Hybrid Workload Prediction for Improved Autoscaling in IaaS Clouds: An ARIMA-OLSTM Approach
Bibliographic record
Abstract
Cloud computing's dynamic characteristics require precise prediction of workload and effective auto-scaling to optimize resource usage in Infrastructure-as-a-Service (IaaS) settings.To maximize auto-scaling, this research presents a robust hybrid workload prediction model that uses a hybrid pelican optimization algorithm (POA) for intelligent scaling decisions and Autoregressive Integrated Moving Average-Long Short-Term Memory (ARIMA-OLSTM) for accurate workload forecasting.ARIMA-OLSTM combines deep learning techniques with statistical methods.LSTM (optimized by RMSProp) learns non-linear, sequential information from ARIMA's residuals, while ARIMA represents the linear trends within historical workload sequences.The prediction accuracy is significantly increased by this two-step process.Resource scaling decisions are ideally determined during the planning stage by a Hybrid POA that is inspired by pelican hunting techniques and further improved using Lyrebird Optimization.Through constantly changing virtual machine parameters, it is strategically beneficial to find a balance between cost-effectiveness, system responsiveness, and SLA fulfillment.Extensive tests on realistic cloud workloads demonstrate that the suggested model outperforms existing models such as RHAS, GRASP, and ADA-RP, minimizing RMSE to 0.1513 and MAPE to 0.1557.Furthermore, compared to traditional methods, it maintains 50% less resource use and achieves a 70% reduction in reaction time, confirming the model's effectiveness, scalability, and prediction accuracy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".