SDN-Based NFV deployment for multi-objective resource allocation in edge computing: A deep reinforcement learning for iot workload scheduling

초록

The rapid growth of Internet of Things (IoT) devices presents significant challenges, particularly regarding resource management in real-time data processing environments. Traditional cloud computing struggles with high delay times and limited bandwidth, affecting user interaction and cognitive load. Edge computing mitigates these issues by decentralizing data processing and bringing resources closer to IoT devices, ultimately influencing human-computer interaction. This paper introduces a framework for resource allocation in edge computing environments, leveraging Software-Defined Networking (SDN) and Network Function Virtualization (NFV) alongside Deep Q-Network (DQN) optimization. The framework aims to enhance user experiences by improving CPU, memory, and storage efficiency while reducing network delays, contributing to a smoother and more efficient interaction with IoT systems. Simulated results demonstrate a 40 % improvement in CPU utilization, 30 % in memory, and 20 % in storage efficiency, which can positively impact IoT devices' perceived effectiveness and usability.

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