A multi-agent-based simulator for a transmission control protocol/internet protocol network
Bibliographic record
Abstract
The main goal of this paper is building a novel transmission control protocol/internet protocol (TCP/IP) network simulator engine for simulation of distributed applications for which capturing both higher and lower layer network parameters are important. There are not many comprehensive simulators available in industry and academia to simulate distributed applications while reporting the parameters of all the layers of the TCP/IP network active in such simulations. The major problem in building a comprehensive simulation scenario for applications residing on the higher layers of a network by using currently available simulators is that a core simulator for lower layers of the network should be used together with add-ons or other programs simulating higher network layers to be able to simulate the whole TCP/IP network. This paper presents a novel idea for network simulation that has not been implemented before, which is using agents to simulate all layers of the network. In this simulator, each TCP/IP layer is simulated separately by using a separate agent and its behavior. It is an integrated environment based on agent systems capable of simulating all layers of a TCP/IP network, including application and lower layers. The final goal is other agent systems simulating a complex higher level web-based distributed application being easily used together with these agents, which are simulating the core TCP/IP network. For evaluation and testing purposes, a simple distributed application consisting of several remote procedure calls is simulated. For the validation of the conducted simulations, the achieved results are compared with the results of two non-agent-based simulators. For the verification of each individual agent function, a report is generated that shows the information flow between agents. The communication routes between agents are checked manually to make sure the route selection is based on the expected behavior of each agent. The scalability of the proposed multi-agent-based simulator is tested for the given distributed application.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".