Hestia: Hyperthread-Level Scheduling for Cloud Microservices with Interference-Aware Attention

TL;DR

Hestia is a novel hyperthread-level, interference-aware scheduler for cloud microservices that uses self-attention to model contention patterns, significantly reducing latency and improving resource efficiency.

Contribution

Hestia introduces a self-attention-based predictor and interference scoring model for hyperthread-level scheduling, addressing asymmetric heterogeneity and SMT contention in cloud environments.

Findings

Reduces 95th-percentile latency by up to 80%

Lowers CPU consumption by 2.3% under same workload

Outperforms five state-of-the-art schedulers by up to 30.65%

Abstract

Modern cloud servers routinely co-locate multiple latency-sensitive microservice instances to improve resource efficiency. However, the diversity of microservice behaviors, coupled with mutual performance interference under simultaneous multithreading (SMT), makes large-scale placement increasingly complex. Existing interference aware schedulers and isolation techniques rely on coarse core-level profiling or static resource partitioning, leaving asymmetric hyperthread-level heterogeneity and SMT contention dynamics largely unmodeled. We present Hestia, a hyperthread-level, interference-aware scheduling framework powered by self-attention. Through an extensive analysis of production traces encompassing 32,408 instances across 3,132 servers, we identify two dominant contention patterns -- sharing-core (SC) and sharing-socket (SS) -- and reveal strong asymmetry in their impact. Guided by these insights, Hestia incorporates (1) a self-attention-based CPU usage predictor that models SC/SS contention and hardware heterogeneity, and (2) an interference scoring model that estimates pairwise contention risks to guide scheduling decisions. We evaluate Hestia through large-scale simulation and a real production deployment. Hestia reduces the 95th-percentile service latency by up to 80\%, lowers overall CPU consumption by 2.3\% under the same workload, and surpasses five state-of-the-art schedulers by up to 30.65\% across diverse contention scenarios.