Enabling Power-Awareness for Kubernetes Scheduling Through Multi-Criteria Optimization

This paper proposes a new scheduling framework to optimize the placement of cloud workloads submitted online by users within a Kubernetes-orchestrated environment. The proposed method aims to incorporate power awareness during the scheduling process, along with other criteria, such, load balancing, and bin packing. The framework equitably distributes workloads across cluster nodes while also selecting the most resource-efficient node to reduce the number of active nodes and prevent resource fragmentation. Existing strategies often focus on a single criterion, leading to suboptimal and unsatisfactory workload placements. The proposed framework utilizes the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a well-known multi-criteria decision analysis algorithm, to account for power consumption and other criteria defined by cloud operators, such as load balancing and bin packing. The algorithm is implemented as a Kubernetes scheduling plugin to rank worker nodes based on these criteria and the submitted workloads. Simulation results demonstrate the effectiveness of the proposed strategy across various scenarios, reducing power consumption by 26.46% and comparable CPU and memory load balancing performance within a large Kubernetes cluster under heavy workloads.