The effect of object-agent interactions on the performance of CORBA systems
Why this work is in the frame
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Bibliographic record
Abstract
The notion of middleware has been introduced to provide interoperability as well as transparent location of servers in heterogeneous client server environments. Although such benefits accrue from the use of middleware, careful consideration of system architecture is required to achieve high performance. Based on implementation and measurements made on the system, the paper is concerned with the impact of client agent server interaction architecture on the performance of a CORBA System. CORBA or Common Object Request Broker Architecture proposed by the Object Management Group is one of the commonly used standards for middleware architecture. Using a commercially available CORBA compliant ORB software called ORBeline, we have implemented two different architectures for client agent server interaction on a network of workstations. In the Handle Driven ORB architecture the client gets the address of the server from the agent and communicates with the server directly. In the Forwarding ORB architecture the client request is automatically forwarded by the agent to the appropriate server which then returns the results of the computations to the client. Our measurements show that the differences among the performance of these architectures change with a change in the workload. The paper reports on the relative performance of the two architectures under different workload conditions. The results provide insights into system behavior. In particular the impact of message size on the latency and scalability attributes of these architectures is analyzed. A discussion of how agent cloning can improve system performance is also included.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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