PhysarumSM: P2P Service Discovery and Allocation in Dynamic Edge Networks
Bibliographic record
Abstract
With the continuous growth in the number of mobile networked devices, and their rapidly improving compute capabilities, it has become possible to harness them as an extended cloud. This presents a clear opportunity to place latency-sensitive applications and services at the edge. As applications are increasingly based on the microservices and Network Function Virtualization (NFV) architectures, their overall performance will depend on the location of their constituent microservices relative to one-another. An extended cloud comprising mobile devices therefore results in a dynamic network, making it difficult for traditional orchestration systems in distant clouds to perform timely management and replacement of microservices to ensure the overall application or service is performant. We propose to address this challenge by decentralizing the service discovery and allocation logic, placing it in client microservices. This paper presents a P2P-based design and prototype system that empowers clients to discover desired services based on pre-defined QoS requirements. If none are found, clients identify compute nodes meeting the requirements to request a new service allocation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".