International Journal of Cloud Computing (5 papers in press)

Regular Issues

• Scalable and Adaptable Hybrid LSTM Model with Multi-Algorithm Optimisation for Load Balancing and Task Scheduling in Dynamic Cloud Computing Environments  
  by Mubarak Idris, Mustapha Aminu Bagiwa, Muhammad Abdulkarim, Nurudeen Jibrin, Mardiyya Lawal Bagiwa 
  Abstract: Cloud computing delivers scalable, flexible resources, but dynamic workloads challenge efficient resource management, especially in load balancing and task scheduling. Addressing these challenges is vital for optimal performance, cost efficiency, and meeting growing application demands. This study proposes the MultiOpt_LSTM model, a hybrid approach that integrates long short-term memory (LSTM) networks with multi-algorithm optimisation techniques, including binary particle swarm optimisation (BPSO), genetic algorithm (GA), and simulated annealing (SA). The goal is to optimise resource allocation, reduce response times, and ensure balanced workload distribution across virtual machines. The proposed model is evaluated using both real-world and simulated cloud environments, comparing its performance with state-of-the-art techniques such as ANN-BPSO and heuristic-FSA. Key performance indicators like response time, resource utilisation, and degree of imbalance are used to measure efficiency. Results show that the MultiOpt_LSTM model outperforms competing methods, achieving near-zero imbalance at higher task volumes and demonstrating superior resource utilisation and reduced response times. For example, at 3,000 tasks, the model maintains a balanced distribution, outperforming traditional methods like IBPSO-LBS by a significant margin. While the simulation results are promising, future work will focus on real-world implementations to assess the models scalability and adaptability in diverse cloud environments.
  Keywords: Cloud computing; load balancing; task scheduling; hybrid LSTM model; optimization algorithms; resource utilization; response time; degree of imbalance.
  DOI: 10.1504/IJCC.2025.10071475
   
  
• Optimized Elliptic Curve Cryptography for Data Security in Cloud Computing utilising the CSLEHO algorithm  
  by Najimoddin Khairoddin Shaikh, Rahat Afreen Khan 
  Abstract: The suitability of pit lake closure option was assessed for ten open-pit quarry mines in Lugoba and Msata, Tanzania. The study integrated hydrological, geochemical and geotechnical assessment. Hydrological assessment addressed current and future water quality for pit lakes. Geochemical characterisation established hazardous elements and potential for acid generation. Geotechnical analysis established prior and current stability of pit walls. Hydrological assessment revealed good water quality, with pit lakes attaining equilibrium in 14 to 126 years, at depths of 18 to 40 metres. Geochemical assessment showed albite, quartz, and calcite as the dominant minerals. Aluminium and iron mobilisation was considered negligible due to absence of acidic conditions. Geotechnical assessment revealed high stability of pit walls before and after formation of pit lakes. The study proved that pit lake closure is a viable and sustainable option for quarry mine sites. This transforms former quarries into water resources for irrigation, livestock and other secondary uses, while mitigating post-mining environmental risks in nearby communities.
  Keywords: Elliptic Curve Cryptography; Cloud Computing; Data Security; Optimal Key Generation; Optimization; CSLEHO.
  DOI: 10.1504/IJCC.2025.10072309
   
  
• 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
   
  
• GuCA-KFDCN: Gull Cruise Attack Optimised Hybrid Kernel Filter Enabled Deep Learning Model For Attack Detection And Mitigation In Cloud Computing Environment  
  by Yogesh B. Sanap, Pushpalata G. Aher 
  Abstract: In a cloud computing environment, resources are provided as services over the internet, eliminating the need for significant upfront capital expenditure. However, Distributed Denial of Service (DDoS) attacks create a considerable threat to this availability, making detection a critical aspect. These attacks can disrupt access, undermining the trust and reliability of cloud services. The conventional approaches employed for DDoS attack detection pose significant challenges regarding overfitting issues, computational complexity, and limited generalisability. As a result, to mitigate these challenges this research offers a Gull cruise attack optimized HybridKernel Filter enabled Deep Convolutional Neural Network (GuCA-KFDCN) model. The utilisation of hybrid kernel filters integrates three different kernel functions, which effectively capture the complex attack patterns. Furthermore, the Gull Cruise Attack Optimization (GuCAO) algorithm refines the performance of the model by optimizing the parameters of the proposed model, ensuring robust performance. In addition, the GuCAO algorithm effectively chooses optimal key values for oversampling, which improves detection performance. The experimental outcomes show the efficacy of the proposed model interms of sensitivity of 95.29%, accuracy of96.84%, and specificity of 97.74% for training percentage 80.
  Keywords: Deep Convolutional Neural Network; Cloud computing; Gull cruise attack optimization; Distributed Denial of Service attack; HybridKernel Filter.
  DOI: 10.1504/IJCC.2025.10072473
   
  
• 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