Application partitioning in mobile cloud computing is a significant research area that focuses on dividing mobile applications into smaller components or modules to optimize execution across mobile devices and cloud/edge resources. Research papers in this domain explore strategies to offload computation-intensive tasks to the cloud or edge while keeping latency-sensitive components on mobile devices, aiming to reduce energy consumption, improve performance, and enhance user experience. Studies highlight static, dynamic, and adaptive partitioning techniques that consider network conditions, device capabilities, workload characteristics, and Quality of Service (QoS) requirements. Recent works also investigate the integration of machine learning and predictive analytics to make intelligent partitioning decisions based on real-time context, workload fluctuations, and user behavior. Security- and privacy-aware application partitioning frameworks are increasingly studied to ensure sensitive data is processed securely without compromising performance. Additionally, multi-tier edge–fog–cloud architectures are leveraged to support scalable, resilient, and latency-sensitive mobile cloud applications. Overall, research in application partitioning for mobile cloud computing enables efficient, adaptive, and secure distribution of application components, balancing performance, energy efficiency, and resource utilization in next-generation mobile computing environments.