3C Resource Allocation for Next-Generation Applications in an In-Network Computing-Enabled Edge-Cloud Continuum
Bibliographic record
Abstract
Amidst the emergence of immersive applications, such as, the metaverse, Virtual Reality (VR), Augmented Reality (AR), and Holography, it is clear that substantial enhancements to our existing internet infrastructure are imperative. Fulfilling the stringent Quality of Service (QoS) requirements—which include ultra-low latency, high bandwidth, and optimal frame refresh rates—hinges on the seamless integration of Communication, Caching, and Computing, collectively referred to as the "3C". These elements must be interwoven within the network fabric. Our study presents a novel approach to optimize these 3C resources within a network framework that incorporates Edge and Cloud computing, and In-Network Computing (INC). We propose a resource allocation solution tailored to networks enabled by INC. We aim to efficiently manage the distribution of Service Function Chains and the storage of relevant data for immersive applications. Given the inherent complexity of the tackled problem, we propose two solutions: a Particle Swarm Optimization (PSO)-based meta-heuristic and a simpler, yet effective, greedy heuristic. Our comprehensive simulations, grounded in realistic VR scenarios, validate the effectiveness of the proposed solution, which not only enhances resource efficiency and reduces operational costs but also guarantees high refresh rates and maintains a Motion-To-Photon latency under 22 ms.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".