Multitiered Worker-Oriented Resource Allocation: Mitigating Worker Attrition at the Extreme Edge
Bibliographic record
Abstract
Democratizing edge computing (EC) by harnessing the underused computational resources of extreme edge devices (EEDs) can revolutionize a broad spectrum of Internet of Things applications. However, studying the impact of EED/worker attrition on the Quality of Service (QoS) at the extreme edge has been overlooked. In this article, we propose the multitiered worker-oriented resource allocation (MWORA) scheme. MWORA is the first scheme that studies the impact of worker attrition on the QoS and mitigates its risk by optimizing resource allocation to ensure that workers receive a satisfactory profit to maintain their participation in the service. Furthermore, MWORA accounts for the fact that EEDs are user-owned devices, and are thus subject to a dynamic user access behavior, which can affect the level of computational resources they are willing to endow. MWORA accounts for such dynamicity by pioneering the notion of enabling multitiered computational capabilities to be solicited from each worker based on the profit gained from the assigned tasks. We formulate the problem as an integer linear program (ILP) to maximize the QoS while abiding by certain worker satisfaction, deadline, and budget constraints. We also propose the MWORA-weighted sum (MWORA-WS) scheme to derive an analytical solution using the Karush–Kuhn–Tucker (KKT) conditions and Lagrangian analysis. Extensive simulations show that MWORA outperforms prominent resource allocation schemes by up to 31%, 52%, and 85% in terms of response delay, service capacity, and worker satisfaction, respectively. Additionally, MWORA-WS yields a small gap of 7% and 8% with MWORA in terms of response delay and worker satisfaction, respectively.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".