Performance Evaluation and Comparison of Distributed Messaging Using Message Oriented Middleware
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Message Oriented Middleware (MOM) is an enabling technology for modern event-driven applications that are typically based on publish/subscribe communication (Eugster, 2003). Enterprises typically contain hundreds of applications operating in environments with diverse databases and operating systems. Integration of these applications is required to coordinate the business process. Unfortunately, this is no easy task. Enterprise Integration, according to the authors in (Brosey et al, 2001), "aims to connect and combines people, processes, systems, and technologies to ensure that the right people and the right processes have the right information and the right resources at the right time”. Communication between different applications can be achieved by using synchronous and asynchronous communication tools. In synchronous communication, both parties involved must be online (for example, a telephone call), whereas in asynchronous communication, only one member needs to be online (email). Middleware is software that helps two applications communicate with one another. Remote Procedure Calls (RPC) and Object Request Brokers (ORB) are two types of synchronous middleware—when they send a request they must wait for an immediate reply. This can decrease an application’s performance when there is no need for synchronous communication. Even though asynchronous distributed messaging using message oriented middleware is widely used in industry, there is not enough work done in evaluating the performance of various open source Message oriented middleware. The objective of this work was to benchmark and evaluate three different open source MOM’s performance in publish/subscribe and point-to-point domains, and provide a functional comparison and qualitative study from developers perspective.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it