CHANGE: Delay-Aware Service Function Chain Orchestration at the Edge
Bibliographic record
Abstract
In Mobile Edge Computing (MEC), the network's edge is equipped with computing and storage resources in order to reduce latency by minimizing communication with remote clouds. However, the available computing capacity at the edge is limited compared to that of remote clouds. A promising solution for efficient utilization of the limited capacity at the edge is fine-grained processing of user demands via Virtual Network Functions (VNFs). In this approach, user service demands are expressed as Service Function Chains (SFCs), which are composed of virtual network functions. Such service composition allows constituent VNFs to be flexibly deployed at the edge or in the cloud such that the service latency is minimized. The increasing number of users, however, challenges the scalability of system-managed SFC orchestration. To address this problem, we propose a user-managed online SFC orchestration framework at the edge of the network, called CHANGE, that minimizes service latency by jointly considering the effect of user mobility, edge capacity and service migration. We first present the theoretical foundations of CHANGE and then evaluate its performance via model-driven simulations and realistic Mininet-WiFi emulations. Our results show that CHANGE can improve latency performance by nearly 20% compared to other approaches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".