Idle Fragmentation-Aware Resource Scheduling for Hyperscale Energy-Efficient Cloud Data Centers

Hyperscale cloud data centers are rapidly expanding to support the growing computational demands of AI/ML workloads. This growth increases power density, where even small inefficiencies result in significant energy consumption and cooling overhead. A key challenge in such environments is idle CPU–memory fragmentation, where scattered and unusable resource portions prevent efficient workload placement. As a result, underutilized machines remain active, leading to unnecessary energy usage. To address this problem, this study proposes an Idle Fragmentation-Aware Scheduling (FAS) framework. The framework introduces fragmentation-aware resource modeling and an intelligent scheduling mechanism to reduce residual CPU–memory imbalance at both node and cluster levels. Workload characteristics and fragmentation patterns are analyzed to design effective multi-resource scheduling policies that improve packing efficiency.The proposed FAS framework is implemented and evaluated in a Kubernetes environment, where container workloads are scheduled based on real-time fragmentation conditions. An energy-aware consolidation strategy is applied to safely shut down underutilized nodes. Experimental results show that FAS reduces the Resource Fragmentation Index (RFI) by 33.14%, improves average utilization by 24.8%, and decreases energy consumption by 36.8%, while maintaining 100% job acceptance and SLA reliability. These results demonstrate that fragmentation-aware scheduling significantly improves resource utilization and energy efficiency. Overall, the proposed approach provides a practical solution for sustainable cloud management and supports energy-aware orchestration aligned with UN SDG 7 (Affordable and Clean Energy) and SDG 12 (Responsible Consumption and Production).