technologies that have fundamentally transformed the way business organizations operate are collectively referred to as "Industry 4.0" technologies. A significant group of these technologies are the cloud-based solutions. By using these solutions, the users can get benefits from the ICT infrastructure through a third-party service. These services can be applications made available on a specific online interface (SaaS), or various platform elements (PaaS, for example: application servers, virtual servers, etc.) or infrastructure elements such as leased computing capacity (IaaS). By using cloud-based solutions, the customer can avoid the investment needed to run the ICT infrastructure and the additional problems rooted by the operation of ICT-infrastructure. A questionnaire survey was carried out in early 2019, in which 498 respondents voluntarily participated. The survey investigated the deployment level of ICT infrastructure among SMEs in Hungary, its information security consequences and how the management of the for-profit organizations evaluate the contribution of ICT infrastructure to the success of their business. The use of different cloud-based solutions was also measured in the mentioned questionnaire. The paper presents the research results of the prevalence of use of different cloud solutions with similar studies in the region. Furthermore, it examines whether cloud solutions have individually detectable beneficial effects on business operations, and whether synergies can be identified when multiple cloud applications are used together. Based on the qualitative results of the research and their interpretation, a broader analysis has been offered.

The digital era, marked by the exponential growth of data and advancements in technology, has been revolutionized by the integration of cloud computing and big data. These technologies have transformed the way data is generated, stored, and analyzed, with applications extending across various fields, including scientific research. Cloud computing provides scalable, flexible, and cost-efficient infrastructure, while big data analytics enables the extraction of valuable insights from vast, diverse datasets. Together, they address the challenges posed by the increasing volume, velocity, and variety of data, facilitating innovation and smarter decisionmaking. This review explores the fundamentals of cloud computing, its deployment and service models, the defining characteristics of big data, and their synergistic impact on scientific research. It highlights the transformative role of these technologies in enabling data-driven discoveries while acknowledging challenges such as data security and integration complexities. As the adoption of cloud-based big data technologies grows, their potential to drive innovation and enhance scientific collaboration continues to expand. [ABSTRACT FROM AUTHOR]

With the proliferation of IoT devices, there has been exponential growth in data generation, placing substantial demands on both cloud computing (CC) and internet infrastructure. CC, renowned for its scalability and virtual resource provisioning, is of paramount importance in e-commerce applications. However, the dynamic nature of IoT and cloud services introduces unique challenges, notably in the establishment of service-level agreements (SLAs) and the continuous monitoring of compliance. This paper presents a versatile framework for the adaptation of e-commerce applications to IoT and CC environments. It introduces a comprehensive set of metrics designed to support SLAs by enabling periodic resource assessments, ensuring alignment with service-level objectives (SLOs). This policy-driven approach seeks to automate resource management in the era of CC, thereby reducing the dependency on extensive human intervention in e-commerce applications. This paper culminates with a case study that demonstrates the practical utilization of metrics and policies in the management of cloud resources. Furthermore, it provides valuable insights into the resource requisites for deploying e-commerce applications within the realms of the IoT and CC. This holistic approach holds the potential to streamline the monitoring and administration of CC services, ultimately enhancing their efficiency and reliability. [ABSTRACT FROM AUTHOR]

Many companies or institutions either already have placed their resources in or plan to move them to the cloud. They do so for security reasons and are weary of the fact that by relying on cloud-based resources, they do not have to bear such extensive infrastructure-related costs. However, continuous technology advancement results not only in benefits, but also in disadvantages. The latter include the growing risk associated with IT security, forcing the individual actors to implement monitoring measures and to respond to numerous threats. This work focuses on creating a small infrastructure setup using the publicly available Google Cloud Platform which, thanks to the monitoring systems implemented thereon, allows to rapidly respond to hardware and software faults, including those caused by external factors, such as attacks on specific components. This project may also be customized to satisfy individual needs, depending on the cloud service provider selected. The work uses public cloud provider tools as well as open-source systems available for everyone, both in the cloud and in the on-prem environment. The paper deals also with the concept of a proprietary intrusion detection system.

Many companies or institutions either already have placed their resources in or plan to move them to the cloud. They do so for security reasons and are weary of the fact that by relying on cloud-based resources, they do not have to bear such extensive infrastructure-related costs. However, continuous technology advancement results not only in benefits, but also in disadvantages. The latter include the growing risk associated with IT security, forcing the individual actors to implement monitoring measures and to respond to numerous threats. This work focuses on creating a small infrastructure setup using the publicly available Google Cloud Platform which, thanks to the monitoring systems implemented thereon, allows to rapidly respond to hardware and software faults, including those caused by external factors, such as attacks on specific components. This project may also be customized to satisfy individual needs, depending on the cloud service provider selected. The work uses public cloud provider tools as well as open-source systems available for everyone, both in the cloud and in the onprem environment. The paper deals also with the concept of a proprietary intrusion detection system. [ABSTRACT FROM AUTHOR]

The paper aims at mitigating hot-spots during Offline Scheduling in IaaS (Infrastructure-as-a-Service) cloud systems. Unlike previous studies, the research focuses on identifying and resolving hot-spots not at servers, but at server racks. A two-phase algorithm for performing power-aware offline scheduling is proposed. The first phase aims at identifying and mitigating hot-spots at racks, while the second phase performs VM consolidation, i.e. minimization of the number of occupied servers while maintaining a feasible VM mapping and low migration costs. The proposed algorithm takes into account the dynamic nature of VM's resource consumption: it does not only resolve detected hot-spots, but also tries to avoid hot-spots in a reasonable future time period. The algorithm was tested with the data from a real IaaS cloud with different sets of algorithm's parameters. Experimental evaluation showed that the statistical estimates of the future VM's resource consumption provide the most reliable mapping, which is a result of minimization of the number of new hot-spot occurrences. • A server rack is a hot-spot if it exceeds the upper bound of power consumption. • Hot-spot identification, mitigation, and avoidance is crucial for IaaS systems. • An algorithm is proposed for rack hot-spot mitigation and consolidation of VMs. • Considering future CPU utilization values is associated with migrating more memory. • Ignoring future CPU utilization values leads to more new hot-spots happening shortly. [ABSTRACT FROM AUTHOR]

Abstract Many currently existing cloud cost optimisation solutions are aimed at cloud infrastructure providers, and they often deal only with specific types of application services. Unlike infrastructure providers, the providers of cloud applications are often left without a suitable cost optimisation solution, especially concerning the wide range of different application types. This paper presents an approach that aims to provide an optimisation solution for the providers of applications hosted in the cloud environments, applicable at the early phase of a cloud application lifecycle and for a wide range of application services. The focus of this research is the development of the method for identifying optimised service deployment option in available cloud environments based on the model of the service and its context, intending to minimise the operational cost of the cloud service while fulfilling the requirements defined by the service level agreement. A cloud application context metamodel is proposed that includes parameters related to both the application service and the cloud infrastructure relevant for the cost and quality of service. By using the proposed optimisation method, knowledge is gained about the effects of the cloud application context parameters on the service cost and quality of service, which is then used to determine the optimal service deployment option. The service models are validated using cloud applications deployed in laboratory conditions, and the optimisation method is validated using the simulations based on the proposed cloud application context metamodel. The experimental results based on two cloud application services demonstrate the ability of the proposed approach to provide relevant information about the impact of cloud application context parameters on service cost and quality of service and use this information for reducing service operational cost while preserving the acceptable service quality level. The results indicate the applicability and relevance of the proposed approach for cloud applications in the early service lifecycle phase since application providers can gain valuable insights regarding service deployment decision without acquiring extensive datasets for the analysis.

It is necessary to provide the cyber security of cloud computing according to the components that constitute its structure. The first step in advancing the cyber security of this technology is to accurately identify its threats. In this paper, a new cyber security reference model of the cloud system, which consists of components making up separate layers of cloud computing is proposed. Available reference models of cloud computing security do not describe the virtualization and service layers and the important components for providing the cyber security of cloud computing in detail, do not consider the social media IoT sensor layer, which collects the text data typed by attackers to carry out cyber attacks on the cloud infrastructure, and the cyber resilience issues of the cloud computing at all In addition, this paper studies the cyber security issues of cloud computing service models, and constructs an attack model to provide security of cloud systems. It gives an interpretation of standards and legislative acts on the cyber security of cloud computing. According to security aspects, clarification of the cyber security and cyber resilience concepts of cloud systems is provided. The cyber resilience architecture of intelligent cloud systems is developed. The advantage of developed cyber resilience model over available one is that, it determines the information security and cyber security aspects of cloud computing and combines them to form the cyber resilience aspects of cloud systems.