Simple and Scalable Approach for Virtualized Network Function Placement in Wireless Multi-hop Networks
Bibliographic record
Abstract
Network Function Virtualization (NFV) can lower the CAPEX and/or OPEX for service providers and allow for quick deployment of services.The main challenge in the use of Virtualized Network Functions (VNF) is the VNFs' placement in the network.This research provides mathematical models and heuristics for NF placement for wired and wireless networks.We use Integer Linear and Non-Linear Programming as a mathematical optimization program for NF placement.We start from a basic model for a wired network and extend it gradually to develop a traffic-aware mathematical model for NF placement in wireless multi-hop networks.For the first time, we model the interference which is a major difference between a wired and wireless network and included it in our optimization model.We identified the issue of scarcity of BW in wireless multi-hop networks and its role in the average cost of placement and acceptance rate of requests.The critical problem of mathematical models is that they are NP-hard, and consequently not applicable to larger networks.While there exist many efforts in designing a heuristic model that can provide solutions in a timely manner, the primary focus with such heuristics was almost always whether they provide near-optimal results.Consequently, the heuristics themselves become quite non-trivial, and solving the placement problem for larger networks still takes a significant amount of time.In our research, in contrast, we focus on designing a simple and scalable heuristic.We propose a set of heuristics, which are gradually becoming more complex.We start from the random placement heuristic as the simplest approach and at each step add a parameter such as choosing between shortest paths, sort NFs based on their nodal resources, and replacing previously placed NFs to our heuristic.We compare the performance of our heuristics with each other, related heuristics, and our mathematical model.Our results demonstrate that the simple approach of placing NFs along their shortest path can find near-optimal solutions much faster than the other more complicated heuristics while keeping the ratio of accepted requests close to the acceptance ratio of a NP-hard optimization model.Firstly, I would like to express my sincere gratitude to my advisor, Professor Thomas Kunz, for the continuous support of my Ph.D. study and related research, for your patience, motivation, and immense knowledge.Your guidance helped me in all the time of research and writing of this thesis.Your constant willingness to share ideas with me regarding my research, in spite of your busy schedule, is sincerely appreciated.I cannot express my unfailing gratitude and love to my
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".