Features Of Cloud Services Research Articles

Students’ Perceptions and Experiences of Using Cloud Services in Online Education During Pandemic Time: A Post-Covid Study

ABSTRACT The study examines how Dhaka University students used cloud technologies to assist their online learning throughout the pandemic. A quantitative survey was carried out to collect data from the students of Dhaka University. The questionnaire included both open and close-ended questions and 5-point Likert scale items. Data were collected both online and using printed questionnaires. In this way, a total of 388 responses were recorded. IBM SPSS version 20 was used to analyze the collected data. This study found that the majority of the students used Zoom and Gmail for their online lessons. Students were pleased with the different features of cloud services including the privacy and security of data, free or pay-per-use opportunities, and personalized learning opportunities. The most common hurdle encountered by students, according to the results, was a poor internet connection. The study found no significant differences in the students’ experiences of utilizing cloud services in online learning based on their educational level, gender, age, location of attending online classes, computer experience, cloud use training, and duration of internet use.

Analysis of the Comparison of Selective Cloud Vendors Services

Cloud computing refers to a location that allows us to preserve our precious data and use computing and networking services on a pay-as-you-go basis without the need for a physical infrastructure. Cloud computing now provides us with powerful data processing and storage, exceptional availability and security, rapid accessibility and adaption, ensured flexibility and interoperability, and time and cost efficiency. Cloud computing offers three platforms (IaaS, PaaS, and SaaS) with unique capabilities that promise to make it easier for a customer, organization, or trade to establish any type of IT business. We compared a variety of cloud service characteristics in this article, following the comparing, it's straightforward to pick a specific cloud service from the possible options by comparison with three chosen cloud providers such as Amazon, Microsoft Azure, and Digital Ocean. By using findings of this study to not only identify similarities and contrasts across various aspects of cloud computing, as well as to suggest some areas for further study.

A Comparative Study on Three Selective Cloud Providers

Cloud Computing means a place where we can store our valuable information of data and access the computing and networking services following the pay-as-you-go method without a physical environment. In the present day, cloud computing offers us powerful computing and storage, high availability and security, instant accessibility and adaptation, guaranteed scalability and interoperability, and cost and time effectiveness. Cloud computing has three platforms (IaaS, PaaS, SaaS) with exclusive features which assure to make easy their work for a client, Organization or Trade to build up any kind of IT business. In this paper, we managed a comparison of cloud service features and after the comparison, It's simple to select a certain cloud service from the available features by comparison with three selective cloud providers like Amazon, Microsoft Azure and Digital Ocean. Using the result of this survey to not only find the similarities and differences between various elements of cloud computing but also to propose some topics to look into for further research.

A Study on Cloud Service Selection Using Weighted Sum Model

Cloud service selection is essential in selecting on-demand services on a subscription basis. Since there are many cloud services with comparable functionality, it is important to find out which one best meets the needs and goals of the customer. A cloud service is perfect for the customer choosing a cloud service can be complicated Because of this, decision-making becomes a problem Services performance and cost are fundamentally different. In addition, many characteristics of cloud services there are these entire providers into account when selecting should be taken. When there are Many criteria, a compromise to be done, because in most real-world situations, which A service and other on all criteria Does not perform better than everyone else.However, a service of certain criteria may be fundamentally more advanced, other when judged by other criteria Solutions can be better than that. Action research is multifaceted Criteria that make decisions. A subfield called Contains; it is multi-criteria Focus on problem solving methods pays. Multiple criteria there are many techniques for decision-making. QoS based service in the cloud Several MCDM techniques to choose from although published, very useful the choice of technique is still remains debatable.

Information flow perception modeling and optimization of Internet of Things for cloud services

IoT (Internet of Things) information flow perception is the foundation of the IoT architecture and plays an important role. Providing cloud services on demand is one of the key issues and core features of cloud services in the IoT information flow perception. Firstly, we study the mixed logic dynamics (MLD) modeling method of IoT information flow awareness, and use IoT sensor nodes, controlled nodes, and coordination nodes to describe system application scenarios, and use automata for internal information transmission. Secondly, an open queuing method based on large-scale information flow perception modeling and network delay analysis method is proposed. By analyzing the end-to-end delay of the node path and the average delay analysis of the whole queuing network, the open queuing is obtained. Aiming at the characteristics of distributed data of information flow perception in the IoT, a priority-based queuing network is proposed to model and analyze the aggregation nodes based on embedded multi-core SoC(System on Chip), which greatly improves the performance of embedded multi-core SoC.

RHAS: robust hybrid auto-scaling for web applications in cloud computing

The elasticity characteristic of cloud services attracts application providers to deploy applications in a cloud environment. The scalability feature of cloud computing gives the facility to application providers to dynamically provision the computing power and storage capacity from cloud data centers. The consolidation of services to few active servers can enhance the service sustainability and reduce the operational cost. The state-of-art algorithms mostly focus either on reactive or proactive auto-scaling techniques. In this article, a Robust Hybrid Auto-Scaler (RHAS) is presented for web applications. The time series forecasting model has been used to predict the future incoming workload. The reactive approach is used to deal with the current resource requirement. The proposed auto-scaling technique is designed with the threshold-based rules and queuing model. The security mechanism is used to secure the user’s request and response to the web-applications deployed in cloud environment. The designed approach has been tested with two real-time web application workloads of ClarkNet and NASA. The proposed technique achieves $$14\%$$ reduction in cost, and significant improvement in response time, service level agreement (SLA) violation, and gives consistency in CPU utilization.

CloudFNF: An ontology structure for functional and non-functional features of cloud services

Recently, cloud computing becomes one of the main orientations of many researchers and companies in the IT area. Therefore, a huge number of cloud services have been developed. Because of the diversity and heterogeneity in providing these services, it is urgently needed to develop a unified cloud ontology. Such ontology can classify these services appropriately and participate as a mapping layer to present such services in a unified description format. Although many studies have been conducted to build cloud ontologies, they have adopted the cloud service layers-based structure. On the other hand, the existing cloud services may involve functionalities from different layers due to the continual increase in the complexity of customer demands. Unfortunately, there are no clear relations to organize the interventions among these layers. Therefore, such services may be difficult to be classified into a specific cloud service layer. Additionally, the layers-based structure of ontologies represents an obstacle to address important issues, such as cloud service recommendation. Despite there are few cloud service functionality-based cloud ontologies, they suffer from many overlaps, lack of semantic relations, or poor granularity of concepts. Also, all the existing cloud ontologies (i.e., layers-based and functionality-based) lack important criteria, such as completeness, consistency, conciseness, clarity, preciseness, and granularity. In this paper, a comprehensive cloud ontology called CloudFNF has been proposed to overcome such drawbacks. According to the structure of the proposed ontology, cloud services are classified as functionality-based instead of layers-based. Also, non-functional features of cloud services (i.e., configuration and QoS features) are considered to enable services of the same functionalities to be ranked efficiently. Based on our previously suggested cloud ontology evaluation framework, our proposed cloud ontology has been evaluated compared to the most related cloud ontologies. The evaluation results show that the proposed CloudFNF ontology outperforms the other ontologies.

RETRACTED ARTICLE: A novel centralized cloud information accountability integrity with ensemble neural network based attack detection approach for cloud data

Highly scalable services are enabled by cloud computing and easily consumed over the Internet. User data is normally stored remotely in cloud services. Users do not own or operate the machines. This is a key feature of cloud services. Adoption of cloud services by the users are affected due to the fact that the users’ concerns about having to lose control on their own information and some attackers will hack them. Therefore to overcome the existing issues, this work proposed a new centralized cloud information accountability integrity with imperialist competitive key generation algorithm (CCIAI-ICKGA) is to resolve the above problems. It also provides the attack detection to monitor the practical utilization of the users’ information in the cloud environment. Second, cipher text-policy attribute-based encryption (CP-ABE) with key generation employing ICKGA and trapdoor generator is used to generate the public and private keys for every user. Third, the trapdoor generator ensures data integrity at the user level and also the cloud server level. At last, a dynamically weighted ensemble neural networks (DWENN) classifier is used for attack detection in the network. Additional guarantees of integrity and authenticity are provided by updating the structure of the log records. It is extended the framework in order to provide the security analysis which detects more possible attacks. Finally, a simulation result is carried out and renders a detailed performance analysis of the system. A result from rigorous experimental evaluation demonstrates the efficacy and resourcefulness of the novel CCIAI-ICKGA framework.

Simulasi Analisis Bukti Digital Pada Layanan Cloud Computing Menggunakan Metode NIST 800-86

Ease and support for cloud-based data storage It has supported an increase in the number of cloud services. The increasing number of uses for cloud services also increases the number of digital-based criminal actions related to the addition of facilities to cloud services. The cloud service feature designed to store data to support the smooth running of business processes can be misused by criminal assistance to store crime data. Accurate digital evidence is one way to prove a digital crime, which can then be used as supporting evidence in the trial. This study discusses the analysis of digital evidence from a cloud service. The analysis process using the NIST 800-86 method is carried out on digital evidence from 5 previously prepared scenarios related to the use of cloud service features that use being misused. Data acquisition techniques use the method of direct acquisition and physical imaging to obtain digital evidence. The experimental results showed that after scenario 1 and scenario 3, information on the file name and directory of the paths downloaded by client 1 and client 2 were obtained with information on the IP address, mac address, user name, password and time stamp. After scenario 2, digital evidence has been obtained that contains information on the name and location of the folder on the cloud server. After scenario 4, information on the name of the file and the shared folder is successfully obtained, equipped with client information that has the right to access the files and folders. After scenario 5, information about the file name and directory of the file path is successfully obtained.

Бізнес-моделі хмарного надання ІТ-послуг

Бізнес-моделі хмарного надання ІТ-послуг

A field-based service management and discovery method in multiple clouds context

In diverse and self-governed multiple clouds context, the service management and discovery are greatly challenged by the dynamic and evolving features of services. How to manage the features of cloud services and support accurate and efficient service discovery has become an open problem in the area of cloud computing. This paper proposes a field model of multiple cloud services and corresponding service discovery method to address the issue. Different from existing researches, our approach is inspired by Bohr atom model. We use the abstraction of energy level and jumping mechanism to describe services status and variations, and thereby to support the service demarcation and discovery. The contributions of this paper are threefold. First, we propose the abstraction of service energy level to represent the status of services, and service jumping mechanism to investigate the dynamic and evolving features as the variations and re-demarcation of cloud services according to their energy levels. Second, we present user acceptable service region to describe the services satisfying users’ requests and corresponding service discovery method, which can significantly decrease services search scope and improve the speed and precision of service discovery. Third, a series of algorithms are designed to implement the generation of field model, user acceptable service regions, service jumping mechanism, and user-oriented service discovery.We have conducted an extensive experiments on QWS dataset to validate and evaluate our proposed models and algorithms. The results show that field model can well support the representation of dynamic and evolving aspects of services in multiple clouds context and the algorithms can improve the accuracy and efficiency of service discovery.

Cloud service recommendation based on unstructured textual information

With the rapid growth of cloud service platform, it is more and more difficult for users to discover cloud services satisfying their personalized demands. The unstructured textual information, such as the service descriptive texts and service tags, contain rich features of cloud services and are useful for cloud service platforms to construct personalized service recommendation. This paper proposes a two-stage model for cloud service recommendation by integrating the information of service descriptive texts and service tags. In the first stage, we propose a Hierarchical Dirichlet Processes (HDP) model to cluster cloud services into an optimal number of clusters based on descriptive texts. In the second stage, we propose a Personalized PageRank algorithm based on service tags to rank and recommend cloud services in each cluster. Our experiments on a real data set show that the proposed two-stage model can segment cloud services well and obtain accurate recommendation results compared with the baseline methods.

A Blockchain-based Model for Cloud Service Quality Monitoring

This paper introduces a novel blockchain-based decentralized federation model that embodies quality verification for cloud providers who lease computing resources from each other. The blockchain structure removes the barriers of a traditional centralized federation and offers a fully distributed and transparent administration by enforcing the involved agents to maintain consensus on the data. For a blockchain-based federation, it is vital to avoid blind-trust on the claimed SLA guarantees and monitor the quality of service which is highly desirable considering the multi-tenancy characteristic of cloud services. Due to the fact that the blockchain network is unable to access the outside world, it cannot handle, by its own, providers misbehavior in terms of SLA violations. Thus, we introduce oracle as a verifier agent to monitor the quality of the service and report to the smart contract agents deployed on the blockchain. Oracle is a trusted third-party agent who can communicate with the outside world of the blockchain network. The interaction between cloud service providers (either providing a service or requesting it from another provider) and the oracle through smart contracts comprises a system of autonomous and utility maximizer agents. Cloud requesters seek to receive high quality services with constant monitoring at cheap prices or even with no charge, while cloud providers aim to have a balanced work-load with less preserved capacity, and the oracle tends to charge higher for their monitoring services. Therefore, to model this conflicting situation, we formulate a dynamic Stackelberg differential game to optimize the cost of using the oracle and maximize the profit of the agents with the role of provider agent as a leader, and the requester and verifier agents as followers. Our designed Stackelberg differential game can seize the dynamicity of users’ demand and resource provisioning in a competitive cloud market. We implemented our proposed decentralized model using the Solidity language in the remix IDE on the Ethereum network. We further evaluated the optimal controls and agents’ profit with real-world data simulated for three concrete cloud providers. The results revealed that the requester agent initiates most of the quality verification requests at the beginning to the middle time of the contract. Thus, the provider agent could reserve less computing resources considering the fact that it could share the workload among other customers’ computing resources during the peak-time. Moreover, imposing a higher penalty on the provider agent increased the capacity and decreased the number of requests for quality verification at the equilibrium. The evaluation also disclosed that the impact of timing in the dynamic pricing strategy of the verifier agent is very minimal, and the provisioning capacity of the provider is strongly correlated with the monitoring price.

Towards evaluation of cloud ontologies

Many enterprises consider that cloud computing will continue to play a key role due to its ability to deliver various types of on-demand IT services, and according to customer needs. Although cloud technology has characteristics that distinguish it from other technologies, unfortunately, services in this technology suffer from the heterogeneity issue. As a result, cloud services have many challenges, such as service description, interoperability, and service discovery. Many studies have contributed dealing with such challenges using ontology, as it can participate as a mapping layer to present such services in a unified description. Cloud ontologies included in these studies can be classified into three types of ontologies; ontologies that focus on representing functional features of cloud services, ontologies that focus on non-functional features, and ontologies that focus on both function and non-function features. These ontologies are not unified in representing such features, where they agree in some features and differ in others. By using techniques developed to evaluate ontologies related to the other technologies, it is difficult to decide which cloud ontology captures cloud services in the right way. We have noticed that there is not a framework or a benchmark to evaluate the cloud ontologies. In this survey, the current cloud ontologies are analyzed to identify a set of observations that represent their strengths and weaknesses. According to these observations, a set of suggested rules are introduced and verified to be considered during the construction or the evaluation of cloud ontologies. The work in this paper can be considered as the fundamental step for developing a formal framework to automatically evaluate cloud ontologies.

Cloud Service Description Model: An Extension of USDL for Cloud Services

There are a variety of well-designed specification-modelling-languages serving Internet services, however, none of them is capable of describing the special features of cloud services, from both technical and business points of view. The Unified Service Description Language (USDL) provides a new way to describe Internet services from business, operational, and technical perspectives. Nevertheless, there are various issues with USDL: it lacks a comprehensive specification model, particularly for cloud services, lacks a user-centric specification modeling paradigm, lacks a mechanism to measure cloud service attributes and to present the association relationship and re-usability of the attributes, and lacks semantic representation of cloud services. Based on the above issues, we propose a unified semantic Cloud Service Description Model (CSDM) in this paper. The proposed model will be extended from the basic structure of USDL, by defining cloud-service-specific attributes. Furthermore, an additional module, named transaction module, will be defined, which models the rating system of cloud services from several aspects, such as risk, trust, and reputation. The transaction module facilitates the capability of CSDM with regard to service ranking, and enhances its flexibility and extensibility by providing an extensible sub-module. In addition, we design an OWL-based annotation system to enrich the semantic expressivity of this model. Finally, a case study is provided to explain the application of this model in actual cloud services.

An ontology-based cloud infrastructure service discovery and selection system

In recent years, due to global economic downfall, many of the organisations have resorted to downsizing their Information Technology (IT) expenses by adopting innovative computing models like cloud computing, which allows business houses to reduce their fixed IT costs by promising a greener, scalable, cost-effective alternative to utilise the IT resources. A growing number of pay-per-use cloud services are now available on the web in the form of Software as a Service (SaaS), Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). With the increase in the number of services, there has also been an increase in demand and adoption of cloud services making cloud service identification and discovery a challenging task. This is due to varied service, descriptions, non-standardised naming conventions, heterogeneity in type and features of cloud services. Thus, selecting an appropriate cloud service according to consumer requirements is a daunting task, especially for applications that use a composition of different cloud services. In this paper, we have designed an ontology-based cloud infrastructure service discovery and selection system that defines functional and non-functional concepts, attributes and relations of infrastructure services. We have shown how the system enables one to discover appropriate services optimally as requested by consumers.

An ontology-based cloud infrastructure service discovery and selection system

In recent years, due to global economic downfall, many of the organisations have resorted to downsizing their Information Technology (IT) expenses by adopting innovative computing models like cloud computing, which allows business houses to reduce their fixed IT costs by promising a greener, scalable, cost-effective alternative to utilise the IT resources. A growing number of pay-per-use cloud services are now available on the web in the form of Software as a Service (SaaS), Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). With the increase in the number of services, there has also been an increase in demand and adoption of cloud services making cloud service identification and discovery a challenging task. This is due to varied service, descriptions, non-standardised naming conventions, heterogeneity in type and features of cloud services. Thus, selecting an appropriate cloud service according to consumer requirements is a daunting task, especially for applications that use a composition of different cloud services. In this paper, we have designed an ontology-based cloud infrastructure service discovery and selection system that defines functional and non-functional concepts, attributes and relations of infrastructure services. We have shown how the system enables one to discover appropriate services optimally as requested by consumers.

EC3: Cutting Cooling Energy Consumption Through Weather-Aware Geo-Scheduling Across Multiple Datacenters

Most information technology (IT) equipment found in a data center is air-cooled as electrical component produces heat, which must be removed to prevent the temperature of the IT equipment from rising to an unacceptable level. The energy consumption for the data center cooling system is positively related to the air temperature outside the data center. The difference of data center internal temperature and the outside air temperature varies from each data center location. If we reschedule the workload of Internet cloud services to the least temperature difference, the cooling energy consumption will be the biggest savings. The cooling energy-consumption model and query characteristics of cloud services provide the methodology to formulate the energy consumption and workload rescheduling. However, the cloud service must meet the tail latency constraint after the rescheduling. We solve this problem by estimating the high-percentile tail latency and scheduling the cloud service to where can meet the tail latency constraint. At last, a proactive weather-aware geo-scheduling algorithm, called EC3, is proposed to distribute end-users’ loads among data centers so as to reduce the cooling energy consumption. The trace-driven experiments on real clouds and data center workload traces show the effectiveness of our design for reducing data center cooling consumption.

Virtualisation security risk assessment for enterprise cloud services based on stochastic game nets model

Resource virtualisation is a prominent characteristic of cloud services, and it determines the resource utilisation efficiency and service quality. However, the virtualisation security issues also have a significant impact on the safety of cloud services. The security of virtualisation in cloud services is so complicated that current security risk assessment methods generally have some limitations when applying in cloud services. In this work, a security risk assessment model has been proposed for cloud services as a solution of this problem using stochastic game nets. Based on graphical tools, the virtualisation security risk scenario of cloud services can be described clearly, and virtualisation security risk factors can be evaluated accurately. The analysis results proved this method had powerful ability to simulate complicated and dynamic security issues in cloud services. Furthermore, our achievements can be used to help the cloud provider or tenant of the cloud service system taking corresponding measures to mitigate the risk.

Cloud Computing Services for Improving Efficiency In Omani Small and Medium Enterprises Sector

Small and medium sized enterprises (SMEs) assure economic growth in Oman. Generally, many SMEs are struggling to survive in an ongoing global recession and are often reluctant to use research results and new technologies for business and learning. Cloud Computing offers many opportunities and could help companies to improve their business and use technology more efficiently. Small and medium sized enterprises (SMEs) assure economic growth in Oman, but the last financial crisis and the economic recession have hit SMEs hard in Omani SMEe and some of them have difficulties to survive and less resource to invest in new technologies. Cloud Computing offers many opportunities and can help companies to improve their business and use technology more efficiently. Some features i.e. on-demand services, broad network access, resource pooling, rapid elasticity and measured service distinguish Cloud from other computer networking models. According to the analyst firm IDC in 2012 more than 1.7 million cloud computing jobs have remained unoccupied and the trend should lead to more than seven million cloud-related vacancies worldwide. In this research, cloud computing advantages and disadvantages of cloud technology are reflected in the context of Omani SME sector. The project IN-Cloud is analyzed considering Omani SME project to understand the effectiveness of efforts in this direction. The general objective of the project IN-CLOUD are to study the framework of use of cloud computing in SME sector. Survey results of Cloud Computing and advantages for SMEs are presented in paper within the context of Omani SME sector. The results show that 70% of SMEs have adopted or used some of features of Cloud services but they indicated the need n for awareness, safety and training, qualified professional staff in this field.