Redundancy Schemes for High Availability Computer Clusters
Bibliographic record
Abstract
The primary goal of computer clusters is to improve computing performances by taking advantage of the parallelism they intrinsically provide. Moreover, their use of redundant hardware components enables them to offer high availability services. In this paper, we present an analytical model for analyzing redundancy schemes and their impact on the cluster’s overall performance. Furthermore, several cluster redundancy techniques are analyzed with an emphasis on hardware and data redundancy, from which we derive an applicable redundancy scheme design. Also, our solution provides a disaster recovery mechanism that improves the cluster’s availability. In the case of data redundancy, we present improvements to the replication and parity data replication techniques for which we investigate the availability of the cluster under several scenarios that take into account, among other things, the number of replicated nodes, the number of CPUs that hold parity data and the relation between primary and replicated data. For this purpose, we developed a simulator that analyzes the impact of a redundancy scheme on the processing rate of the cluster. We also studied the performance of two well-known schemes according to the usage rate of the CPUs. We found that two important aspects influencing the performance of a transaction-oriented cluster were the cluster’s failover and data redundancy schemes. We simulated several data redundancy schemes and found that data replication offered higher cluster availability than the parity model.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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 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".