Heuristic Distribution of Latency-Sensitive Tasks in Multi-Access Edge Computing Systems
Bibliographic record
Abstract
Multi-Access Edge Computing (MEC) paradigm has been widely studied as a potential solution to cope with the challenges emerging from new generations of mobile networks. By processing applications’ data closer to the users, service providers are able to offload origin servers and their underlying network infrastructure, which consequently reduces users’ experienced latency. In this paper, we consider internet-based applications with strict latency tolerance which are primarily enabled by the MEC architecture. Moreover, nodes at the edge may host application-related tasks as well as assist in their provision. We address the Task Distribution Problem (TDP), where the objective is to maximize the overall Quality of Service (QoS) based on the achieved throughput while ensuring that tasks’ latency requirements are satisfied. The TDP is modeled as an Integer Programming problem, taking into account three components: (i) tasks’ priority assignment, (ii) placement and (iii) routing through the MEC network. We propose to approach the problem through two different heuristics: a greedy replacement algorithm and a streaming algorithm. In our experiments, we evaluate the algorithms’ performance by showing numerical results across different experimental settings. We observe that, for the tested scenarios, our techniques provide a good trade-off between run time and high performance.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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".