Web service replica selection analysis using a multiagent-based simulator
Bibliographic record
Abstract
A distributed system always runs on top of a computer network and cannot be separated from it. In many cases this network consists of hundreds or thousands of computers and processing nodes. An effective distributed system simulator needs to simulate the underlying network. Unfortunately a great majority of existing simulation tools are pure network simulators. Even though they are very effective for designing, and evaluating computer networks, they could not be used to simulate a distributed application like a web service based application. Many components in a distributed system are complex servers and software applications running on top of all network layers. Network simulators cannot simulate them. A higher-level simulator is required to simulate their behavior. This work introduces an agent-based simulation model that integrates the simulation of a computer network and higher-level components of a distributed application. The distributed nature of agents makes them suitable to model and simulate distributed architectures including computer networks and distributed systems. To evaluate this approach the behavior of a replicated web service application will be simulated to show how effectively multi- agent-systems could be used to simulate the behavior of a distributed system.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".