Ubiquitous Learning: Refining Education in the Digital Age
A Hybrid Approach for Resource Utilization Prediction in Cloud Data Center using Functional Link Neural Network and Convolutional Neural Network
An Intelligent QoS-Aware Resource Optimization Framework for Performance-Efficient Cloud Computing Environments
Federated Learning-Based IoT Security Model for Privacy Preserving Analytics
Pathways for Preserving Indigenous Knowledge through Cloud Technology for Sustainable Education
A Comprehensive Review of Security Issues in Cloud Computing
Data Quality Evaluation Framework for Big Data
An Extended Min-Min Scheduling Algorithm in Cloud Computing
Be Mindful of the Move: A Swot Analysis of Cloud Computing Towards the Democratization of Technology
An Architectural Framework for Ant Lion Optimization-based Feature Selection Technique for Cloud Intrusion Detection System using Bayesian Classifier
GridSim Installation and Implementation Process
A Survey on Energy Aware Job Scheduling Algorithms in Cloud Environment
Genetic Algorithm Using MapReduce - A Critical Review
Clustering based Cost Optimized Resource Scheduling Technique in Cloud Computing
Encroachment of Cloud Education for the Present Educational Institutions
A major change is happening in education, with ubiquitous learning, or u-learning, making knowledge accessible through various digital platforms anytime and anywhere. By integrating mobile technologies, cloud computing, artificial intelligence (AI), and the Internet of Things (IoT) into teaching and learning, u-learning goes beyond traditional e- learning. This article explores the theoretical background, practical uses, and challenges of u-learning. It also addresses issues like digital fairness, privacy, and teaching adaptability, while highlighting how u-learning supports personalized, collaborative, and lifelong learning. The paper emphasizes the transformative role of ubiquitous learning in building education systems ready for the future, drawing on previous academic research.
In recent years, the services offered by cloud computing have gained prominence due to their scalability and cost- effectiveness. The increasing number of cloud data centers, driven by the demands of numerous users, has raised both economic and environmental concerns. However, cloud computing faces several challenges related to forecasting resource requirements for larger workloads. However, many recent approaches have introduced effective prediction techniques, yet they face issues related to the inability to handle multiple variates during prediction. Moreover, the prediction accuracy diminished when the number of tasks increased along with the enhanced time of execution. So, this research aims to put forward an effective approach using the hybrid approach of Functional Link Neural Network (FLNN) and Convolutional Neural Network (CNN). The output from a hidden layer of FLNN is fed into the input layer of CNN, which acts as the point of integrating two types of architectures and helps in developing the proposed hybrid model for prediction. The data is obtained from Google cluster trace, and the preprocessing takes place using one-hot encoder and standard scalar. After this phase, feature selection is performed using Chaotic-PSO, and the prediction of resource utilization is carried out using the proposed hybrid model. The efficiency of the suggested model is evaluated with a Functional Link Neural Network along with a hybrid approach of Genetic Algorithm and Particle Swarm Optimization (FLNNGAPSO), BG-LSTM, and clustering-based stacked LSTM. The CPU usage based on RMSE of the suggested model for univariate is 0.001298, whereas the CPU usage of existing BG-LSTM and Clustering-based Stacked LSTM is 0.16 and 0.00558.
Cloud computing has emerged as a dominant paradigm for delivering scalable and on-demand computing services to a wide range of applications, including enterprise systems, data analytics, and Internet of Things (IoT) platforms. Despite its widespread adoption, ensuring consistent Quality of Service (QoS) while efficiently utilizing cloud resources remains a critical challenge, particularly in multi-tenant and dynamic environments. Fluctuating workloads, heterogeneous resource requirements, and strict Service Level Agreement (SLA) constraints typically lead to performance degradation, inefficient resource usage, and increased operational costs. To address these challenges, this paper presents an intelligent QoS-aware resource optimization framework aimed at enhancing performance efficiency and SLA compliance in cloud computing environments. The proposed framework integrates workload profiling, QoS analysis, adaptive scheduling, and multi-objective optimization to dynamically allocate resources based on real-time system conditions. A comprehensive optimization model is formulated to simultaneously minimize response time, execution cost, and SLA violations while maximizing resource utilization. Unlike conventional static or heuristic- based approaches, the proposed solution continuously adapts to workload variations using a feedback-driven decision mechanism. The effectiveness of the framework is evaluated through simulation-based experiments under diverse workload scenarios. Performance metrics such as response time, throughput, resource utilization, and SLA violation rate are analyzed and compared with traditional scheduling techniques. The results demonstrate that the proposed approach achieves significant improvements in performance efficiency and QoS assurance, making it suitable for modern cloud infrastructures supporting latency-sensitive and resource-intensive applications. The outcomes confirm that intelligent, QoS-driven optimization is essential for sustainable and high-performance cloud service provisioning.
The exponential expansion of the Internet of Things (IoT) ecosystem has accelerated the need for real-time, distributed data analytics while intensifying privacy and security risks arising from centralized data collection. Federated Learning (FL) provides a promising alternative by collaboratively training global models across edge devices without exposing raw data. Nevertheless, conventional FL frameworks face multiple challenges, including susceptibility to gradient inversion, membership inference, and poisoning attacks, as well as significant communication and energy overheads on resource-constrained IoT nodes. To address these limitations, FL-ISM is proposed, a federated learning–based IoT security model that integrates secure aggregation, calibrated differential privacy, and Byzantine-resilient optimization with reputation-aware client selection and communication compression mechanisms. The system and threat model are formally defined, privacy and robustness guarantees are derived, and FL-ISM is evaluated on intrusion and anomaly detection benchmarks under non-IID data conditions. Experimental results demonstrate that FL-ISM not only achieves competitive predictive performance but also reduces uplink traffic and effectively mitigates backdoor and inference attacks, thereby enabling scalable, privacy-preserving, and secure analytics in safety-critical IoT environments.
This paper examines pathways for preserving Indigenous knowledge through cloud technology as a foundation for sustainable education. Indigenous knowledge, rooted in traditions and community experiences, offers practices that foster ecological balance, cultural resilience, and holistic learning. However, this wisdom is increasingly at risk of erosion due to rapid globalization and technological dominance. Cloud technology provides an opportunity to safeguard and revitalize these knowledge systems by offering platforms for storage, access, and intergenerational exchange. Unlike static preservation, cloud-based solutions allow communities to share narratives, practices, and ecological insights dynamically, ensuring continuity while adapting to present needs. This study highlights thematic connections between Indigenous knowledge, sustainability, and digital innovation, emphasizing how cloud-based education can bridge tradition and modernity. Challenges of ethics, sovereignty, and cultural sensitivity are also addressed, with recommendations for embedding Indigenous voices in digital design. This thematic exploration suggests future pathways for equitable and sustainable educational practices.
