Green Resource Allocation Algorithms for Publish/Subscribe Systems
Bibliographic record
Abstract
A popular trend in large enterprises today is the adoption of green IT strategies that use resources as efficiently as possible to reduce IT operational costs. With the publish/subscribe middleware playing a vital role in seamlessly integrating applications at large enterprises including Google and Yahoo, our goal is to search for resource allocation algorithms that enable publish/subscribe systems to use system resources as efficiently as possible. To meet this goal, we develop methodologies that minimize system-wide message rates, broker load, hop count, and the number of allocated brokers, while maximizing the resource utilization of allocated brokers to achieve maximum efficiency. Our contributions consist of developing a bit vector supported resource allocation framework, designing and comparing four different classes with a total of ten variations of subscription allocation algorithms, and developing a recursive overlay construction algorithm. A compelling feature of our work is that it works under any arbitrary workload distribution and is independent of the publish/subscribe language, which makes it easily applicable to any topic and content-based publish/subscribe system. Experiments on a cluster testbed and a high performance computing platform show that our approach reduces the average broker message rate by up to 92% and the number of allocated brokers by up to 91%.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".