Diktopos: A two-stage framework for joint container-based microservice placement and distributed volume allocation on cloud-edge networks

Gouaouri, M., Gouaouri, M. D. E., Ouahouah, S., Bagaa, M., Ouameur, M. A., Massicotte, D. et Ksentini, A. (2026). Diktopos: A two-stage framework for joint container-based microservice placement and distributed volume allocation on cloud-edge networks. IEEE Transactions on Cloud Computing . ISSN 2168-7161 DOI 10.1109/TCC.2026.3676203

Prévisualisation

PDF Disponible sous licence Creative Commons Attribution. Télécharger (1MB) | Prévisualisation

Résumé

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.

Type de document:	Article
Mots-clés libres:	Microservice architectures Resource management Cloud computing Edge computing Servers Optimization Energy consumption Costs Dynamic scheduling Solid modeling
Date de dépôt:	22 avr. 2026 18:13
Dernière modification:	22 avr. 2026 18:13
Version du document déposé:	Post-print (version corrigée et acceptée)
URI:	https://depot-e.uqtr.ca/id/eprint/12816

Actions (administrateurs uniquement)

Éditer la notice