Model Agnostic Predictive Smart Load Balancer in Microservices Environment
Bibliographic record
Abstract
This paper presents a model-agnostic predictive smart load balancer for microservices environments, utilizing machine learning to optimize load distribution and resource utilization. The system leverages access logs from NASA, Calgary, and Clarknet datasets to categorize requests and estimate service times for various request types. Advanced ML models, including LSTM, GRU, RNN, ARIMA, and SARIMA, are employed to predict future loads on microservice instances. The load balancer uses these predictions to make proactive decisions, redirecting requests using a weighted round-robin algorithm and preemptively creating new instances when necessary. This approach ensures efficient handling of incoming requests, reduces wait times, and prevents resource over-provisioning. The system’s model-agnostic design allows for seamless integration of new ML models, enhancing its flexibility and robustness. Experimental results demonstrate significant improvements in load management and cost optimization compared to traditional load balancers, highlighting the effectiveness of incorporating ML for intelligent load balancing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".