Deep Learning and Optimization Approaches in Joint Resource Allocation, Security, and Efficient Task Scheduling in Cloud Computing Using Hybrid Pyramidal Convolution Split-Attention Networks: A Review

Abstract

Cloud computing has become a fundamental technology in modern distributed computing environments by providing scalable infrastructure, on-demand resources, and flexible platforms for large-scale data processing and application deployment. Organizations across various industries increasingly depend on cloud platforms for storage, computation, and service delivery due to their efficiency and cost-effectiveness. However, the rapid expansion of cloud services has introduced major challenges related to resource allocation, task scheduling, and system security. Efficient resource management is essential to maintain high performance, minimize latency, and ensure optimal utilization of cloud infrastructure. Dynamic resource allocation remains one of the most critical challenges because cloud providers must distribute computing resources such as CPU, memory, storage, and bandwidth among users with continuously changing workloads. Poor allocation strategies can lead to resource wastage, increased operational costs, and degraded system performance. In addition, task scheduling plays an important role in balancing workloads and minimizing execution delays across cloud nodes. Security is another significant concern, as cloud environments frequently face cyber threats including data breaches, malicious intrusions, and distributed denial-of-service attacks. Integrating intelligent security mechanisms with resource allocation and scheduling frameworks can improve reliability, prevent unauthorized access, detect anomalies, and enhance the overall trustworthiness and efficiency of cloud computing systems.