Deep Learning Approach for Cost and Storage Optimization of Video Streaming in Cloud Environments
Bibliographic record
Abstract
As the demand for video streaming continues to rise, cloud computing has become a vital infrastructure for delivering reliable and scalable services. However, cost management and storage allocation pose significant challenges in this context. This paper presents a novel application of deep learning techniques to optimize cost and storage utilization in cloud environments for video streaming. The proposed framework leverages a hybrid deep learning model that combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. CNNs are employed for pattern recognition, allowing the model to identify recurring patterns in resource usage. LSTM networks, on the other hand, specialize in sequence prediction and enable accurate forecasting of future resource demands. By analyzing historical video views, the model can make precise predictions about future resource needs. This enables dynamic resource scaling, allowing cloud infrastructure to be efficiently allocated in response to streaming demands. Moreover, the framework addresses storage optimization challenges by incorporating deep learning insights into data placement, replication, and retrieval strategies. The results demonstrate significant improvements in cost efficiency and storage utilization, with cost reductions ranging from 15% to 20% compared to state-of-the-art methods.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".