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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".