Deep and reinforcement learning for automated task scheduling in large‐scale cloud computing systems
Why this work is in the frame
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Bibliographic record
Abstract
Summary Cloud computing is undeniably becoming the main computing and storage platform for today's major workloads. From Internet of things and Industry 4.0 workloads to big data analytics and decision‐making jobs, cloud systems daily receive a massive number of tasks that need to be simultaneously and efficiently mapped onto the cloud resources. Therefore, deriving an appropriate task scheduling mechanism that can both minimize tasks' execution delay and cloud resources utilization is of prime importance. Recently, the concept of cloud automation has emerged to reduce the manual intervention and improve the resource management in large‐scale cloud computing workloads. In this article, we capitalize on this concept and propose four deep and reinforcement learning‐based scheduling approaches to automate the process of scheduling large‐scale workloads onto cloud computing resources, while reducing both the resource consumption and task waiting time. These approaches are: reinforcement learning (RL), deep Q networks, recurrent neural network long short‐term memory (RNN‐LSTM), and deep reinforcement learning combined with LSTM (DRL‐LSTM). Experiments conducted using real‐world datasets from Google Cloud Platform revealed that DRL‐LSTM outperforms the other three approaches. The experiments also showed that DRL‐LSTM minimizes the CPU usage cost up to 67 % compared with the shortest job first (SJF), and up to 35 % compared with both the round robin (RR) and improved particle swarm optimization (PSO) approaches. Moreover, our DRL‐LSTM solution decreases the RAM memory usage cost up to 72 % compared with the SJF, up to 65 % compared with the RR, and up to 31.25 % compared with the improved PSO.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it