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 key technology in modern distributed systems, offering scalable resources, flexible storage, and efficient infrastructure for large-scale data processing. Organizations across various industries rely on cloud platforms to deploy applications, manage workloads, and store vast amounts of data. However, the rapid expansion of cloud services has introduced challenges related to resource allocation, task scheduling, and system security. Efficient resource management is essential to ensure high performance, low latency, and optimal utilization of infrastructure. Dynamic resource allocation is particularly critical, as providers must distribute CPU, memory, storage, and bandwidth among users with varying demands. Poor allocation can result in underutilization, increased costs, and degraded performance. Similarly, effective task scheduling is necessary to minimize execution time, reduce delays, and balance workloads across cloud nodes. In addition to performance concerns, security remains a major issue, as cloud systems are vulnerable to threats such as data breaches and cyberattacks. Integrating robust security mechanisms into resource management frameworks helps protect sensitive data, detect anomalies, and ensure reliable and secure cloud operations.