Online Convex Optimization for Resource Allocation Scheme in Edge Computing-enabled Networks
Bibliographic record
Abstract
The dynamic edge computing-enabled networks contain various resources, and network parameters and system models are subject to uncertainty. Despite this, there is still a lack of comprehensive online solutions for coordinating wireless, transport, and computing resources. This paper investigates the use of online convex optimization for resource allocation in edge computing-enabled networks with time-varying cost and time-varying constraint functions. Taking into account the uncertainty of wireless status, quality of service requirements, and cost function, the goal is to minimize the long-term cost by optimizing the selection of access points, association of computing nodes, allocation of computing resources, and bandwidth allocation. To address the proposed online resource allocation problem, the modified online saddle-point algorithm is employed and dynamic regret and accumulative constraint violation are defined to measure the performance of the algorithm. To reduce the computational complexity of the projection in the modified online saddle point algorithm, the projection is reformulated as quadratic programs, which can be solved efficiently by convex optimization. Finally, the effectiveness and superiority of the proposed solution are demonstrated through simulation analysis.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".