High Availability with Diagonal Replication in 2D Mesh (DR2M) Protocol for Grid Environment
Bibliographic record
Abstract
Replication is a useful technique for distributed database systems and has been implemented in EU data grid and HEP in CERN for handling huge data access. Replica selection in their prototypes still can be enhanced to provide high availability, fault tolerant and low in communication cost. This paper introduces a new replica control protocol, named Diagonal Replication in 2D Mesh (DR2M) for grid environment and compares its performance with the previous protocols. The performance in this paper is data availability for read and write operation, which are compared to the Read-One Write-All (ROWA), Voting (VT), Tree Quorum (TQ), Grid Configuration (GC), Three Dimensional Grid Structure (TDGS), and Diagonal Replication on Grid (DRG). This paper discusses the protocol of replicating data for grid environment, putting the protocol in a logical 2D mesh structure by employing the quorums and voting techniques. The data file is copied in a selected replica from the diagonal sites in each quorum. The selection of a replica depends on the diagonal location of the structured 2D mesh network where the middle replica is selected because it is the shortest path to get a copy of the data from most of the direction in the quorum. The algorithm in this paper also calculates the optimized number of nodes to be grouped in each quorum and how many quorums are needed for the number of nodes, N in a network. DR2M protocol also ensures that the data for read and write operations are consistent, by ensuring the quorum must not have a nonempty intersection quorum. To evaluate the DR2M protocol, we developed a simulation model in Java. Our results prove that our protocol improves the performance of data availability compared to the previous data replication protocol, namely Read-One Write-All (ROWA), Voting (VT), Tree Quorum (TQ), Grid Configuration (GC), Three Dimensional Grid Structure (TDGS), and Diagonal Replication on Grid (DRG).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 | 0.002 |
| 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".