International Journal of Cloud Computing (9 papers in press)

Regular Issues

• Neural Network Optimization Combining Feature Filtering and Cross Entropy in Software Defined Network Security  
  by Lu Liu 
  Abstract: Software defined networks (SDN) are an emerging network architecture with high flexibility and editable capabilities. However, the centralised control plane of SDN makes it vulnerable to abnormal traffic attacks, while traditional detection methods face challenges such as feature redundancy and data imbalance. To improve the stability and security of SDN, this study proposes a lightweight federated learning-based SDN anomaly detection model that combines a feature filtering module with a cross-entropy loss function optimisation. The results showed that after five iterations, the loss values of all three models reached convergence. The federated learning model without compression had the worst convergence effect, and the convergence of the two models trained 20 and 15 times was basically the same. After completing the model training, the loss values of these three models remained around 1.0. The software defined network abnormal traffic detection model could reduce the loss value to around 1.0 during training, maintain recall and accuracy at around 0.99, and maintain precision at around 0.98. The software defined network abnormal traffic detection model can effectively identify attack behaviours in the network, improve the security protection level, and protect the privacy of users during network use.
  Keywords: Software defined network; Deep learning; Cross entropy; Feature selection; Abnormal traffic.
  DOI: 10.1504/IJCC.2025.10072390
   
  
• Cloud Tourism Scene Image Processing Technology Based on K-means and Image Brightness Enhancement Algorithm  
  by Xiaomei Sun 
  Abstract: To improve segmentation accuracy and visual quality in cloud tourism images, this study proposes an enhanced framework combining a refined K-means algorithm and a DCGAN-based brightness enhancement network. K-means is improved using Canny edge detection for clearer boundaries, maximum contour suppression to avoid misclassification in bright areas, and weighted cluster updates for better texture handling. Simultaneously, a Convolutional Block Attention Module is added to the DCGAN generator to emphasise critical spatial and channel features. Experiments on COCO and Cityscapes datasets yield segmentation accuracies of 98.53% and 98.04%, with PSNR reaching 33.4?dB and SSIM at 0.93, confirming the method's effectiveness.
  Keywords: K-means; DCGAN; Image processing; Cloud tourism; Image segmentation; CBAM.
  DOI: 10.1504/IJCC.2025.10072836
   
  
• An Image Semantic Understanding Model based on Double-Layer LSTM with Information Gain  
  by Chen Li 
  Abstract: In the era of big data, efficient semantic parsing of multi-modal data is crucial for intelligent service systems. However, existing image semantic understanding methods face issues such as cross-modal semantic gaps and insufficient modelling of long-range dependencies. To address these challenges, this paper proposes a novel hybrid network architecture that combines convolutional neural networks, recursive auto-encoders, and a dual-layer long short-term memory (LSTM) network guided by information gain. The proposed model achieves a highest semantic description score of 0.168 and improves both type agnostic accuracy and type aware accuracy to 0.932 and 0.901, respectively outperforming three baseline methods. Compared to the original model, it increases accuracy by 0.016 and 0.010. This architecture effectively bridges cross-modal gaps and enhances feature selection and long-term dependency modelling. The model demonstrates strong potential for deployment in cloud services, semantic web platforms, and virtualised infrastructures to support fault detection, resource optimisation, and intelligent quality management.
  Keywords: Information gain; LSTM; CNN; Image; Semantics; RAE.
  DOI: 10.1504/IJCC.2025.10073180
   
  
• Review on Virtual Machine Placement in Green Data Centres  
  by Muhammed Hemeda, Mahmoud El-Alem, Ahmed Zekri 
  Abstract: Cloud computing delivers scalable, reliable, and high-performance services without the burden of infrastructure management. However, the surge in computational demand has significantly increased the consumption of energy and greenhouse gas emissions in datacentres. Integrating renewable energy sources (RES) and optimising virtual machine placement (VMP) have emerged as critical strategies for achieving sustainable cloud operations. This paper provides a comprehensive review of VMP techniques, highlighting their role in reducing energy use and environmental impact. A novel taxonomy is proposed, classifying green computing strategies across hardware, software, and infrastructure layers. Furthermore, we evaluate the performance of cutting-edge VMP algorithms, especially in RES-powered environments, based on metrics such as energy savings and carbon footprint. The review also addresses deployment challenges and outlines practical solutions to enhance the feasibility of implementation. Finally, we identify key future research directions that can drive the development of intelligent, resilient, and environmentally sustainable cloud infrastructures.
  Keywords: virtual machine placement; VMP; green data centres; GDC; renewable energy sources; RES; cloud computing; CC; Sustainability; energy consumption.
  DOI: 10.1504/IJCC.2026.10073592
   
  
• A Secure and Effective Hybrid Encryption Technique to Enhance the Security of Cloud Storage  
  by Bhagyashree Sunil Patil, Pooja Sapra 
  Abstract: Cloud storage permits the users to store multiple files and makes them accessible on any device from anywhere. This study, an effective and secure hybrid encryption technique is introduced to enhance cloud security. The presented hybrid technique leverages the advantages of both elliptic curve cryptography (ECC) and Rivest Cipher 6 (RC6) to improve cloud storage security in the cloud service industry. The secure key is generated using the ECC method, and the encryption and decryption are performed using RC6. Furthermore, the proposed model is implemented in Python, and its performance is measured with key generation time, encryption time, decryption time, and throughput. The research outcomes illustrate that the proposed models encryption, decryption, key generation times, and throughput are 0.008487 s, 0.008021 s, 0.000030 s, and 2000.58 KB/s for a 16 KB input file size. Therefore, the numerical results demonstrated that the proposed hybrid method offers reliable and efficient cloud storage while requiring less time to maintain system security.
  Keywords: Cloud Network; Cloud computing; Data Security; Hybrid Encryption Algorithm; Elliptic Curve Cryptography (ECC); Rivest Cipher 6 (RC6).
  DOI: 10.1504/IJCC.2025.10073595
   
  
• Improved Artificial Bee Colony Algorithm and its Feature Selection and Evaluation Based on Granularity Rough Entropy and Cloud Model  
  by Shouchun Yue, Lili Zhang 
  Abstract: With the rise of the digital economy, data analysis is crucial. Current machine learning struggles with multi-feature data. An enhanced artificial bee colony algorithm improves feature selection. In the comparison of running time and loss value, the average running time was 1.25s and the loss value was 0.13, which was significantly better than comparison algorithms. This result indicated that the algorithm was effective. In addition, in the comparative analysis of application effects, the algorithm performed better than other comparison algorithms in the selected feature subset size on different datasets. In the bearing fault dataset, it was found that the classification accuracy of this algorithm was 98.8%, significantly better than the comparison methods. The designed method has good performance and practical value, which is conducive to improving the accuracy and quality of data feature selection, and providing a certain theoretical basis for improving data analysis and theory.
  Keywords: Granularity roughness entropy; Cloud model; ABC; Feature selection; Data.
  DOI: 10.1504/IJCC.2025.10073611
   
  
• Distributed QoS Anomaly Detection with Adaptive Sampling: a Middleware-integrated Approach for Cloud SLA Compliance  
  by Peng Xiao 
  Abstract: Cloud computing necessitates robust Quality of Service (QoS) management to ensure adherence to Service Level Agreements (SLAs), yet existing systems often lack efficient QoS violation detection mechanisms. To address this gap, this study proposes a novel QoS violation detection framework integrated into the CP-M&E middleware, focusing on scalability and communication efficiency in large-scale cloud environments. The framework introduces a distributed sliding-window algorithm to balance detection accuracy and overhead, coupled with a likelihood-based adaptive sampling technique that dynamically adjusts intervals to minimize monitoring costs. A quantitative communication-cost model further optimizes parameter tuning for diverse operational scenarios. Experimental deployment in real-world cloud infrastructures demonstrated a 30%40% reduction in communication overhead compared to traditional methods, alongside enhanced scalability for resource pools. Results validate the frameworks superior performance in reducing false positives and maintaining SLA compliance, establishing its viability for modern cloud ecosystems.
  Keywords: cloud computing; quality of service; service level agreement; performance monitoring.
  DOI: 10.1504/IJCC.2026.10073626
   
  
• A Multi-Tool Framework for Enterprise System Deployment Decisions in Public Cloud environments: Benefits, Risks, and Cost Modelling  
  by Neerav Nishant, Vaishali Singh 
  Abstract: Cloud computing has transformed the way enterprises provision and consume IT services, yet many organisations still struggle with adopting public cloud solutions due to uncertainties regarding suitability, risks, and cost implications. This research addresses these challenges by presenting a comprehensive decision support framework designed to aid enterprises during their cloud adoption process. The framework comprises three practical tools: the Cloud Suitability Checklist, the Benefits and Risks Assessment Tool, and the Elastic Cost Modelling Tool. Each tool is developed based on academic literature and industry practices, and evaluated using real-world case studies. Together, these tools provide a systematic approach to assess the viability of deploying IT systems in public clouds. The study contributes a vendor-neutral methodology that bridges the gap between academic research and practical enterprise adoption, enabling informed, strategic decision-making.
  Keywords: Cloud computing; Public cloud adoption; Cloud suitability checklist; Cloud strategy; Cloud migration; Cost forecasting & risk management in cloud computing.
  DOI: 10.1504/IJCC.2026.10073854
   
  
• From Legacy to Intelligence: the Future of Cloud Load Balancing  
  by Neha Miglani, Chander Diwaker 
  Abstract: The inherent cloud dynamicity and resource-intensive nature mandates development of vigorous load balancers to ensure optimal resource utilisation. This paper aims to provide insight into state-of-the-art techniques falling under static, dynamic and nature-inspired load balancing environment paving way to explore machine learning-based solutions. It expounds how predictive capabilities of ML algorithms can yield informed decisions in dynamically allocating resources. Numerous LB models that can attain better solutions and various performance metrics have been analysed. The paper envision future trends in developing ML-based solutions by entailing manifold research gaps. The novelty of survey originates from cohesive analysis of static, dynamic and nature-inspired LB strategies coupled with emerging ML-based approaches, performance-metric-based comparative assessment and discernment of open research challenges. By orchestrating LB indebted by advancements through ML approaches, this paper serves as collectible resource for researchers by yielding nuanced understanding for developing roadmap in developing efficacious cloud load balancers.
  Keywords: Cloud Computing; Load Balancing; Virtual Machine; Task Scheduling; Resource Utilization; Energy Consumption; Makespan; Scalability; Machine Learning; Accuracy.
  DOI: 10.1504/IJCC.2026.10074040