Fast Near-Optimal Result Deduplication and Relocation in Collaborative and Distributed Edge Computing Networks
Bibliographic record
Abstract
The increasing complexity of collaborative and distributed edge computing networks necessitates efficient management of result caching and deduplication to optimize storage and reduce redundant processing. Traditional caching strategies often suffer from storage inefficiencies and computational overhead due to excessive result duplication across edge servers. To address this challenge, we propose a convex optimization framework for result deduplication and relocation (COFRDR) that strategically reallocates cached results while maintaining service quality. Unlike conventional heuristics, our approach transforms the inherently NP-hard result deduplication and relocation problem into a continuous convex optimization framework, allowing for faster convergence and better scalability across large-scale collaborative and distributed edge computing networks (CDECN). We introduce a fairness-aware storage balancing mechanism and a latency-space benefit (LSB) model to ensure optimal storage utilization across edge servers. A rounding algorithm is further integrated to map continuous solutions back to the binary domain, ensuring practical deployability. Extensive simulations confirm the effectiveness of our method, demonstrating significant improvements in storage efficiency, deduplication ratio (DR), and latency reduction compared to existing heuristics. The proposed framework offers a scalable and adaptive solution for future large-scale edge computing infrastructures, meeting the increasing demand for real-time, low-latency processing in IoT-driven 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".