Cloud Service Users Research Articles

Оценка готовности российских регионов к использованию облачных услуг

The objective of the study is to develop methodological approaches for the comparative assessment of Rus-sian regions’ readiness to adopt cloud services and the intensity of their usage, in order to substantiate strategic directions for the development of the infocommunication sector at the regional level. The approach is based on the construction of integrated indices of economic and infrastructural conditions favorable to cloud service adoption, as well as an index of the intensity of their usage. The selection of statistical indicators included in these indices is justified. A methodology for the comparative evaluation of Russian regions’ readiness to utilize cloud services is proposed. Readiness assessments of Russian regions for the development of cloud services were constructed based on current data from Rosstat for 2022 and 2023. The regions were grouped analo-gously to the BCG matrix and Gartner’s quadrant. Framework recommendations were developed, taking into account the identified specifics of regional conditions for the development of cloud service markets and the index of cloud service usage intensity. These recommendations can be used to substantiate strategic directions for the development of the infocommunication sector in Russian regions.

A Study the review of Duplicate Data in Cloud Computing

In the realm of cloud computing, the management of data redundancy, often referred to as duplicate data, poses a significant challenge due to its implications on storage efficiency, data integrity, and overall system performance. This study provides a comprehensive review of the current strategies employed to detect, manage, and eliminate duplicate data in cloud environments. Duplicate and nearly duplicate web pages cause major issues for web search engines. Duplicate document identification, or the process of finding document pairs that represent the same entity, is a basic component of data cleansing. In order to address the structural heterogeneity issue, data must first undergo a data preparation step that involves parsing, data transformation, and data standardization. The findings aim to aid cloud service providers and users in understanding the importance of effective data deduplication strategies to optimize storage resources and enhance cloud computing performance.

E2SVM: Electricity-Efficient SLA-aware Virtual Machine Consolidation approach in cloud data centers.

Cloud data centers present a challenge to environmental sustainability because of their significant energy consumption. Additionally, performance degradation resulting from energy management solutions, such as virtual machine (VM) consolidation, impacts service level agreements (SLAs) between cloud service providers and users. Thus, to achieve a balance between efficient energy consumption and avoiding SLA violations, we propose a novel VM consolidation algorithm. Conventional algorithms result in unnecessary migrations when improperly selecting VMs. Therefore, our proposed E2SVM algorithm addresses this issue by selecting VMs with high load fluctuations and minimal resource usage from overloaded servers. These selected VMs are then placed on normally loaded servers, considering their stability index. Moreover, our approach prevents server underutilization by either applying all or no VM migrations. Simulation results demonstrate a 12.9% decrease in maximum energy consumption compared with the minimum migration time VM selection policy. In addition, a 47% reduction in SLA violations was observed when using the medium absolute deviation as the overload detection policy. Therefore, this approach holds promise for real-world data centers because it minimizes energy waste and maintains low SLA violations.

BLOOM : CLOUD OPTIMIZATION TOOL

Bloom: Cloud Optimization tool" endeavors to harness the power of the Ant Colony Optimization (ACO) algorithm to address the challenge of organizing unstructured input data in cloud storage systems. The primary objective is to optimize cloud storage utilization and minimize associated billing expenses. Through the utilization of ACO, the solution aims to transform unorganized and unstructured data into a structured format, facilitating efficient storage allocation and enhancing data management practices. By leveraging predictive insights derived from the structured data, the solution empowers both cloud service providers and users to make informed decisions regarding data storage optimization. Ultimately, this approach not only streamlines data organization processes but also aids in maximizing cost-effectiveness by ensuring that cloud resources are allocated optimally based on usage patterns and predicted future demands. The integration of ACO into the solution offers a promising avenue for addressing the complexities inherent in managing vast amounts of unstructured data in cloud environments, thereby paving the way for improved efficiency and cost savings.

Revolutionizing collaborative auditing: A dynamic blockchain-based cloud storage framework for data updates and assurance

Effective data management has arisen as a major concern in today’s era of ubiquitous data generation from a plethora of intelligent gadgets. While data proliferation promises unparalleled benefits, it imposes significant storage and computing constraints, particularly on end-users with limited capabilities. To solve these difficulties, this article investigates the confluence of cloud storage, blockchain technology, public auditing, reputation systems, and dynamic auditing. Because of their low-cost data storage and processing capabilities, cloud computing services have grown in popularity, leading customers to embrace data outsourcing to reduce local administrative overhead. This study digs into a novel paradigm for ensuring the integrity and security of data stored in cloud environments using blockchain technology. Integrating public auditing systems enables visible and verifiable data audits, ensuring consumers of data trustworthiness. A reputation system is also included to build trust among cloud service providers and users, improving the overall trustworthiness of the ecosystem. The suggested system also includes dynamic auditing, which allows for real-time changes and data verification, reacting to the changing nature of cloud-stored information. This study provides a thorough examination of the architectural components, techniques, and protocols used in this novel approach. We illustrate the feasibility and usefulness of our approach in ensuring data integrity, security, and reliability in cloud storage systems through empirical analysis and case studies. The findings show the potential benefits of this integrated strategy to solving the issues posed by the modern digital landscape’s tremendous proliferation of data. Through the synergistic integration of cloud storage, blockchain technology, public auditing, reputation systems, and dynamic auditing, this research provides a holistic solution for managing data in the cloud while ensuring data integrity, security, and trust. This comprehensive strategy lays the way for a more robust and dependable cloud data management ecosystem, increasing user trust in cloud-based services.

Legal Regulation of the Cloud Services Market of Ukraine

Based on the analysis of the economic state of the cloud services market and trends of its development, it was concluded that cloud technologies have a significant impact on the activities of business entities, as well as state administration bodies of all developed countries of the world.
 It was established that the cloud services market of Ukraine is at the formation stage, which is connected with the unsatisfactory economic condition of business entities due to the military aggression launched by the Russian Federation.
 The current state of legal security of the cloud services market of Ukraine is studied; at the same time, it is substantiated that the Law “On Cloud Services” adopted by the Verkhovna Rada of Ukraine in February 2022 only lays the foundations for its legal development and further formation of a legal framework aimed at the comprehensive regulation of the relations arising from the provision and consumption of cloud services under market principles.
 It is emphasized that for proper implementation of the Law provisions, the approval by the Cabinet of Ministers of Ukraine of a number of subordinate legal acts is required, namely: structure and order of the formation and use of electronic catalogs of cloud services and services of data processing centers; requirements to the providers of cloud services; procedure for providing cloud services related to the processing of the state information resources; procedure for the proposals development for the use of cloud services by state authorities, as well as a standard contract that must be concluded between the providers and users of cloud services.
 Criteria for the quality and safety of cloud services that must be guaranteed by their provider, in case of crisis situations and cyber attacks, are proposed. 
 The expediency of supplementing the Law of Ukraine “On Cloud Services” with the provisions that empower the regulator of cloud services to inspect the providers of cloud services in the event that they provide the regulator with incomplete of false information, as well as to make requests that are mandatory for consideration by other addressees, is substantiated.
 It is proposed to introduce legal liability of the officials of state bodies in case they do not return to Ukraine state information databases and their backup copies within six months after the termination or cancellation of the legal regime of martial law.
 
 Keywords: cloud services, cloud computing technologies, cloud servers, regulator in the field of communication services

Resource allocation on periotity based schuduling and improve the security using DSSHA-256

Cloud computing has gained popularity with advancements in virtualization technology and the deployment of 5G. However, scheduling workload in a heterogeneous multi-cloud environment is a complicated process. Users of cloud services want to ensure that their data is secure and private, especially sensitive or proprietary information. Several research works have been proposed to solve the challenges associated with cloud computing. The proposed Adaptive Priority based scheduling (PBS) focuses on reducing data access completion time and computation expense for task scheduling in cloud computing. PBS assigns tasks depending on its size and selects the minimum cost path for data access. It contains a task register, scheduler, and task execution components for efficient task execution. The proposed system also executes a double signature mechanism for data privacy and security in data storage. This study correlates the performance of three algorithms, PBS, (Task Requirement Degree) TRD and (recommended a Risk adaptive Access Control) RADAC, in terms of task execution time and makespan time. The experimental results demonstrate that PBS outperforms TRD and RADAC in both metrics, as the number of tasks increases. PBS has a minimum task execution time and a lower makespan time than the other two algorithms

EFFICIENT LOAD BALANCING ALGORITHM: MINIMIZING TIME WITH THRESHOLD- BASED APPROACH

The trending technology “Cloud Computing” provides computation and various resources over the internet through providing diverse level of dynamic services at a cost effective rate. The effectiveness of the infrastructure and the usage of cloud services will determine the direction of cloud computing in the future. There are always issues and difficulties in the implementation process of Cloud Computing. In order to better the cloud environment for users, load balancing seeks to meet these needs. Additionally, load balancing makes sure that when workloads are distributed, a particular node is never overloaded or underloaded. It also ensures that tasks are distributed equally. The goal is to investigate on several static and dynamic load balancing techniques in the field of cloud computing. The Threshold based load balancing approach consumes least amount of processing time at cloud data centers through experimental results.

Functions and Accountabilities in Cloud Computing: Examining the Relationships Between Cloud Service Providers and Users

Functions and Accountabilities in Cloud Computing: Examining the Relationships Between Cloud Service Providers and Users

Energy Efficient Task Scheduling Strategy using Modified Coot Optimization Algorithm for Cloud Computing

Cloud computing (CC) refers to a current computing method that provides the virtualization of computing services as a utility to Cloud service users. Problems based on ineffective task mapping to cloud resource frequently happen in a cloud atmosphere. Task scheduling (TS), thus, means effective scheduling of rational allocation and computational actions of computing resource in certain limitations in the IaaS cloud network. Job scheduling was to allocate tasks to the most appropriate sources to reach more than one goal. Thus, choosing a suitable work scheduling technique for rising CC resource efficiency, whereas maintaining high quality of service (QoS) assurances, becomes a significant problem that remains to attract interest of researchers. Metaheuristic techniques shown remarkable efficacy in supplying near-optimal scheduling solutions for a complicated large-sized issues. Recently, a rising number of independent scholar has examined the QoS rendered by TS approaches. Therefore, this study develops an Energy Efficient Task Scheduling Strategy using Modified Coot Optimization Algorithm (EETSS-MCOA) for CC environment. The EETSS-MCOA method carries out the derivation of features and MCOA is applied to schedule tasks. In addition, the MCOA algorithm is derived by the combination of adaptive β hill climbing concept with the COA for enhanced task scheduling. The conventional COA is stimulated by the swarming characteristics of birds known as coots. The COA followed two distinct stages of bird movements on water surface. The experimental results of the EETSS-MCOA model are validated on CloudSim tool. The solutions attained by the EETSS-MCOA model are found to be better than the existing algorithms.

Spatial analysis and technological influences on smart city development in Kazakhstan

This study delves into the evolving landscape of smart city development in Kazakhstan, a domain gaining increasing relevance in the context of urban modernization and digital transformation. The research is anchored in the quest to understand how specific technological factors influence the formation of smart cities within the region. To this end, the study adopts a Spatial Autoregressive Model (SAR) as its core analytical tool, leveraging data on server density, cloud service usage, and electronic invoicing practices across various Kazakhstani cities. The crux of the research revolves around assessing the impact of these selected technological variables on the smart city development process. The SAR model’s application facilitates a nuanced understanding of the spatial dynamics at play, offering insights into how these factors vary in influence across different urban areas. A key finding of this investigation is the significant positive correlation between the adoption of electronic invoicing and smart city development, a result that stands in contrast to the relatively insignificant impact of server density and cloud service usage. The conclusion drawn from these findings underscores the pivotal role of digital administrative processes, particularly electronic invoicing, in driving the smart city agenda in Kazakhstan. This insight not only contributes to the academic discourse on smart cities but also holds practical implications for policymakers and urban planners. It suggests a strategic shift towards prioritizing digital administrative innovations over mere infrastructural or technological upgrades. The study’s outcomes are poised to guide future smart city initiatives in Kazakhstan and offer a reference point for similar emerging economies embarking on their smart city journeys.

Analyzing transparency and malicious insiders prevention for cloud computing environment

The emerging cloud technology and its integration with cloud services from an enterprise standpoint have provided a gateway for Cloud Service Providers (CSPs) to garner consumers and their data. Due to the growing increase of Cloud Service Users (CSUs), it is pertinent to provide an adequate amount of cloud security to prevent detrimental impacts to businesses. Two large challenges CSPs face for cloud security include transparency and malicious insiders. The primary objective of this work is to provide a framework that analyzes CSPs based on evaluation metrics such as data breaches, data loss, account hijacking, insecure APIs, DoS, malicious insiders, abuse of cloud services, insufficient due diligence, and shared technology concerns. The security index of a CSP is then computed based on the listed evaluation metrics and given a score that translates to a linguistic rating of the CSP. Factors within transparency and malicious insiders are further broken down within the framework to emphasize various components. Transparency factors include published audit certifications, consumer base, membership of cloud organizations, and published incidents. Malicious insiders are sub-sectioned into personnel-related prevention, policy-related prevention, third-party CSP prevention, technical-related prevention, and hypervisor-related prevention. With this scoring contribution, analysis is able to be done on a CSP's potential threats within the network, which can then be visualized into recommended security controls or preventative measures. Finally, we conducted case studies to demonstrate the application of the scoring analysis using our proposed framework and to evaluate the security levels of two extensively employed CSPs.

ЦИФРОВІ ТЕХНОЛОГІЇ УПРАВЛІНСЬКОГО ОБЛІКУ В ДЕРЖАВНОМУ ТА ПРИВАТНОМУ СЕКТОРІ НАЦІОНАЛЬНОЇ ЕКОНОМІКИ

Under martial law, public and private sector entities have found themselves in a rather difficult financial situation, which, accordingly, negatively affects efficiency of their functioning and requires a comprehensive reorientation of the strategy and tactics of managing their activity. Accounting system is the information content for building an effective management system, an important subsystem of which is management accounting in the digital environment of Ukrainian software products. This, in turn, determines relevance of the study. The purpose of the article is to research and substantiate the necessity and peculiarities of Ukrainian management accounting digital technologies and cloud services usage at public economy sector institutions as well as private sector enterprises, in order to increase their economic efficiency and to ensure the accounting data security in war conditions. In the course of the study, the author used general scientific theoretical methods of cognition: system analysis - to clarify the main categories of the study; visual interpretation - to visualize screen forms and dialog modes of functioning of software products and cloud services with the implementation of management accounting functions. The article makes an attempt to substantiate the need to introduce digital management accounting technologies in the public and private sectors of the economy in order to improve their efficient functioning under martial law in the context of post-war recovery of the national economy. The author proves the necessity of introducing digital management accounting technologies and specialized cloud services into the practice of public and private sector entities in order to increase efficiency of their functioning in the context of difficult financial situation and martial law in Ukraine and to ensure the required level of security of accounting reporting data. The article comprehensively substantiates the peculiarities of use and the need for practical implementation of Ukrainian digital management accounting technologies and cloud services in the activities of public and private sector institutions, which made it possible to objectively reveal the range of software products from domestic developers. Prospects for further research have be defined as follows: to provide a theoretical and practical justification for the need to introduce actuarial accounting into the modern Ukrainian digital environment by separating it into a separate module "Actuarial Accounting" with the implementation of the procedure for transforming financial statements into actuarial management reporting in the context of European integration trends in the Ukrainian economy.

Hybrid approach for virtual machine allocation in cloud computing

In this manuscript, a Combined Approach of Generalized Backtracking Regularized Adaptive Matching Pursuit Algorithm and Adaptive β-Hill Climbing Algorithm for Virtual Machine Allocation in Cloud Computing (BA-VMA-CC) is proposed. Generalized Backtracking Regularized Adaptive Matching Pursuit Algorithm (GBRAMP) is used for Virtual Machine (VM) Migration process and Adaptive β-Hill Climbing Algorithm is used to Virtual Machine Placement. These two tasks are essential elements of VM allocation. GBRAMP is used to minimize cost and energy for both cloud service providers and users with help of migration process and to save time and energy. Adaptive β-Hill Climbing Algorithm (AβHCA) is employed for maximizing efficiency, minimizing power consumption and resource wastage. By Combining both GBRAMPA-AβHCA VM is optimally allocated in PM with high efficiency by minimizing cost and energy consumptions. The proposed BA-VMA-CC is implemented in MATLAB platform. The performance of proposed method attains 23.84 %, 28.94 %, 33.94 % lower energy consumption, 28.94 %, 34.95 %, 25.36 % lower CPU utilization is analyzed with existing methods, such as sine cosine with ant lion optimization for VM allocation in Cloud Computing (SCA-ALO-VMA-CC), hybrid distinct multiple object whale optimization and multi-verse optimization for VM allocation in Cloud Computing (DMOWOA-MVO-VMA-CC) and Cuckoo search optimization algorithm and particle swarm optimization algorithm (CSO-PSO-VMA-CC) respectively.

Cloud Services Monitoring: Implementing a Stratified Approach for Enhanced Visibility and Efficiency

- The research paper introduces a novel stratified approach to monitor cloud services, addressing the unique challenges arising from the distributed and dynamic nature of cloud environments. The main objectives of the study are to enhance the effectiveness of cloud service monitoring and management while ensuring the availability, performance, and security of cloud- based applications. The proposed stratified monitoring framework consists of four distinct layers: Infrastructure, Platform, Application, and Security. Each layer focuses on specific monitoring aspects, encompassing infrastructure resources, platform services, application performance, and security events. Methodologically, the paper explores various monitoring tools and techniques suitable for each layer to provide a comprehensive view of the cloud ecosystem. The research includes an empirical case study to demonstrate the practicality and effectiveness of the stratified monitoring approach in real-world cloud deployments. A comparison with traditional monitoring methods is conducted to showcase the advantages of the proposed approach. The obtained results indicate that the stratified monitoring approach significantly improves visibility into the cloud infrastructure, enables early detection of anomalies, and enhances security measures. This approach empowers cloud service providers and users to optimize resource utilization and make informed decisions concerning cloud performance and security. The research paper's significance lies in offering a structured and adaptable framework to monitor cloud services effectively, thus supporting organizations in maintaining reliable and efficient cloud-based applications. The proposed approach fosters a deeper understanding of the cloud ecosystem, mitigates risks, and facilitates prompt issue resolution. Key Words: Cloud computing, Cloud services, Monitoring, Stratified approach, Infrastructure, Platform, Application, Security.

A Review on Cloud Computing Elasticity

Cloud computing is an internet-based computing environment where computing resources (hardware, software) are managed, pooled, and provided on demand by the cloud service providers to cloud service users. Users may rent resources with fixed capacity by considering the application's peak workload, but the application workload may vary. The workload variation may depend on many situations like time of the day, day of the week, etc., So resources may be underutilized when the workload is lower than the peak workload and overutilized when the workload is higher than the peak workload so some strategies are required to manage resources that can add or remove resource capacity as per the variation in workload. For managing cloud resource capacity elasticity plays a vital role. Elasticity is the dynamic property of the cloud and is used for the provisioning and de-provisioning of cloud resources to map between workload and cloud resources. This study reviews the elasticity for “VM only”, “Container only” and “VM and containers together”.

An Evaluation of Machine Learning and Big Data Analytics Performance in Cloud Computing and Computer Vision

Although cloud computing is receiving a lot of attention, security remains a significant barrier to its general adoption. Cloud service users frequently worry about data loss, security risks, and availability issues. Because of the accessibility and openness of the huge volume of data amassed by sensors and the web throughout recent years, computer applications have seen a remarkable change from straightforward data processing to machine learning. Two widely used technologies, Big Data and Cloud computing, are the focus of worry in the IT industry. Enormous data sets are put away, handled, and broke down under the possibility of "Big Data." Then again, cloud computing centres around giving the framework to make such systems conceivable in a period and cash saving way. The objective of the review is to survey the Big Data Analytics and Machine learning ideal models for use in cloud computing and computer vision. The programmed data examination of enormous data sets and the production of models for the wide connections between data are the centre highlights of machine learning (ML). The usefulness of machine learning-based strategies for identifying threats in a cloud computing environment is surveyed and compared in this research.

Cloud Security And Electronic Transaction Security:

The insecurity of cloud leads to loss or exposure of vital information thereby threatens online transactions. These unfortunate happenings resulted in financial loss, reduced patronage of cloud services and investment diversions. Therefore, this chapter assesses cloud security and electronic transaction security. The chapter adopts qualitative research method, data were sourced secondarily through content analysis of edited books, peer-reviewed journals and credible online resources. It was descriptive in nature and thematically analyzed. The paper anchors on routine activity theory and broken windows theory. Findings revealed that cloud activities are under internal security threats of the cloud service providers and external threats of other users of cloud services. The chapter recommends proper background checks for the staff of the cloud service providers, public enlightenment on cloud security protocols. This chapter will be useful to individuals, cloud services providers, NGOs and researchers in the field of online crimes.

Cloud-based Cost Effective IIoT Model Towards Industry 5.0

Companies across industries increasingly depend upon cloud computing to manage their Industrial Internet of Things (IIoT) technology. Machines are connected over a network in the IIoT. Cloud computing plays an essential role by connecting people, devices, work processes, and buildings to deliver cloud services in industries. But cloud computing faces a problem with task scheduling, high latency delay, and memory management, affecting the overall cost of industries using cloud services. A major concern in the cloud computing field is task scheduling which is essential for achieving cost-effective execution and improving resource usage. It refers to assigning available resources to user tasks. This problem can be solved effectively by improving task execution and increasing the use of resources. The waiting time between a client’s sent request and a cloud service provider to give a response, known as latency, is another issue in cloud environments. In cloud computing, this delay can be significantly higher. As a result, users of various cloud services may incur increased expenses due to this delay. Finally, among the most significant topics in cloud computing is efficient memory management, which handles integrated data and optimizes memory management algorithms. This paper proposes a cloud model for IIoT, which provides task scheduling, helps reduce latency, and optimizes memory management. This proposed model helps to reduce the cost of using cloud computing in IIoT.

LM-GA: A Novel IDS with AES and Machine Learning Architecture for Enhanced Cloud Storage Security

Cloud Computing (CC) is a relatively new technology that allows for widespread access and storage on the internet. Despite its low cost and numerous benefits, cloud technology still confronts several obstacles, including data loss, quality concerns, and data security like recurring hacking. The security of data stored in the cloud has become a major worry for both Cloud Service Providers (CSPs) and users. As a result, a powerful Intrusion Detection System (IDS) must be set up to detect and prevent possible cloud threats at an early stage. Intending to develop a novel IDS system, this paper introduces a new optimization concept named Lion Mutated-Genetic Algorithm (LM-GA) with the hybridization of Machine Learning (ML) algorithms such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Initially, the input text data is preprocessed and balanced to avoid redundancy and vague data. The preprocessed data is then subjected to the hybrid Deep Learning (DL) models namely the CNN-LSTM model to get the IDS output. Now, the intruded are discarded and non-intruded data are secured using Advanced Encryption Standard (AES) encryption model. Besides, the optimal key selection is done by the proposed LM-GA model and the cipher text is further secured via the steganography approach. NSL-KDD and UNSW-NB15 are the datasets used to verify the performance of the proposed LM-GA-based IDS in terms of average intrusion detection rate, accuracy, precision, recall, and F-Score.