Ksurf-Drone: Attention Kalman Filter for Contextual Bandit Optimization in Cloud Resource Allocation

TL;DR

This paper introduces Ksurf, a variance-minimizing estimator integrated with Drone for cloud resource orchestration, significantly reducing latency variance and resource costs in highly variable cloud environments.

Contribution

The paper presents Ksurf, a novel estimator for improved resource estimation in cloud orchestration, enhancing Drone's performance under high variability conditions.

Findings

41% latency variance reduction at p95

47% latency variance reduction at p99

7% cost savings in worker pod count

Abstract

Resource orchestration and configuration parameter search are key concerns for container-based infrastructure in cloud data centers. Large configuration search space and cloud uncertainties are often mitigated using contextual bandit techniques for resource orchestration including the state-of-the-art Drone orchestrator. Complexity in the cloud provider environment due to varying numbers of virtual machines introduces variability in workloads and resource metrics, making orchestration decisions less accurate due to increased nonlinearity and noise. Ksurf, a state-of-the-art variance-minimizing estimator method ideal for highly variable cloud data, enables optimal resource estimation under conditions of high cloud variability. This work evaluates the performance of Ksurf on estimation-based resource orchestration tasks involving highly variable workloads when employed as a contextual multi-armed bandit objective function model for cloud scenarios using Drone. Ksurf enables significantly lower latency variance of $41\%$ at p95 and $47\%$ at p99, demonstrates a $4\%$ reduction in CPU usage and 7 MB reduction in master node memory usage on Kubernetes, resulting in a $7\%$ cost savings in average worker pod count on VarBench Kubernetes benchmark.