SDN-based Service Discovery and Assignment Framework to Preserve Service Availability in Telco-based Multi-Access Edge Computing
Bibliographic record
Abstract
The ever-growing number of connected User Equipment (UE), e.g., Internet of Things (IoT) devices, Connected Autonomous Vehicles (CAVs), has driven the evolution of Software-Defined Networks (SDN) and Fifth-Generation (5G) Networks to push the computing resources closer to the UE. Towards that end, Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) are promising solutions to facilitate the deployment of the required user’s service instances within an approximately one-hop communication range. However, discovering and assigning such service instances to the UE to maintain a high service availability is still an open challenge in the 3rd Generation Partnership Project (3GPP) standards due to the UE mobility and Telco network heterogeneity. This paper proposes an SDN-based dynamic service discovery and assignment framework for a distributed MEC infrastructure. The proposed framework considers various decision parameters such as UE’s location, the service instance’s demand (i.e., resource utilization), the network link status, and the service instance performance requirements (i.e., service profile) to offer a generic solution for discovering and assigning the service instances to the UE. The framework implementation results show an enhancement of the packet delivery ratio and a lower users’ perceived latency.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".