A two-stage framework for topology-aware joint microservice placement and distributed volume allocation on cloud-edge networks
Bibliographic record
Abstract
The Cloud Edge Continuum enables the deployment of latency-sensitive and data-intensive applications closer to end users, but it poses challenges for microservice placement due to resource heterogeneity and limited edge capacity, especially when storage requirements must be met through aggregated node resources. To address this, we propose a two-stage, topology-aware optimization framework that jointly handles microservice deployment and distributed storage volume allocation in edge networks. Our framework decomposes this joint placement problem into two subproblems, microservice placement followed by a distributed volume allocation subproblem, with the goal of optimizing computation, communication, energy, and storage costs. At its core is a lightweight, rank-based heuristic that ensures scalable, accurate placement across distributed edge nodes. Evaluations on real-world scenarios show our method achieves near-optimal placement (within 1.67% of the exact solution), reduces system costs by up to 30%, and accelerates convergence by 5× compared to state-of-the-art approaches, demonstrating its suitability for dynamic, resource-constrained edge environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".