Adoption Of Computing Research Articles

Study of Access Control Techniques on the Blockchain-enabled Secure Data Sharing Scheme in Edge Computing

Background: The pervasive adoption of edge computing is reshaping real-time big data analysis, smart city management, intelligent transportation, and various other domains. Its appeal lies in its distributed nature, decentralization, low latency, mobile support, and spatial awareness. However, this ubiquity exposes data to security threats, jeopardizing privacy and integrity. Consequently, access control assumes paramount importance in securing data sharing within edge computing and blockchain technologies. Methods: This research addresses this critical issue by conducting a comprehensive study on access control techniques within the context of edge computing and blockchain for secure data sharing. Our methodology commences with an exhaustive review of relevant articles, aiming to identify and expound upon gaps in existing research. Subsequently, we perform a meticulous analysis of access control methods, mechanisms, and performance metrics, seeking to establish a holistic understanding of the landscape. Results: The culmination of this research effort is a multifaceted contribution. We distill insights from a diverse range of access control schemes, shedding light on their nuances and effectiveness. Our analysis extends to evaluating the performance metrics vital for ensuring robust access control. Through this research, we also pinpoint critical research gaps within traditional access control methods, creating a foundation for innovative approaches to address the evolving challenges within edge computing and blockchain environments. Conclusion: In conclusion, this research venture paves the way for secure data sharing in edge computing and blockchain by offering a thorough examination of access control. The findings from this study are anticipated to guide future developments in access control techniques and facilitate the evolution of secure, privacy-conscious, and efficient data sharing practices in the dynamic landscape of digital technology.

An Improved Design for a Cloud Intrusion Detection System Using Hybrid Features Selection Approach with ML Classifier

The rapid adoption of cloud computing has introduced new security concerns, particularly regarding data privacy and protection from sophisticated cyberattacks. Intrusion Detection Systems (IDSs) based on Machine Learning (ML) offer improved detection of malicious behaviour in network traffic. However, existing IDS solutions struggle with large-dimensional data and imbalanced datasets, resulting in higher false positive rates (FPR) and reduced detection rates (DR). In this paper, we propose an improved Cloud IDS leveraging a hybrid features selection approach using Information Gain (IG), Chi-square (CS), and Particle Swarm Optimization (PSO). To address the challenge of imbalanced datasets, the Synthetic Minority Over-sampling Technique (SMOTE) is employed. The Random Forest (RF) classifier is used to detect and classify attacks. The system is evaluated on the UNSW-NB15 and Kyoto datasets, achieving accuracies of over 98% and 99%, respectively, outperforming other methods in terms of detection accuracy.

Mitigating power grid impact from proactive data center workload shifts: A coordinated scheduling strategy integrating synergistic traffic - data - power networks

The widespread adoption of cloud computing has markedly escalated the demand for Internet services, rendering Internet Data Centers (IDCs) a critical component in the consideration for demand response (DR) initiatives and grid energy management. This work proposes a flexible scheduling strategy integrating data, traffic, and power networks (DN-TN-PN) to mitigate the impact of proactive IDC workload transfers on power system balance. The spatial-temporal transfer characteristics of EV charging and discharging loads are utilized to track the proactive spatial-temporal transfer of IDC workloads. Considering the influence of weather factors and traffic congestion on EV travel time, an incentive pricing model for EV users is introduced to alleviate bus load fluctuation and line congestion in the distribution network. Numerical simulations demonstrate that the proposed EV pricing strategy, which accounts for the coupled DN-TN-PN, reduces IDC-induced load fluctuations by over 20 % and decreases transmission line occupancy by approximately 15 %, opening up more opportunities for the implementation of DR programs.

API management and cloud integration model for SMEs

In the rapidly evolving technological landscape, Small and Medium-sized Enterprises (SMEs) face significant challenges in maintaining efficient and scalable IT systems. This review outlines a strategic model for API management and cloud integration designed to enhance system performance and optimize cloud-native features. The proposed model focuses on the redesign and optimization of Application Programming Interfaces (APIs) to leverage cloud capabilities effectively, addressing common issues faced by SMEs in system integration and performance. The model begins with a comprehensive assessment of existing API architectures to identify inefficiencies and performance bottlenecks. It emphasizes the need for a cloud-native approach, which includes the adoption of microservices architecture and serverless computing. By redesigning APIs to align with cloud-native principles, the model aims to achieve greater scalability, flexibility, and responsiveness in handling varying workloads. Key components of the model include API versioning strategies, improved security protocols, and enhanced data management practices. The integration of cloud-native features such as containerization, orchestration, and automated scaling ensures that APIs are optimized for performance and reliability. This approach not only streamlines system operations but also reduces the overhead associated with traditional server-based architectures. Furthermore, the model incorporates best practices for continuous integration and continuous deployment (CI/CD), facilitating seamless updates and maintenance of APIs. It also includes monitoring and analytics tools to provide real-time insights into API performance and user interactions, enabling proactive management and optimization. By implementing this strategic model, SMEs can achieve significant improvements in system performance, reduce latency, and enhance overall cloud integration. The model supports the efficient utilization of cloud resources, leading to cost savings and improved operational efficiency. This approach positions SMEs to better compete in the digital economy by leveraging advanced technologies and optimizing their IT infrastructure.

The Impact of Organizational Factors and Environmental Factors on the Adoption of Computer Assisted Audit Tools and Techniques (CAATTs) by SME in Sri Lanka

The study investigates the low adoption of Computer-Assisted Audit Tools and Techniques (CAATTs) in developing countries, focusing on environmental and organizational factors that influence technology adoption in firms, particularly small audit firms in Sri Lanka. Key variables identified include Clients’ AIS complexity firm size as organizational factors and, perceived support from professional accounting bodies firm size, and top management commitment as environmental factors to CAATT adoption. The objective is to assess the impact of organizational and environmental factors on CAAT adoption by SME in Sri Lanka. The population comprises 71,126 small audit firms listed as Small and Medium-Sized Enterprises (SMEs) as of December 31, 2021, according to the Accounting Auditing Standards Monitoring Board in Sri Lanka. A sample size of 40 small audit firms was selected from this population. Data collection involved distributing questionnaires among auditors, with analysis conducted using the SPSS version 23. Correlation analysis reveals a positive correlation between all variables and CAATT adoption, while regression analysis demonstrates the impact of AIS complexity, perceived support from professional accounting bodies has a high impact on CAAT adoption by SME. The findings suggest that organizational factors such as AIS complexity and Perceived Support from professionals play a significant role in CAATT adoption among small audit firms. In conclusion, the study highlights the importance of considering organizational factors, such as in promoting CAATT adoption. These findings provide valuable insights for enhancing audit productivity and reducing costs in developing countries like Sri Lanka.

Integrating Quantum Computing into Business Analytics: Opportunities and Challenges

This article explores the transformative potential of quantum computing in the field of business analytics. It begins with an introduction to quantum computing, explaining its fundamental principles and recent advancements. The study highlights the limitations of current business analytics methods and demonstrates how quantum computing could address these limitations by offering enhanced data processing capabilities, advanced algorithms, and solutions to complex optimization problems. A comprehensive literature review is conducted to provide context and identify gaps in the existing research. The article then outlines a research design that incorporates both real-world and simulated data, using online datasets and quantum computing frameworks for analysis. The findings reveal significant opportunities for quantum computing to revolutionize business analytics, including improved efficiency, accuracy, and the ability tosolve previously intractable problems. However, the article also addresses key challenges such as technical limitations, cost, accessibility, and integration issues. The discussion highlights emerging trends and provides strategic recommendations for businesses considering the adoption of quantum computing. The article concludes with a summary of the implications of integrating quantum computing into business analytics and reflects onfuture potential and challenges.

Challenges and Solutions for Sustainable ICT: The Role of File Storage

Digitalization has been increasingly recognized for its role in addressing numerous societal and environmental challenges. However, the rapid surge in data production and the widespread adoption of cloud computing has resulted in an explosion of redundant, obsolete, and trivial (ROT) data within organizations’ data estates. This issue adversely affects energy consumption and carbon footprint, leading to inefficiencies and a higher environmental impact. Thus, this opinion paper aims to discuss the challenges and potential solutions related to the environmental impact of file storage on the cloud, aiming to address the research gap in “digital sustainability” and the Green IT literature. The key findings reveal that technological issues dominate cloud computing and sustainability research. Key challenges in achieving sustainable practices include the widespread lack of awareness about the environmental impacts of digital activities, the complexity of implementing accurate carbon accounting systems compliant with existing regulatory frameworks, and the role of public–private partnerships in developing novel solutions in emerging areas such as 6G technology.

Reliability-assured service function chain migration strategy in edge networks using deep reinforcement learning

With the widespread adoption of edge computing and the rollout of 5G technology, the edge network is experiencing rapid growth. Edge computing enables the execution of certain computational tasks on edge devices, fostering more efficient resource utilization. However, the reliability of the edge network is constrained by its network connections. Network instability can significantly compromise service quality. An effective service function chain (SFC) migration algorithm is essential to optimize resource utilization, enhance service quality. This paper begins by analyzing the current research landscape of edge networks and SFC migration algorithms. Subsequently, the challenges associated with edge network and SFC migration are formally articulated, leading to the proposal of a SFC migration algorithm based on deep reinforcement learning (DRL) with a focus on reliability assurance (RA-SFCM). The algorithm leverages multi-agent deep reinforcement learning to dynamically perceive changes in the edge network environment. It introduces an advantage function to evaluate the performance of each agent relative to the average level and incorporates a central attention mechanism with multiple attention heads to better capture the interdependencies and relationships among different agents. Additionally, this paper innovatively defines and quantifies the reliability of the migration process. By introducing a reliability penalty mechanism based on the migration target nodes and link capacity, it enhances the reliability of the migration schemes. The experimental results conclusively demonstrate the remarkable advantages of the RA-SFCM algorithm in terms of real-time performance, resource utilization efficiency, and reliability. Compared to algorithms such as Sa-VNFM, ROVM, and DLTSAC, RA-SFCM exhibits superior performance. For RA-SFCM, the optimized deployment migration strategy enhances real-time performance, precise resource management improves utilization efficiency, and advanced fault tolerance mechanisms strengthen reliability.

Like teacher, like pupil: Transferring backdoors via feature-based knowledge distillation

With the widespread adoption of edge computing, compressing deep neural networks (DNNs) via knowledge distillation (KD) has emerged as a popular technique for resource-limited scenarios. Among various KD methods, feature-based KD, which leverages the feature representations from intermediate layers of the teacher model to supervise the training of the student model, has shown superior performance and enjoyed wide application. However, users often overlook potential backdoor threats when using knowledge distillation (KD) to extract knowledge. To address the issue, this paper mainly contributes to three aspects: (1) we try the first step of exploring the security risks in feature-based KD, where implanted backdoors in teacher models can survive and transfer to student models. (2) We propose a backdoor attack method targeting feature distillation, achieved by encoding backdoor knowledge into specific neuron activation layers. Specifically, we optimize triggers to induce consistent feature map values in the teacher model and transfer the backdoor knowledge to student models through these features. We also design an adaptive defense method against this attack. (3) Extensive experiments on four common datasets and six sets of different teacher and student models validate that our attack outperforms the state-of-the-art (SOTA) baselines, with an average attack success rate of (∼×1.5). Additionally, we discuss effective defense methods against such backdoor attacks.

Adoption of cloud computing by external auditors in raq.Exploratory Study

Adoption of cloud computing by external auditors in raq.Exploratory Study

Cloud computing in the Polish public administration: current state and development prospects

The EU's goal of digital transformation of the economy and society requires increased participation of businesses and citizens. This cannot be achieved without a high level of digitalization in public administration. The paper aims to examine the scale and determinants of cloud computing (CC) adoption in Polish public administration units from 2018 to 2021. A comparison was conducted between the level of adoption of cloud services in the public administration sector and in the enterprise sector in Poland. Next, a synthetic taxonomic measure of technology development was determined so as to assess the differentiation of cloud computing usage in the country's voivodeships. Finally, a two-way fixed effects panel data model was estimated to identify the determinants of the level of CC implementation in public administration. The results showed a wide variation in the adoption of cloud solutions between voivodeships (Polish provinces). The study found a significant correlation between the CC use in public administration and the level of economic development of individual voivodeships, as well as IT training for employees, but no correlation with the level of CC use in enterprises.

Machine Learning Based Intelligent Management System for Energy Storage Using Computing Application

INTRODUCTION: Cloud computing, a still emerging technology, allows customers to pay for services based on usage. It provides internet-based services, whilst virtualization optimizes a PC’s available resources. OBJECTIVES: The foundation of cloud computing is the data center, comprising networked computers, cables, electricity components, and various other elements that host and store corporate data. In cloud data centres, high performance has always been a critical concern, but this often comes at the cost of increased energy consumption. METHODS: The most problematic factor is reducing power consumption while maintaining service quality and performance to balance system efficiency and energy use. Our proposed approach requires a comprehensive understanding of energy usage patterns within the cloud environment. RESULTS: We examined power consumption trends to demonstrate that with the application of the right optimization principles based on energy consumption models, significant energy savings can be made in cloud data centers. During the prediction phase, tablet optimization, with its 97 % accuracy rate, enables more accurate future cost forecasts. CONCLUSION: Energy consumption is a major concern for cloud data centers. To handle incoming requests with the fewest resources possible, given the increasing demand and widespread adoption of cloud computing, it is essential to maintain effective and efficient data center strategies.

The Role of Cloud Computing Adoption and Firm Performance Using SMEs Technological and Environmental Contexts in Hospitality Industry of Henan, China

The Role of Cloud Computing Adoption and Firm Performance Using SMEs Technological and Environmental Contexts in Hospitality Industry of Henan, China

Cloud Computing Technology Adoption: Challenges for SMEs, A Case of Selected SMEs in Tanzania

The study aims to explore the challenges faced by SMEs during the implementation of cloud computing technologies and propose potential solutions. The primary objectives include identifying critical barriers, understanding their impact on small and medium-sized enterprises, and suggesting viable strategies to promote cloud adoption. The research employs a mixed-methods strategy that blends quantitative and qualitative techniques. Surveys are circulated among SMEs to acquire quantitative data, while interviews with IT professionals, industry experts, and SME owners are used to collect qualitative data. Statistical techniques and thematic analysis are employed for the study of qualitative and quantitative data, respectively. The study identifies several obstacles preventing SMEs from adopting cloud computing, such as concerns about data security and privacy, a lack of technological know-how, interoperability problems, an ambiguous return on investment, and difficulties with regulatory compliance. The importance of these obstacles is supported by quantitative analysis, which also emphasizes their frequency and effect on SMEs’ adoption of cloud computing. The results highlight the need for focused interventions to address the issues raised and encourage SMEs to embrace cloud computing. Specific training programs, incentives to offset start-up expenses, partnerships with service providers, legislative clarity, and knowledge-sharing activities among SMEs are examples of practical ideas. By removing these obstacles, SMEs will be better equipped to harness cloud computing’s revolutionary potential, fostering innovation and increasing their competitiveness in the digital age.

Intrusion detection and secure data storage in the cloud were recommend by a multiscale deep bidirectional gated recurrent neural network

Data communication security is developing very day with the creation of cloud computing. The imperative for robust data communication security has become increasingly evident with the pervasive adoption of cloud computing. However, challenges persist due to the inherent complexities and limited security measures in cloud environments, particularly in transmitting and storing sensitive information. Previous studies have underscored the intricate nature of intrusion detection systems (IDS) in cloud-based settings. In this research, a Multi scale Deep Bidirectional Gated Recurrent Neural Network and Optimal Encryption Scheme espoused Intrusion Detection and Secure Data Storage in the Cloud (MDBGRNN-ID-SCESOA) is proposed. Leveraging the KDD CUP 99 dataset and DS2OS Dataset, initial data preprocessing involves Domain Transform Filtering (DTF) for tokenization, dimension reduction, and semantic analysis. Subsequently, MDBGRNN is employed to discern intrusion from non-intrusion data. Furthermore, a two-way encryption mechanism, integrating Elliptical Curve Cryptography (ECC) with the Sine Cosine Egret Swarm Optimization Algorithm (ECC-SCESOA), enhances data security while minimizing computational overhead. To safeguard encrypted data at rest in the cloud, a steganography process is devised, effectively concealing sensitive content. Performance evaluation metrics, including accuracy, specificity, sensitivity, encryption/decryption time, execution time, memory usage, and Matthews correlation coefficient (MCC), demonstrate the efficacy of MDBGRNN-ID-SCESOA. Comparative analysis with existing techniques reveals notable enhancements in computational efficiency and data security. This comprehensive approach addresses critical security concerns in cloud computing, offering a promising avenue for safeguarding sensitive data in cloud environments.

Cloud Computing Adoption Strategy for Government Sector in Indonesian Cloud Industry

Cloud Computing Adoption Strategy for Government Sector in Indonesian Cloud Industry

Evaluating Benefits and Challenges of Cloud Computing Adoption in It Industry

Evaluating Benefits and Challenges of Cloud Computing Adoption in It Industry

A Reliable and Equitable Attribute-Based Proxy Re-encryption System for Cloud Data Sharing

The widespread acceptance and quick growth of cloud computing have made data sharing easier than ever before. What is preventing widespread adoption of cloud computing, however, is how to guarantee the security of the user's data in the cloud. Safe data sharing in cloud computing can be achieved through the use of proxy re-encryption. With the use of a semi trusted cloud server, a data owner can encrypt shared data in the cloud using their own public key, converting it into an encryption meant only for authorized recipients to control access. To help us grasp this fundamental better, this paper provides a thorough and motivating overview of re-encryption of proxy servers from a variety of angles. For granular access control of shared data, Ciphertext-Policy Attribute based Aes (CP-ABE) is a possible cryptographic primitive. Each user in CP-ABE has a set of attributes, and access structures based on attributes are used to encrypt data. If and only if a user's characteristics meet the requirements of the ciphertext access structure, the user can decrypt a ciphertext. Practical applications typically call for additional requirements in addition to this fundamental one. Our research centers on the significant problem of attribute revocation, which poses a challenge for CP-ABE methods

Cost Optimization in Cloud Computing: Capacity Reservation for Intermittent Random Demand Surges

The adoption of cloud computing has been accelerating over the last decade, while enterprise cloud users (“firms”) are struggling to manage their growing cloud expenditures in the face of intermittent digital demand surges caused by planned or random events. To deal with such challenges, a firm can employ reserved instances with the standard contract length (e.g., one year) to meet the stationary base demand, which we refer to as the base contracts, complemented by additional reserved instances with either standard or shorter contract lengths, which we refer to as the supplementary contracts, to cope with the demand surges. We first analyze a model whereby the surge and inter-surge durations are deterministic, demand magnitude is random, and cancellation of the reservations is allowed. We develop a capacity management plan for the firm including not only the optimal capacity levels, which follow a newsvendor-type solution, but also the optimal policy for managing the purchase, renewal, cancellation, or expiration of the supplementary contracts, which can be characterized as a two-threshold policy. Due to the complexity of the structure of the optimal policy, we also construct an effective heuristic policy by excluding the renewal option from the action space, which can be applied to a more general setting where the surge and inter-surge durations are random. We examine two model extensions: (1) when trades of reserved instances are allowed in a secondary marketplace; (2) when the firm does not have exact information about the distributions of the surge magnitude and duration while it can adjust the capacity levels as data unveils. Our analysis shows that the optimal policy for managing the supplementary contracts depends on the relative magnitude of the surge and inter-surge durations in relationship to the cancellation fee. Moreover, our numerical results show that cloud platforms that offer a secondary marketplace are more attractive to firms from a cost standpoint than those that allow cancellation only. The latter, without the secondary marketplace, however, can achieve parity with the former by offering a deeper discount rate for the reserved instances, thereby bypassing the cost of administering the secondary marketplace.

Load Prediction in Double-Channel Residual Self-Attention Temporal Convolutional Network with Weight Adaptive Updating in Cloud Computing.

When resource demand increases and decreases rapidly, container clusters in the cloud environment need to respond to the number of containers in a timely manner to ensure service quality. Resource load prediction is a prominent challenge issue with the widespread adoption of cloud computing. A novel cloud computing load prediction method has been proposed, the Double-channel residual Self-attention Temporal convolutional Network with Weight adaptive updating (DSTNW), in order to make the response of the container cluster more rapid and accurate. A Double-channel Temporal Convolution Network model (DTN) has been developed to capture long-term sequence dependencies and enhance feature extraction capabilities when the model handles long load sequences. Double-channel dilated causal convolution has been adopted to replace the single-channel dilated causal convolution in the DTN. A residual temporal self-attention mechanism (SM) has been proposed to improve the performance of the network and focus on features with significant contributions from the DTN. DTN and SM jointly constitute a dual-channel residual self-attention temporal convolutional network (DSTN). In addition, by evaluating the accuracy aspects of single and stacked DSTNs, an adaptive weight strategy has been proposed to assign corresponding weights for the single and stacked DSTNs, respectively. The experimental results highlight that the developed method has outstanding prediction performance for cloud computing in comparison with some state-of-the-art methods. The proposed method achieved an average improvement of 24.16% and 30.48% on the Container dataset and Google dataset, respectively.