A Robust, Preference-Based Coordinator Election Algorithm for Distributed Systems
Bibliographic record
Abstract
In peer-to-peer distributed systems, the selection of a reliable coordinator is a pivotal process, often vulnerable to node failure and communication link failure.Herein, we present an innovative Fault-Tolerant Coordinator Election Algorithm (FTCEA) designed to address these issues, specifically crafted to withstand node failures in peer-to-peer distributed systems.Our algorithm distinguishes itself by capitalizing on a unique preference-based method, which incorporates significant nodal attributes into the election process.This integration of nodal attributes contributes to the election of a durable and reliable coordinator, significantly enhancing the robustness of the system.A comprehensive analysis was conducted to measure FTCEA's communication complexity, execution time, and space complexity using a peer-to-peer distributed application.The results demonstrated that FTCEA successfully identifies a coordinator node with a communication cost of O(n) messages and a space complexity linear to the number of attributes, represented as O(n.m).Remarkably, FTCEA demonstrated an approximately 50.10% improvement in communication cost compared to the enhanced Bully algorithm, a widely utilized method in this domain.Moreover, FTCEA can maintain a linear storage cost of O(n), thereby significantly improving the computation cost.In summary, FTCEA offers a scalable and efficient solution for coordinator election in distributed systems, showing promising potential for practical applications in the field.The algorithm's unique design, robustness, and efficiency make it a valuable contribution to the advancement of peer-to-peer distributed systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".