Distributionally Robust Multi-Timescale Elastic Compute Scheduling for Tail-Latency Controlled Microservices
TL;DR
DR-MPC-Elastic is developed, a distributionally robust multi-timescale controller for microservice autoscaling that materially reduces SLA-risk slots while preserving a transparent cost-risk trade-off.
Abstract
Elastic compute platforms must provision enough replicas to absorb bursty arrivals while avoiding persistent over-reservation. This paper develops DR-MPC-Elastic, a distributionally robust multi-timescale controller for microservice autoscaling. The method replaces a fixed safety margin with a data-dependent ambiguity radius estimated from the tail of recent forecast residuals. It combines an arrival-service workload model, a Wasserstein distributional uncertainty set, a CVaR tail-risk surrogate, and a mirror-descent projection that produces integer replica decisions with bounded actuation. The empirical case study uses a public Alibaba microservice trace containing 44,903 cleaned records, 390 thirty-second control windows, 381 service identifiers, and 13,060 container identifiers. Compared with a reactive HPA-like policy, DR-MPC-Elastic materially reduces SLA-risk slots while preserving a transparent cost-risk trade-off. The contribution is theoretical, auditable, and implementation-oriented: autoscaling is treated as risk-calibrated online optimization rather than as an opaque reinforcement-learning policy.