Research on Edge-Cloud Collaborative Resource Scheduling and Security Management Based on Intelligent Optimization and Privacy Protection
Abstract
With the rapid development of edge computing, cloud computing, big data, artificial intelligence and the Internet of Things, traditional centralized cloud computing faces increasing challenges in latency, bandwidth pressure, resource utilization and data privacy protection. Edge-cloud collaboration provides a new computing paradigm by extending computing, storage and network resources from centralized cloud data centers to edge nodes closer to users and data sources. This architecture can improve service response efficiency and reduce data transmission pressure, but it also introduces heterogeneous resources, dynamic task requests, unclear security boundaries and privacy leakage risks. This paper reviews existing studies on edge-cloud collaboration, resource scheduling, intelligent optimization and privacy protection, and then conducts an analytical discussion of the research gaps rather than an experimental evaluation. The review shows that existing studies have achieved valuable progress in task offloading, resource allocation, deep reinforcement learning, access control, encryption and federated learning. However, research on integrated frameworks that combine intelligent resource scheduling with privacy-aware security management remains limited. To address this limitation, this paper proposes a formalized privacy-aware scheduling perspective that incorporates latency, energy consumption, cost, node trustworthiness, data sensitivity, privacy leakage risk, reliability and auditability into a unified decision model. The analysis indicates that future edge-cloud systems should evolve from efficiency-centric scheduling toward secure, trustworthy and sustainable collaborative governance.
