ADASCALE: An Adaptive Scaling and Placement Framework for Microservices Under Dynamics

2026-07-17

AI Summary

In cloud-edge collaborative environments, microservices struggle to simultaneously address computational bottlenecks and end-to-end latency due to dynamic factors such as heterogeneous nodes, time-varying network delays, non-stationary traffic, and mixed request patterns, often leading to violations of service-level objectives (SLOs). To tackle this challenge, this work proposes ADASCALE, a novel framework that uniquely integrates root operation identification, SLO-aware scaling, and a demand-weighted latency-minimizing placement strategy. ADASCALE employs a dual-loop control mechanism—combining reactive and steady-state control—to jointly optimize microservice scaling and cross-node scheduling. Leveraging distributed tracing and service mesh metrics, ADASCALE demonstrates significant improvements over NetMARKS_Scale on the DeathStarBench benchmark, reducing average response time by 1.93× and increasing throughput by 2.16× while consistently meeting SLO requirements.

Abstract

Microservice applications are increasingly deployed across cloud--edge environments, where heterogeneous nodes and time-varying inter-node delays amplify the impact of placement decisions. At the same time, these applications face non-stationary traffic, shifts in the mix of root request operations that exercise different call graphs, and heterogeneous communication modes that determine how network latency and queuing propagate to end-to-end (E2E) performance. Existing autoscalers and network-aware schedulers typically handle only a subset of these dynamics, leading to either compute bottlenecks or inflated cross-node latency and thus SLO violations. We propose ADASCALE, an adaptive framework that jointly scales and places microservice replicas under such multi-dimensional dynamics. ADASCALE implements a Monitor--Analyzer--Planner--Executor (MAPE) loop that extracts per-edge and per-service demand from distributed traces and service-mesh metrics, identifies the most critical root operation under a mixed workload, computes SLO-aware replica targets, and then places replicas to minimize a demand-weighted latency objective given the current inter-node latency matrix. To react quickly to networking perturbations, ADASCALE triggers a reactive placement loop, while a steady-state autoscaling loop handles demand shifts. We evaluate ADASCALE on a cloud--edge Kubernetes cluster using the DeathStarBench Social Network application with three root operations under varying load and workload mixes. Across scenarios, ADASCALE consistently meets SLO targets and improves both latency and throughput: compared with NetMARKS_Scale, it achieves up to 1.56x, 1.93x, and 1.34x lower average response time (for compose-post, read-home-timeline, and read-user-timeline) and up to 2.16x, 1.32x, and 1.36x higher throughput, respectively.

Problem

• microservices
• cloud-edge
• autoscaling
• placement
• SLO violation

Innovation

• adaptive scaling
• microservice placement
• cloud-edge computing
• SLO-aware optimization
• MAPE loop