Lightweight Robust Optimizer for Distributed Application Deployment in Multi-Clouds
Bibliographic record
Abstract
This thesis creates a new approach for task assignment in an edge-core multi-cloud architecture to reduce power consumption in service centers using multilevel graph partitioning technique. Multilevel graph partitioning has three phases of coarsening, refinement and uncoarsening. For the refinement phase, a new algorithm based on a modified Kernighan–Lin algorithm is proposed which takes into account multiple constraints, and that mitigates the problem of stopping at a local minimum. Once tasks are assigned to the edge and core, multidimensional bin-packing is used to deploy tasks to individual hosts so that power consumption can be calculated. The approach is validated by comparing it to extended simulated annealing and an extended modified Kernighan–Lin algorithm. The experiments show that our approach is fast and produces better results. It is also less prone to failure in finding a feasible deployment for given constraints.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".