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A Multi-Objective Framework for Power-Aware Scheduling in Kubernetes
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DOI：10.1109/TNSM.2025.3630045.png)
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Abstract
En 中文
Efficient workload scheduling in Kubernetes is crucial for optimizing energy consumption and resource utilization in large-scale and heterogeneous clusters. However, existing Kubernetes schedulers either ignore power-awareness or rely on simplified, static power models, which limit their effectiveness in managing energy efficiency under dynamic workloads. To address these shortcomings, we present a multi-objective scheduling framework for online Kubernetes pod placement that jointly considers power consumption, resource utilization, and load balancing. The framework follows a two-stage design: (i) a node power–profiling component trains a machine–learning model from real power measurements to predict per-node consumption under varying utilizations; and (ii) an online scheduler uses these predictions within a multi-objective optimization formulation. We implement scheduling optimization using two algorithms, TOPSIS and NSGA-II, adapting them to the Kubernetes context, and also propose a distributed variant of the NSGA-II algorithm that parallelizes fitness evaluation with controlled migration between workers. Experimental results show that the proposed framework outperforms baseline schedulers, achieving a 40% reduction in power consumption and improvements of 74% and 68% in CPU and memory utilization, respectively, while sustaining scalability under high workloads. To the best of our knowledge, this is the first work to integrate learned power models and distributed multi-objective optimization into Kubernetes for power-aware pod scheduling.
Keywords:
Scheduling
power-aware scheduling
multi-objective optimization
Kubernetes
NSGA-II
TOPSIS
Journal
IF:
5.4
Papers:
527
Citations:
9.2K
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