Bibliographic record
Abstract
In recent years, multi-tier architectures have become the standard computing environment for web- and enterprise applications. The application server tier is often the heart of the system embedding the business logic. Adaptability, in particular the capability to adjust to the load submitted to the system and to handle the failure of individual components, are of outmost importance in order to provide 7/24 access and high performance. Replication is a common means to achieve these reliability and scalability requirements. With replication, the application server tier consists of several server replicas. Thus, if one replica fails, others can take over. Furthermore, the load can be distributed across the available replicas. Although many replication solutions have been proposed so far, most of them have been either developed for fault-tolerance or for scalability. Furthermore, only few have considered that the application server tier is only one tier in a multi-tier architecture, that this tier maintains state, and that execution in this environment can follow complex patterns. Thus, existing solutions often do not provide correctness beyond some basic application scenarios. In this thesis we tackle the issue of replication of the application server tier from ground off and develop a unified solution that provides both fault-tolerance and scalability. We first describe a set of execution patterns that describe how requests are typically executed in multi-tier architectures. They consider the flow of execution across client tier, application server tier, and database tier. In particular, the execution patterns describe how requests are associated with transactions, the fundamental execution units at application server and database tiers. Having these execution patterns in mind, we provide a formal definition of what it means to provide a correct execution across all tiers, even in case failures occur and the application server tier is replicated. Informally, a replicated system is correct if it behaves exactly as a non-replicated that never fails. From there, we propose a set of replication algorithms for fault-tolerance that provide correctness for the execution patterns that we have identified The main principle is to let a primary AS replica to execute all client requests, and to propagate any state changes performed by a transaction to backup replicas at transaction commit time. The challenges occur as requests can be associated in different ways with transactions. Then, we extend our fault-tolerance solution and develop a unified solution that provides both fault-tolerance and load-balancing. In this extended solution, each application server replica is able to execute client requests as a primary and at the same time serves as backup for other replicas. The framework provides a transparent, truly distributed and lightweight load distribution mechanism which takes advantage of the fault-tolerance infrastructure. Our replication tool is implemented as a plug-in of JBoss application server and the performance is carefully evaluated, comparing with JBoss' own replication solutions. The evaluation shows that our protocols have very good performance and compare favorably with existing solutions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".