Research on Cost and Performance-efficient Edge Analytics focuses on designing frameworks and strategies that optimize both operational costs and computational performance when processing data at edge devices. This area addresses challenges such as resource-constrained edge nodes, dynamic workloads, heterogeneous devices, and the need to meet latency, throughput, and Quality of Service (QoS) requirements without excessive cost. Key research directions include adaptive resource allocation, workload balancing, and task scheduling algorithms to maximize performance while minimizing energy consumption and operational expenses. Other emerging topics involve edge–cloud collaborative analytics, cost-aware deployment of AI and machine learning models, and performance modeling for predictive optimization. Additionally, research on lightweight and scalable analytics frameworks, multi-objective optimization for latency, energy, and cost, and privacy-preserving analytics represents significant avenues for advancing efficient, intelligent, and economically sustainable edge computing systems.