QoS-based Task Replication for Alleviating Uncertainty in Edge Computing
Bibliographic record
Abstract
Edge Computing (EC) has been evolving towards harvesting latent yet underutilized computational resources of the Extreme Edge Devices (EEDs), such as autonomous vehicles, smartphones, and tablets. However, EEDs tend to be user-owned devices. This triggers a high level of uncertainty, the impact of which is mostly overlooked. Such uncertainty can stem from the potential loss of network connectivity, battery depletion, as well as the dynamic user access behavior that can affect the computational capability of EEDs and compromise the convenience of users. This uncertainty can profoundly impact the devices' reliability of executing the offloaded tasks. In this context, we propose the Replica Maximization at the Extreme Edge (RMEE) scheme. RMEE employs task replication to achieve maximum reliability and improve successful task execution while abiding by certain QoS requirements. Towards that end, RMEE aims to maximize the number of offloaded replicas for each task, while ensuring that the task execution delay is kept within a certain threshold. We formulate the task replication optimization problem as a Mixed-Integer Linear Program (MILP) and devise an analytical solution using the Karush-Kuhn-Tucker (KKT) conditions and Lagrangian analysis. Extensive simulations have shown that RMEE outperforms other baseline schemes that involve single and fixed number of replicas, in terms of drop rate, satisfaction ratio, and the number of replicas by up to 100%, 100% and 60%, and 95.1 % and 85.4%, respectively.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".