Morphis: SLO-Aware Resource Scheduling for Microservices with Time-Varying Call Graphs

TL;DR

Morphis is a resource scheduling framework for microservices that leverages recurring call graph patterns to optimize CPU usage and meet latency SLOs amid dynamic structural changes.

Contribution

It introduces a pattern-aware trace analysis and a global optimization approach for resource provisioning in evolving microservice call graphs.

Findings

Reduces CPU consumption by 35-38% compared to baselines.

Maintains 98.8% SLO compliance in evaluations.

Effectively handles dynamic call graph structures.

Abstract

Modern microservice systems exhibit continuous structural evolution in their runtime call graphs due to workload fluctuations, fault responses, and deployment activities. Despite this complexity, our analysis of over 500,000 production traces from ByteDance reveals a latent regularity: execution paths concentrate around a small set of recurring invocation patterns. However, existing resource management approaches fail to exploit this structure. Industrial autoscalers like Kubernetes HPA ignore inter-service dependencies, while recent academic methods often assume static topologies, rendering them ineffective under dynamic execution contexts. In this work, we propose Morphis, a dependency-aware provisioning framework that unifies pattern-aware trace analysis with global optimization. It introduces structural fingerprinting that decomposes traces into a stable execution backbone and interpretable deviation subgraphs. Then, resource allocation is formulated as a constrained optimization problem over predicted pattern distributions, jointly minimizing aggregate CPU usage while satisfying end-to-end tail-latency SLOs. Our extensive evaluations on the TrainTicket benchmark demonstrate that Morphis reduces CPU consumption by 35-38% compared to state-of-the-art baselines while maintaining 98.8% SLO compliance.