Diktopos: A Two-Stage Framework for Joint Container-Based Microservice Placement and Distributed Volume Allocation on Cloud-Edge Networks

Abstract

The Cloud-Edge collaborative computing enables the deployment of latency-sensitive and data-intensive applications closer to end users. However, it introduces significant challenges for microservice placement, due to resource heterogeneity, limited edge capacity, and the need to satisfy storage requirements using aggregated resources across multiple nodes. To address these issues, we propose Diktopos, a topology-aware, two-stage scheduling framework that jointly optimizes microservice placement and distributed storage volume allocation in cloud-edge networks. The joint optimization problem is decomposed into two subproblems: (i) microservice placement and (ii) distributed volume allocation, with the objective of minimizing computation, communication, energy, and storage costs. At its core, Diktopos employs a low-complexity, rank-based heuristic that ensures scalable and accurate placement across heterogeneous edge nodes. Simulation results show that our method achieves near-optimal placement decisions (within 1.67% of the optimal solution), and converges up to 5× faster than state-of-the-art approaches in large-scale deployments. Real-world experiments in Kubernetes environments demonstrate up to 53% latency reduction compared to the default scheduler, and up to 23% improvement over other baselines, confirming Diktopos’ effectiveness in dynamic, resource-constrained edge scenarios.