Survey of the Influence of Routing Protocols to Network Performance Enhancement
Bibliographic record
Abstract
Routing protocols play a crucial role in the operation of computer networks by determining the optimal paths for data transmission. The selection of an appropriate routing protocol can significantly impact network performance, including factors such as latency, throughput, reliability, and scalability. This survey aims to provide a comprehensive analysis of the influence of various routing protocols on network performance enhancement. The survey begins by presenting an overview of common routing protocols with their key characteristics. Subsequently, it explores the impact of these protocols on network performance metrics, focusing on their ability to adapt to changing network conditions, mitigate congestion, and ensure efficient resource utilization. Through a systematic review of literature, empirical studies, and real-world implementations, this survey aims to provide network administrators, researchers, and practitioners with valuable insights into selecting and optimizing routing protocols for specific network environments. Additionally, it identifies areas for further research and development to continue advancing the field of routing protocols and network performance enhancement. Keywords: Optimizing Routing Protocols, Performance Metrics, Reliability and Scalability, Resource Utilization, Wireless Local Area Networks, Accurate Routing Tables Juliana I. Consul & Bunakiye R. Japheth (2023): Survey of the Influence of Routing Protocols to Network Performance Enhancement. Journal of Advances in Mathematical & Computational Science. Vol. 11, No. 4. Pp 13-28 Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V11N4P2
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".