Ksurf+: Attention Kalman Filter for Prediction Under Highly Variable Cloud Workloads

Abstract

Resource estimation and workload forecasting are critical in cloud data centers. Complexity in the cloud provider environment due to varying numbers of virtual machines introduces high variability in workloads and resource usage, making estimations problematic using state-of-the-art models that fail to deal with nonlinear characteristics. High measurement noise and variance affect the estimation of resource metrics of cloud systems across packet networks influenced by unknown external dynamics. An ideal solution to these problems is the Kalman filter, a variance-minimizing estimator, ideal for highly variable data. This work introduces Ksurf+, a novel Kalman filter estimator using selective principal component analysis and an attention mechanism for enhanced short-horizon prediction. Ksurf+ improves prediction accuracy by 37% over state-of-the-art Kalman filters in prediction tasks, reduces the time series prediction error of the state-of-the-art Bi-directional Grid Long Short-Term Memory neural network by over 40%, improves Kafka workload-based scaling stability by 58%, reduces Kafka queue size and lowers Kubernetes worker pod CPU usage by 11.6% on the $VarBench$ benchmark.