Profit-aware resource management in edge computing has become an emerging research focus, as service providers and infrastructure operators seek to balance performance efficiency with economic sustainability. Research papers in this area examine strategies to allocate computation, storage, and networking resources while maximizing profit under constraints such as latency, energy consumption, and user quality of service (QoS). Studies highlight auction-based models, game-theoretic approaches, and pricing strategies to achieve fair resource allocation among competing users and tenants. Recent works explore machine learning and reinforcement learning techniques for dynamic profit optimization, enabling adaptive pricing and resource scheduling in highly variable environments. Profit-aware frameworks are also being integrated with workload offloading, edge-cloud collaboration, and network slicing to support diverse applications such as mobile video streaming, industrial IoT, and autonomous systems. Additionally, research emphasizes the importance of energy-aware profit models, where minimizing operational costs contributes to higher provider revenue without compromising service quality. Overall, profit-aware resource management in edge computing is recognized as a critical direction for building economically viable, scalable, and sustainable edge ecosystems.