Performance Evaluation and Comparison of Distributed Messaging Using Message Oriented Middleware
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.002 | 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".