Cloud Services Research Articles

A BiGRU Model Based on the DBO Algorithm for Cloud-Edge Communication Networks

With the development of IoT technology, central cloud servers and edge-computing servers together form a cloud–edge communication network to meet the increasing demand for computing tasks. The data transmitted in this network is of high value, so the ability to quickly and accurately predict the traffic load of each link becomes critical to ensuring the security and stable operation of the network. In order to effectively counter the potential threat of flood attacks on network stability, we combine the Bi-directional Gated Recurrent Unit (BiGRU) model with the Dung Beetle Optimizer (DBO) algorithm to design a DBO-BiGRU short-term traffic load prediction model. Experimental validation on a public dataset shows that the proposed model has better prediction accuracy and fit than the mainstream models of RNN, LSTM, and TCN.

Cumulus: Blockchain-Enabled Privacy Preserving Data Audit in Cloud

Data owners upload large files to cloud storage servers, but malicious servers may potentially tamper data. To check integrity of remote data, Proof of Retrievability (PoR) schemes were introduced. Existing PoR protocols assume that data owners and third-party auditors are honest and audit only the potentially malicious cloud server to check integrity of stored data. In this paper we consider a system where any party may attempt to cheat others and consider collusion cases. We design a protocol, Cumulus, that is secure under such adversarial assumptions and use blockchain smart contracts to act as mediator in case of dispute and payment settlement. We use state channels to reduce blockchain interactions in order to build a practical audit solution. The security of the protocol has been proven in Universal Composability (UC) framework. Finally, we illustrate several applications of our basic protocol and evaluate practicality of our approach via a prototype implementation for fairly selling large files over the Ethereum platform. We evaluate the prototype and show that our scheme has comparable performance.

Identifying Performance Issues in Cloud Service Systems Based on Relational-Temporal Features

Cloud systems, typically comprised of various components ( e.g. , microservices), are susceptible to performance issues, which may cause service-level agreement violations and financial losses. Identifying performance issues is thus of paramount importance for cloud vendors. In current practice, crucial metrics, i.e. , key performance indicators (KPIs), are monitored periodically to provide insight into the operational status of components. Identifying performance issues is often formulated as an anomaly detection problem, which is tackled by analyzing each metric independently. However, this approach overlooks the complex dependencies existing among cloud components. Some graph neural network-based methods take both temporal and relational information into account, however, the correlation violations in the metrics that serve as indicators of underlying performance issues are difficult for them to identify. Furthermore, a large volume of components in a cloud system results in a vast array of noisy metrics. This complexity renders it impractical for engineers to fully comprehend the correlations, making it challenging to identify performance issues accurately. To address these limitations, we propose Identifying Performance Issues based on Relational-Temporal Features (ISOLATE ), a learning-based approach that leverages both the relational and temporal features of metrics to identify performance issues. In particular, it adopts a graph neural network with attention to characterizing the relations among metrics and extracts long-term and multi-scale temporal patterns using a GRU and a convolution network, respectively. The learned graph attention weights can be further used to localize the correlation-violated metrics. Moreover, to relieve the impact of noisy data, ISOLATE utilizes a positive unlabeled learning strategy that tags pseudo labels based on a small portion of confirmed negative examples. Extensive evaluation on both public and industrial datasets shows that ISOLATE outperforms all baseline models with 0.945 F1-score and 0.920 Hit rate@3. The ablation study also proves the effectiveness of the relational-temporal features and the PU-learning strategy. Furthermore, we share the success stories of leveraging ISOLATE to identify performance issues in Huawei Cloud, which demonstrates its superiority in practice.

Optimizing the Agricultural Internet of Things (IoT) with Edge Computing and Low-Altitude Platform Stations

Using low-altitude platform stations (LAPSs) in the agricultural Internet of Things (IoT) enables the efficient and precise monitoring of vast and hard-to-reach areas, thereby enhancing crop management. By integrating edge computing servers into LAPSs, data can be processed directly at the edge in real time, significantly reducing latency and dependency on remote cloud servers. Motivated by these advancements, this paper explores the application of LAPSs and edge computing in the agricultural IoT. First, we introduce an LAPS-aided edge computing architecture for the agricultural IoT, in which each task is segmented into several interdependent subtasks for processing. Next, we formulate a total task processing delay minimization problem, taking into account constraints related to task dependency and priority, as well as equipment energy consumption. Then, by treating the task dependencies as directed acyclic graphs, a heuristic task processing algorithm with priority selection is developed to solve the formulated problem. Finally, the numerical results show that the proposed edge computing scheme outperforms state-of-the-art works and the local computing scheme in terms of the total task processing delay.

COST OPTIMIZATION METHOD FOR INFORMATIONAL INFRASTRUCTURE DEPLOYMENT IN STATIC MULTI-CLOUD ENVIRONMENT

Context. In recent years, the topic of deploying informational infrastructure in a multi-cloud environment has gained popularity. This is because a multi-cloud environment provides the ability to leverage the unique services of cloud providers without the need to deploy all infrastructure components inside them. Therefore, all available services across different cloud providers could be used to build up information infrastructure. Also, multi-cloud offers versatility in selecting different pricing policies for services across different cloud providers. However, as the number of available cloud service providers increases, the complexity of building a costoptimized deployment plan for informational infrastructure also increases. Objective. The purpose of this paper is to optimize the operating costs of information infrastructure while leveraging the service prices of multiple cloud service providers. Method. This article presents a novel cost optimization method for informational infrastructure deployment in a static multicloud environment whose goal is to minimize the hourly cost of infrastructure utilization. A genetic algorithm was used to solve this problem. Different penalty functions for the genetic algorithm were considered. Also, a novel parameter optimization method is proposed for selecting the parameters of the penalty function. Results. A series of experiments were conducted to compare the results of different penalty functions. The results demonstrated that the penalty function with the proposed parameter selection method, in comparison to other penalty functions, on average found the best solution that was 8.933% better and took 18.6% less time to find such a solution. These results showed that the proposed parameter selection method allows for efficient exploration of both feasible and infeasible regions. Conclusion. A novel cost optimization method for informational infrastructure deployment in a static multi-cloud environment is proposed. However, despite the effectiveness of the proposed method, it can be further improved. In particular, it is necessary to consider the possibility of involving scalable instances for informational infrastructure deployment.

DSDOS Cloud: A Decentralized Secure Data Outsourcing System With Hybrid Encryption, Blockchain Smart Contract‐Based Access Control, and Hash Authentication Codes for Cloud Security

ABSTRACTWith the increasing trend of outsourcing data to cloud services, ensuring data security and privacy has become crucial. Typically, data are stored on cloud servers in encrypted form to mitigate risks. However, accessing the encrypted data requires an access key distributed by a third party. If this third party is untrustworthy, it poses a significant security threat to the system. To address this challenge, we propose a Decentralized Secure Data Outsourcing System (DSDOS) that uses blockchain technology to ensure data security and privacy. The DSDOS system comprises three modules: data security and privacy, access control and authorization, and data integrity and availability. The data security and privacy module uses a hybrid encryption scheme that combines Advanced Encryption Standard (AES), partially homomorphic encryption (PHE), and Diffie–Hellman (DH) to ensure secure data storage and access. The access control and authorization module uses a blockchain‐based smart contract system to manage access to the encrypted data. The data integrity and availability module uses hash‐based message authentication code (HMAC) to ensure that the data are not tampered with and is always available. We conducted a security and performance analysis of the DSDOS system and found that it outperforms previous schemes in terms of security and performance. The DSDOS system is a secure and privacy‐preserving data outsourcing system that can be used to mitigate the security risks associated with traditional cloud storage systems.

Web of shadows: Investigating malware abuse of internet services

Internet Web and cloud services are routinely abused by malware, but the breadth of this abuse has not been thoroughly investigated. In this work, we quantitatively investigate this abuse by leveraging data from the Cyber Threat Alliance (CTA), where 36 security vendors share threat intelligence. We analyze CTA data collected over 4 years from January 2020 until December 2023 comprising over one billion cyber-security observations from where we extract 7.7M URLs and 1.8M domains related to malware. We complement this dataset with an active measurement where we periodically attempt to download the content pointed out by 33,876 recently reported malicious URLs. We investigate the following questions. How generalized is malware abuse of Internet services? How do domains of abused Internet services differ? For what purpose are Internet services abused? and How long do malicious resources remain active? Among others, we uncover a broad abuse affecting 22K domains of Internet services, that Internet services are largely abused for enabling malware distribution, and that malicious content in Internet services remains active longer than on malicious domains.

Leveraging AI to Revolutionize Subscription Business Models

This article explores the transformative impact of Artificial Intelligence (AI) on subscription-based business models across various industries. It examines how AI is revolutionizing key aspects of subscription services, including personalization, customer retention, pricing strategies, customer support, operational efficiency, and fraud detection. The article highlights specific AI applications such as content recommendations, dynamic user interfaces, churn prediction, advanced customer segmentation, dynamic and usage-based pricing, AI-powered chatbots, and sentiment analysis. Additionally, it discusses how AI enhances operational efficiency through automated billing and inventory management, and improves security via anomaly detection and behavioral biometrics. Case studies of Adobe Creative Cloud and Amazon Web Services (AWS) are presented to illustrate real-world applications of AI in subscription services. The article concludes by emphasizing the paradigm shift AI represents in customer engagement, operational optimization, and revenue generation for subscription businesses, forecasting continued innovation and opportunities in this rapidly evolving sector.

PulseGuard: Health Monitoring and Analysis System

Abstract: The use of wearable devices such as smart watches has led to a major change in the way a person is able to manage their health. In this paper, health monitoring system construction based on a smartwatch is designed which is capable of Health parameters like heartbeat monitoring, counting steps and their physical activity levels at continuous intervals. The system is built on modern web technologies and cloud services for data processing in a real-time manner, thus ensuring that alert messages are delivered immediately in case health parameters go beyond the normal ranges. The goal is to improve personal health management by providing expansion, dependability and availability to the users in areas where there is poor or no health care provision. The paper discusses system architecture, implementation, and the prospect of the system in enhancing preventive medicine and its practice for better health.

A Genetic Optimized Federated Learning Approach for Joint Consideration of End-to-End Delay and Data Privacy in Vehicular Networks

In 5G vehicular networks, two key challenges have become apparent, including end-to-end delay minimization and data privacy. Learning-based approaches have been used to alleviate these, either by predicting delay or protecting privacy. Traditional approaches train machine learning models on local devices or cloud servers, each with their own trade-offs. While pure-federated learning protects privacy, it sacrifices delay prediction performance. In contrast, centralized training improves delay prediction but violates privacy. Existing studies in the literature overlook the effect of training location on delay prediction and data privacy. To address both issues, we propose a novel genetic algorithm optimized federated learning (GAoFL) approach in which end-to-end delay prediction and data privacy are jointly considered to obtain an optimal solution. For this purpose, we analytically define a novel end-to-end delay formula and data privacy metrics. Accordingly, a novel fitness function is formulated to optimize both the location of training model and data privacy. In conclusion, according to the evaluation results, it can be advocated that the outcomes of the study highlight that training location significantly affects privacy and performance. Moreover, it can be claimed that the proposed GAoFL improves data privacy compared to centralized learning while achieving better delay prediction than other federated methods, offering a valuable solution for 5G vehicular computing.

[formula omitted]: A blockchain-based secure access control management for the Internet of Things

The Internet of Things (IoT) paradigm has widespread applications across many fields in which private and sensitive user or environmental data are sensed and shared. Most present-day IoT applications depend on centralized cloud servers for authentication and access control. Validating the identity of a user and determining the legitimacy of his/her access requests require multiple rounds of data communications over the untrusted Internet, exposing sensitive data to potential attacks. Thus, protecting these data from security and privacy attacks and ensuring legitimate access is imperative. To address this challenge, we adopt an emerging technology called blockchain to propose a decentralized security framework called BloAC. It ensures secure access control in IoT networks without the intervention of the back-end cloud. We have used the Hyperledger Fabric, an open-source, permissioned blockchain platform, for implementing a prototype system using customized attribute-based access control (ABAC) policies. We have performed simulated and real test bed-based experiments to illustrate that BloAC outperforms the cloud–server-based access control in latency and scalability, significantly reducing latency by up to 42.45% compared to cloud-based solutions. Finally, we conduct a security analysis to formally verify the ABAC policies used in BloAC and establish its robustness against attacks theoretically and using the AVISPA tool.

Advanced Hybrid Optimization Algorithms for Energy Efficient Cloud Resource Allocation

AbstractCloud computing is highly sought after for its dynamic resource allocation capabilities and pay‐per‐use model. However, previous research has identified several challenges, such as lower coverage, high integration rates, longer computation times, and complex operators, all of which are associated with NP‐hard problems. These issues negatively impact the efficiency of resource allocation and scheduling, leading to slower processes, inefficiencies in multi‐objective optimization, lower throughput, and higher power consumption. To address these challenges, we propose a unique Hybridized Optimization Algorithm that integrates Crow Swarm Optimization (CSO), Cuckoo Search Optimization (CSO), and Cat Hunting Optimization (CHO). Initially, Particle Swarm Optimization (PSO) and the Crow Search Algorithm (CSA) handle the exploitation and exploration phases to balance task loads. Subsequently, Cuckoo Search Optimization (CSO) enhances resource utilization and addresses NP‐hard issues, while Cat Hunting Optimization (CHO) refines the search from global to local optimal spaces to achieve the best values. The results demonstrate that the proposed hybrid technique effectively reduces user request waiting times, lowers energy consumption, and decreases execution times on cloud servers compared to baseline approaches, thereby significantly improving overall system performance.

The impact of ISO security standards on enhancing cybersecurity posture in organizations

The increasing frequency and sophistication of cyber threats have made organizations need to adopt robust cybersecurity frameworks. ISO security standards, particularly the ISO/IEC 27000 series, play a critical role in enhancing organizations' cybersecurity posture worldwide. These standards provide a systematic approach to managing sensitive information, ensuring its confidentiality, integrity, and availability. ISO/IEC 27001, which focuses on establishing an Information Security Management System (ISMS), is widely recognized for its ability to help organizations identify, manage, and mitigate cybersecurity risks. By adopting ISO standards, organizations benefit from improved risk management, enhanced incident response capabilities, and stronger alignment with regulatory compliance requirements, such as GDPR and HIPAA. In addition, ISO security standards promote a security-first culture within organizations, fostering greater employee awareness and encouraging the consistent implementation of best practices across departments and regions. The adoption of standards like ISO/IEC 27001 (Information security, cybersecurity and privacy protection), ISO/IEC 27018 (Code of practice for protection of personally identifiable information (PII) in public clouds acting as PII processors), ISO/IEC 27017 (code of practice for information security controls based on ISO/IEC 27002 for Cloud services), ISO/IEC 27015 (Information security management guidelines for financial services) ISO/IEC 27002 (Information security, cybersecurity and privacy protection - Information security controls), and ISO/IEC 27701 (Security techniques- Extension to ISO/IEC 27001 and ISO/IEC 27002 for privacy information management – requirements and guidelines) has demonstrated significant improvements in data protection, especially in industries handling sensitive personal or financial data. Despite their benefits, implementing ISO standards poses challenges, such as resource constraints, scalability, and the need for continuous updates. As the threat landscape evolves, ISO security standards will remain integral to developing a proactive cybersecurity strategy, integrating with emerging technologies such as artificial intelligence and IoT. The global adoption of these standards reflects their pivotal role in securing the digital infrastructure of modern organizations.

A Markov chain-based multi-criteria framework for dynamic cloud service selection using user feedback

A Markov chain-based multi-criteria framework for dynamic cloud service selection using user feedback

Decentralized CP-ABE Scheme for Enhanced University Data Security Using Blockchain

In studying ample data security in universities, it is essential to protect the privacy and integrity of data. Current schemes utilize blockchain technology for data sharing, where cloud servers are responsible for storing ciphertext and performing partial encryption and decryption calculations. Data encryption technology can effectively prevent hacker attacks and ensure the integrity of data transmission. However, cloud servers that are only partially trusted may tamper with the ciphertext or return incorrect results. To solve these problems, this paper improves the privacy of access policy, attribute revocation and data authenticity of traditional CP-ABE algorithm. A protection scheme for ample public data security in universities is proposed, and a decentralized network structure based on multiple nodes is designed to improve the efficiency of data queries. The simulation results show that the scheme performs well in the scenario where the number of system attributes is moderate and the complexity of the access policy is not high.

Decentralized Machine Learning Framework for the Internet of Things: Enhancing Security, Privacy, and Efficiency in Cloud-Integrated Environments

The Internet of things (IoT) presents unique challenges for the deployment of machine learning (ML) models, particularly due to constraints on computational resources, the necessity for decentralized processing, and concerns regarding security and privacy in interconnected environments such as the Internet of cloud. In this paper, a novel decentralized ML framework is proposed for IoT environments characterized by wireless communication, dynamic data streams, and integration with cloud services. The framework integrates incremental learning algorithms with a robust decentralized model exchange protocol, ensuring that data privacy is preserved, while enabling IoT devices to participate in collaborative learning from distributed data across cloud networks. By incorporating a gossip-based communication protocol, the framework ensures energy-efficient, scalable, and secure model exchange, fostering effective knowledge sharing among devices, while addressing the potential security threats inherent in cloud-based IoT ecosystems. The framework’s performance was evaluated through simulations, demonstrating its ability to handle the complexities of real-time data processing in resource-constrained IoT environments, while also mitigating security and privacy risks within the Internet of cloud.

Post-Quantum Cryptography for AI-Driven Cloud Security Solutions

This article presents an innovative framework combining post-quantum cryptography with AI-driven automation to address the imminent threat quantum computing poses to current cloud security systems. With the global cloud computing market projected to reach $1,554.94 billion by 2030 and 94% of enterprises using cloud services, the need for quantum-resistant security solutions is critical. The proposed framework integrates lattice-based cryptography, hash-based signatures, and multivariate cryptography with AI-driven security automation to provide a scalable, adaptable, and future-proof security solution. Initial tests demonstrate the framework's ability to process 5,000 encryption tasks per second while maintaining 99.9% uptime. The article explores the framework's methodology, evaluation results, practical applications across government, finance, and healthcare sectors, and future research directions. By leveraging advanced cryptographic techniques and AI, this framework aims to revolutionize cloud security in the quantum era, ensuring long-term data protection and system resilience.

The Impact of Digital Finance on the Efficiency of Urban Green Economy

Digital finance is a new financing model for small and medium-sized enterprises and individuals. In recent years, with the rapid development of big data, cloud service technology, and artificial intelligence (AI) technology, it has to some extent promoted the efficient development of urban green economy. From the perspective of applying digital finance to urban enterprises and individual consumers, this study explores the important role of digital finance in enterprise investment and financing services, industrial structure upgrading, technological innovation, and the promotion of personal credit, consumption patterns, job employment, and environmental participation activities. Based on this, the impact of digital finance on the operation and efficiency improvement of urban green economy is obtained, and the problems of inadequate digital transformation and low coordination level in different industries of the city's economy are solved.

Efficient Schemes for Optimizing Load Balancing and Communication Cost in Edge Computing Networks

Edge computing architectures promise increased quality of service with low communication delays by bringing cloud services closer to the end-users, at the distributed edge servers of the network edge. Hosting server capabilities at access nodes, thereby yielding edge service nodes, offers service proximity to users and provides QoS guarantees. However, the placement of edge servers should match the level of demand for computing resources and the location of user load. Thus, it is necessary to devise schemes that select the most appropriate access nodes to host computing services and associate every remaining access node with the most proper service node to ensure optimal service delivery. In this paper, we formulate this problem as an optimization problem with a bi-objective function that aims at both communication cost minimization and load balance optimization. We propose schemes that tackle this problem and compare their performance against previously proposed heuristics that have been also adapted to target both optimization goals. We study how these algorithms behave in lattice and random grid network topologies with uniform and non-uniform workloads. The results validate the efficiency of our proposed schemes in addition to the significantly lower execution times compared to the other heuristics.

Advanced Analysis of Scalability and Mobility in Cloud Servers using Logistic Regression Models.

With the rapid expansion of cloud computing, efficiently managing scalability and mobility has become paramount. This paper presents an in-depth examination of how logistic regression models can predict and optimize these two critical aspects of cloud infrastructure. By analyzing various cloud configurations, workload patterns, and system performance data, the logistic regression framework forecasts resource needs and identifies potential bottlenecks. The study incorporates real-world applications and case studies to demonstrate the practical benefits of logistic regression in enhancing cloud performance. Additionally, the paper compares logistic regression with other predictive models and explores future advancements in cloud optimization through AI-driven automation and real-time machine learning. The findings underscore the importance of predictive analytics in maintaining seamless cloud operations and provide actionable insights for cloud architects aiming to design more resilient and efficient cloud systems.