AMSche: Affinity-Aware Microservice Scheduling for Communication-Intensive Tasks

Abstract

As computing resources in cloud environments become increasingly abundant, executing complex scientific workflows on large-scale cloud infrastructure has become a standard practice. However, communication-intensive workflows face two fundamental bottlenecks. First, the lack of physical topology awareness often forces high-frequency interacting microservices to be placed on geographically distant nodes, which generates excessive cross-node communication overhead, leads to network load imbalance, and increases latency. Second, the prohibitive online computation time of conventional iterative scheduling algorithms further degrades response speed, making them unsuitable for real-time scenarios. To address these bottlenecks, this paper proposes AMSche, a framework for microservice deployment and task scheduling that is aware of both position and topology. The framework comprises two core mechanisms. The first mechanism, position-aware service deployment, colocates high-frequency interacting services on the same physical node based on communication affinity, thereby compressing cross-node communication overhead at the physical level. The second mechanism, topology-aware task scheduling, leverages online topology feature similarity mapping to instantly reuse historical scheduling plans, achieving scheduling decisions at the millisecond level. Extensive experiments on real-world scientific workflow datasets demonstrate that AMSche achieves an average improvement of 16.19% to 39.15% over existing baseline methods in comprehensive metrics including response time, total communication volume, and network load balance.