EDGE COMPUTING AND CLOUD SYNERGY: OPTIMIZING LATENCY AND BANDWIDTH FOR IOT APPLICATIONS THROUGH DISTRIBUTED CLOUD-EDGE ARCHITECTURES

Abstract

The rapid expansion of the Internet of Things (IoT) has introduced significant challenges related to latency, bandwidth, and system reliability in cloud-based infrastructures. This study investigates the potential of integrating edge computing with cloud architectures to optimize these critical parameters for IoT applications. Through performance testing, qualitative expert interviews, and case study analysis, we examine the impact of edge-cloud synergy on latency reduction, bandwidth optimization, throughput improvement, and system stability. Our data verifies that edge computing reduces latency between 66% and 73% specifically for real-time applications which include autonomous cars and smart healthcare systems.  A data processing shift to edge devices leads to the utilization of 75% of bandwidth which reduces cloud system demands.  Through edge-field and cloud combinations processors achieve an enhanced throughput of 66% to 80%.  The reliability of systems experiences improvement due to decreased failure rates which surpass conventional cloud-only systems by up to 60%.  Although these advantages exist security problems along with resource challenges continue to emerge due to the necessity for secure data transfers and effective load balancing according to experts.  The research supplies valuable knowledge about future IoT system deployments that scale while being dependable and high-performing and demonstrates the benefits of edge-cloud integration for addressing cloud infrastructure limitations.