METHODS FOR OPTIMIZING NETWORK TRAFFIC THROUGH EDGE COMPUTING
Abstract
The continuous expansion of digital services, Internet of Things (IoT) infrastructures, mobile applications, and real-time communication systems has resulted in a significant increase in network traffic. Conventional cloud-centered architectures often require large amounts of data to be transmitted to remote data centers, which may create bandwidth bottlenecks and increase communication latency. Edge Computing has emerged as an effective paradigm that processes data closer to its source, thereby reducing unnecessary network transmissions and improving service responsiveness. This study investigates various approaches for reducing network load through Edge Computing technologies. Particular attention is given to local data processing, traffic filtering, edge caching, computation offloading, and collaborative edge-cloud architectures. The analysis demonstrates that Edge Computing can significantly improve network efficiency, minimize bandwidth consumption, and support latency-sensitive applications in IoT and next-generation communication environments.
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