Load-Aware Orchestrator for Edge-Computing-Aided Wireless Augmented Reality
Bibliographic record
Abstract
Mobile augmented reality (MAR) has gained increased attention thanks to its potential to transform applications in different domains. One of the challenges to realizing MAR systems is the processing of video frames efficiently. MAR user devices are often resource-constrained and unsuitable for real-time object detection and recognition from video streams. Edge computing has tremendous potential to enable MAR systems, where processing instances (e.g., serverless functions, containers, or virtual machines) can implement and manage the execution of convolutional neural networks (CNNs) for processing MAR offloaded video frames. One of the challenges is how to balance the video frames across the edge servers and processing instances. In this article, we proposed the LAOS orchestrator for resource management and load balancing of distributed edge servers for MAR systems. The LAOS orchestrator balances incoming video frames among processing instances at the edge servers that process video frames. It also determines when to spawn new instances of the CNN functions aimed at ensuring a predefined latency threshold for the processing of video frames. Besides, we devised a novel queuing-based framework for modeling the resource management problem of distributed edge servers for MAR systems. The obtained numerical results show that the proposed LAOS orchestrator reduces the latency and efficiently manages the edge computing resources when dynamic workload peaks are considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| 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".