Evolving Operating System Strategies for Intelligent Scheduling and Adaptive Memory Management

Operating system design has moved beyond fixed scheduling rules and uniform memory policies as modern hardware and workloads have grown more diverse and demanding. Contemporary systems increasingly rely on learning driven schedulers that adjust decisions in real time, allowing CPU resources to be balanced more effectively under fluctuating loads while considering performance and energy use. At the same time, heterogeneous processor architectures require schedulers to reason not only about timing but also about task placement across cores with different capabilities. Memory management has followed a similar path, shaped by the rise of remote memory access, disaggregated memory pools, and persistent non-volatile memory technologies. These developments challenge traditional paging and eviction strategies, encouraging adaptive, workload-aware placement across multiple memory tiers. While many of these approaches remain experimental and introduce added system complexity, they reflect a clear shift toward operating systems that coordinate compute and memory resources in a more responsive and integrated manner, aligned with real-world demands across cloud, desktop, and edge environments.