Distributed Resource Allocation and Coordinated Scheduling for End-Edge-Cloud Collaborative Computing
Bibliographic record
Abstract
Multi-tier computation offloading is crucial to address capacity constraints and improve flexibility for mobile devices. However, existing research on multi-layer computing offloading faces challenges like inefficient resource utilization and poor scalability, particularly in handling diverse computational tasks. To address these challenges, this paper proposes a distributed resource allocation and mixed task offloading framework for end-edge-cloud collaborative systems that support partial and full task offloading modes. First, we propose a three-tier network computing architecture and formulate a task-offloading utility maximization problem by jointly optimizing mixed task-offloading and resource allocation. The proposed problem is a mixed integer nonlinear program (MINLP), which we solve by decomposing it into two subproblemsresource allocationandtask offloading. Edge computing resources and bandwidth allocation can be independently optimized at each edge node with a fixed task offloading strategy. Cloud computing resource allocation, while convex, involves a global constraint, which we solve in a decentralized manner using a multi-agent optimization approach. Then, we propose a joint task offloading and resource allocation optimization algorithm, CNO-TORA, to obtain the solution to the formulated problem. The algorithm is supported by strong theoretical guarantees and is almost surely convergent to a globally optimal solution. Experimental results on a real dataset demonstrate that our algorithm is scalable to large-scale networks and outperforms baselines, achieving improvements in average system utility ranging from 4.01%-28.15%.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".