Task Scheduling Optimization in Cloud Computing by Social Group Optimization Algorithm
Bibliographic record
Abstract
In cloud computing systems, task scheduling is crucial. Task scheduling cannot be done based on a single criterion but rather on rules and regulations that may be referred to as an agreement between cloud customers and providers. This agreement is nothing more than the user's desire for the providers to offer the kind of service that they expect. Providing high-quality services to consumers under the deal is a critical duty for providers, who must also manage many responsibilities. The task scheduling problem may be considered the search for an ideal assignment or mapping of a collection of subtasks of distinct tasks across the available set of resources to meet the intended goals for tasks. This paper proposes an efficient scheduling task algorithm based on the social group optimization of cloud computing systems. By applying it to three cases, we evaluate the performance of our algorithm. The findings suggest that the proposed strategy successfully achieved the best solution in Makespan, Speedup, Efficiency, and Throughput.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".