This chapter examines Kubernetes resource management strategies tailored for both cloud-native and edge applications. It begins by defining key parameters for allocation, including CPU and memory requests and limits, pod density estimation, network bandwidth requirements, and disk I/O throughput. A cluster-wide resource allocation strategy is then outlined, using objective functions and constraints to balance efficiency with system reliability. Performance evaluation metrics such as CPU and memory utilization, network throughput, disk I/O, and energy efficiency are presented to assess allocation effectiveness. The chapter further explores scaling strategies, contrasting reactive autoscaling, which adjusts resources based on current load, with proactive autoscaling, which leverages forecasting techniques to anticipate demand. A comparative performance analysis demonstrates the trade-offs between responsiveness and predictive accuracy. By integrating quantitative metrics with autoscaling strategies, this chapter provides a comprehensive framework for optimizing Kubernetes resource management across diverse application environments.