Optimizing Microservice Placement for Heterogeneous Workloads in Collaborative Edge-Cloud Computing

The growing demand for edge services calls for an efficient microservice placement strategy to ensure optimal deployment in dynamic edge-cloud computing environments. In this work, we propose a heterogeneous workload-aware microservice placement framework that optimizes deployment by maximizing edge throughput while minimizing costs. To account for the diverse deployment requirements of microservices under heterogeneous workloads, we explicitly address the placement and resource constraints for both light and heavy workloads—an aspect rarely addressed in existing studies. The formulated problem is inherently non-continuous, non-convex, and involves fractional operations, posing significant computational challenges. To overcome this, we develop a Sparsity-promoting Fractional Programming (SFP) algorithm that relaxes and reformulates the problem into a sparse-promoting linear program using l<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf>-norm approximation. Extensive evaluations demonstrate the effectiveness of our framework in improving edge throughput, reducing deployment costs, and efficiently managing workload heterogeneity.