The effect of object-agent interactions on the performance of CORBA systems
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.
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 | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".