CLOUD COMPUTING

Cloud computing has become key to modern digital infrastructure, yet traditional reactive auto-scaling systems struggle to balance performance requirements with cost efficiency. This study addresses the limitations of existing cloud resource management approaches by developing the Predictive and Cost-Optimized Auto-Scaling Framework (PCOAF), a conceptual model that integrates machine learning-based workload forecasting with multi- objective optimization. Through systematic application of Design Science Research Methodology, the research analyzed current auto-scaling systems, identified critical deficiencies including reactive latency, prediction inaccuracy, and cost inefficiency, and designed a three-layered architecture comprising monitoring, prediction and decision, and optimization and execution modules. The framework employs archetype-aware prediction to classify workloads into four behavioral patterns; SPIKE, PERIODIC, RAMP, and STATIONARY enabling tailored scaling strategies for each type. Theoretical validation demonstrates that PCOAF achieves 99.8% workload classification accuracy, reduces mean absolute percentage error to 15%, and projects cost reductions of 22% while decreasing service-level objective violations by 61.4% compared to baseline reactive systems. The study establishes PCOAF's feasibility across five design criteria: relevance, consistency, feasibility, scalability, and economic viability. By addressing identified gaps in both international research and Nigeria's emerging cloud ecosystem, this framework contributes a theoretically grounded and practically applicable solution for intelligent, cost-aware cloud resource management in resource-constrained environments.

Cloud computing has revolutionized the way businesses manage their IT infrastructure, offering scalable, cost-effective, and flexible solutions. However, as organizations migrate to the cloud, cybersecurity becomes a critical concern. This paper explores how cloud computing enhances cybersecurity for businesses by leveraging advanced security mechanisms such as encryption, multi-factor authentication, artificial intelligence (AI)-driven threat detection, and automated compliance management. Cloud service providers (CSPs) offer robust security frameworks, including real-time monitoring, distributed denial of service (DDoS) protection, and secure access controls, reducing the risk of cyber threats. Additionally, cloud-based security facilitates disaster recovery, data loss prevention, and regulatory compliance, strengthening overall business resilience. While cloud computing introduces new security challenges, implementing best practices and leveraging CSP security measures can significantly enhance an organization's cybersecurity posture. This study highlights the benefits, challenges, and future trends of cloud computing
in securing business operations against evolving cyber threats. To this purpose, this project designs and implements an encryption and multi-factor authentication system for cloud computing environments using a two-factor authentication approach: first-factor authentication via user ID/email and password, and second-factor authentication via OTP sent to user email. The system is developed using HTML, CSS, JavaScript, and Viejas for the front-end, Laravel and PHP for the backend, and MySQL for the database.

Cloud computing has revolutionized the way businesses manage their IT infrastructure, offering scalable, cost-effective, and flexible solutions. However, as organizations migrate to the cloud, cybersecurity becomes a critical concern. This paper explores how cloud computing enhances cybersecurity for businesses by leveraging advanced security mechanisms such as encryption, multi-factor authentication, artificial intelligence (AI)-driven threat detection, and automated compliance management. Cloud service providers (CSPs) offer robust security frameworks, including real-time monitoring, distributed denial-of service (DDoS) protection, and secure access controls, reducing the risk of cyber threats. Additionally, cloud-based security facilitates disaster recovery, data loss prevention, and regulatory compliance, strengthening overall business resilience. While cloud computing introduces new security challenges, implementing best practices and leveraging CSP security measures can significantly enhance an organization's cybersecurity posture. This study highlights the benefits, challenges, and future trends of cloud computing in
securing business operations against evolving cyber threats. To this purpose, this project designs and implements an encryption and multi-factor authentication system for cloud computing environments using a two-factor authentication approach: first-factor authentication via user ID/email and password, and second-factor authentication via OTP sent to user email.
The system is developed using HTML, CSS, JavaScript, and VueJS for the front-end, Laravel and PHP for the backend, and MySQL for the database.

Cloud computing has revolutionized the way resources are allocated and utilized in the IT
industry. Efficient resource allocation is crucial for optimizing cost, performance, and reliability in cloud environments. This project aims to explore the various resource allocation strategies, challenges, and optimization techniques in the context of cloud computing. We will analyze different cloud service models and deployment models, and evaluate their impact on resource allocation. Additionally, we will develop a resource allocation algorithm and conduct experiments to assess its performance.