The HAS architecture : a highly available and scalable cluster architecture for Web servers
Bibliographic record
Abstract
This dissertation proposes a novel architecture, called the HAS architecture, for highly available and scalable web server clusters. The proof-of-concept of the HAS architecture was validated for performance and scalability, tested for its failover mechanisms, and externally modeled and simulated to study the failure and repair behavior and to calculate the availability of the cluster. The HAS architecture is able to maintain the base line performance per cluster processor, for up to 16 traffic processors in the cluster, achieving close to linear scalability. The architecture supports dynamic traffic distribution, supports heterogeneous cluster nodes, provides a mechanism to keep track of available cluster nodes, and offers connection synchronization to ensure that web connections survive software or hardware failures. Furthermore, the architecture supports different redundancy models and high availability capabilities such as Ethernet and NFS redundancy, and node level redundancy that contribute in increasing the availability of the service, and in eliminating single points of failure. This dissertation presents current methods for scaling web servers, discusses their limitations, and investigates how clustering technologies can help overcome some of these challenges and enable the design of scalable web servers based on a cluster of workstations. It examines various ongoing research projects in the academia and the industry that are investigating scalable and highly available architectures for web servers. It discusses their scope, architecture, provides a critical analysis of their work, and presents their contributions to this dissertation. This dissertation contributes the HAS architecture, a highly available and scalable architecture for web servers, and offers contributions in areas of scalability, maintaining baseline performance, and availability
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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 | 0.001 |
| 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".