Classroom resource allocation is a challenging problem in educational institutions. The use of cloud computing can aid in the optimization of this process by allowing resource providers to allocate resources to users in a timely and efficient manner. In this paper, we propose a modified Salp Swarm Algorithm (SSA) to optimize the allocation of classroom resources. The proposed approach involves generating a population of salps representing potential solutions to the resource allocation problem and updating the positions of the salps iteratively based on a fitness function and mathematical equations. We conducted simulations of the proposed algorithm on various numbers of tasks and sources to evaluate its performance. The results indicate that the modified SSA algorithm can efficiently allocate classroom resources, achieving lower completion time of tasks (makespan) with lower costs. This paper contributes to the literature by introducing an effective approach to solve the classroom resource allocation problem.

Optimizing resource management is essential for improving the performance of web platforms in dynamic cloud environments. This paper introduces a predictive cloud resource management framework that integrates Long Short-Term Memory (LSTM) neural networks for workload forecasting with a dynamic resource scaling mechanism. The framework proactively allocates cloud resources, addressing challenges such as latency, cost inefficiency, and under-utilization. Experimental evaluations on a benchmark dataset demonstrated significant performance improvements over traditional methods. The proposed framework achieved a mean squared error (MSE) of 0.012 in workload prediction, a 28% reduction in response time, and a 23% increase in cost savings. These results highlight the effectiveness of predictive resource management in managing dynamic workloads and optimizing distributed web systems. The findings provide a scalable and cost-efficient solution, contributing to advancements in cloud-based resource management. Future work will explore hybrid models to further enhance adaptability and address real-time anomalies. This research lays a foundation for developing innovative and sustainable strategies in distributed computing environments.