EXPLORING EDGE COMPUTING AND CLOUD COMPUTING: A COMPARATIVE STUDY OF FEATURES AND APPLICATIONS

Abstract

Edge computing and cloud computing represent complementary paradigms for processing massive data volumes from Internet of Things (IoT) devices, cyber-physical systems, and mobile applications. This research examines theoretical foundations, architectural models, advantages, limitations, and applications of both paradigms. Cloud computing centralizes resources in data centers providing elasticity and scalability, while edge computing decentralizes computation near end-users, reducing latency, conserving bandwidth, and enhancing privacy. Key findings reveal edge computing reduces latency to single-digit milliseconds for autonomous vehicles and industrial automation, achieving sixty to ninety percent bandwidth reduction versus cloud-only architectures. Cloud computing excels in machine learning training, batch processing, and long-term storage. Hybrid fog computing architectures enable optimal workload distribution across three-tier hierarchies. Security analysis shows edge computing enhances privacy through local processing but challenges distributed node security, while cloud computing offers centralized management with transmission vulnerabilities. Applications in autonomous vehicles, smart cities, industrial IoT, and healthcare leverage hybrid architectures. Future directions emphasize intelligent workload partitioning using reinforcement learning for dynamic allocation based on network conditions and privacy requirements. This analysis establishes edge and cloud computing as synergistic technologies addressing diverse computational needs.