Probability-Based Crossover Genetic Algorithm for Task Scheduling in Cloud Computing
Bibliographic record
Abstract
In order to meet the rising demand for cloud services and remain in compliance with Service Level Agreements (SLA), service providers require effective task scheduling solutions capable of adapting to cloud computing’s elastic and dynamic characteristics.In this paper, we propose a novel approach to optimize task scheduling in cloud computing called a ProbabilityBased Crossover Genetic Algorithm (PxGA) with a primary objective of minimizing the tasks execution makespan. PxGA is an improvement on the Genetic Algorithm (GA) achieved by introducing the concept of Virtual Machine (VM) fitness and applying it to implement an effective weighted probabilistic crossover technique. Using the CloudSim simulation toolkit, we conduct our analysis of PxGA and evaluate it against standard and more recent task scheduling algorithms. The results of the simulations show that our proposed task scheduling algorithm is superior to other task scheduling algorithms in terms of the makespan, the VMs energy consumption, and the degree of imbalance (DoI). Moreover, the computational time (CT) for the PxGA decreases when compared against the other evaluated algorithms, except for its base GA.
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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 | 0.001 | 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 | 0.001 |
| 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".