Scalability and Versatility of Energy-Aware Workload Allocation Optimizer (WAO) on Kubernetes
Shunsuke Ise, Chizuko Mizumoto, Ying-Feng Hsu, Kazuhiro Matsuda, Morito Matsuoka · 2025
The Workload Allocation Optimizer (WAO), an energy-aware method for workload scheduling and load balancing, along with its implementation on Kubernetes, achieves substantial energy savings in data center operations without requiring hardware modifications or infrastructure changes. This paper evaluates the scalability and versatility of WAO through experimental analysis conducted in an actual data center environment. To facilitate energy-aware workload placement, power consumption models were developed for each CPU frequency governor, enabling precise estimation of allocation impacts. Furthermore, by integrating caching mechanisms, the WAO Scheduler maintains scheduling performance comparable to the default Kubernetes scheduler; combined with data center experiments and regression-based projections, this confirms its practicality even at the Kubernetes scalability limit of 5,000 Nodes. Experimental results further demonstrate WAO's effectiveness across diverse environments, ranging from small server rooms to large-scale data centers with heterogeneous hardware. In addition, by incorporating both power consumption and processing time into the scheduling criteria, WAO consistently delivers substantial energy savings without compromising computational performance. These findings establish WAO as a practical and effective energy-saving solution for Kubernetes-based data center environments, and suggest its effectiveness for compute-intensive tasks, such as AI model training and inference.