Optimizing Task Scheduling in Cloud Computing Using Discrete Tuna Swarm Optimization
Bibliographic record
Abstract
Task scheduling in cloud computing represents a pivotal challenge, necessitating the efficient allocation of computing tasks to available resources.This challenge is crucial in diverse sectors such as e-commerce, e-learning, and e-health, and is compounded by the heterogeneity of tasks and resources, fluctuating demands, and the need to optimize multiple objectives like Makespan, resource utilization, and throughput.In the quest to resolve these complexities, meta-heuristic algorithms inspired by natural phenomena have gained prominence.Among them, the Tuna Swarm Optimization (TSO) algorithm stands out for its proficient ability to navigate and exploit the search space effectively.This paper introduces a novel algorithm, the Discrete Tuna Swarm Optimization for Task Scheduling (DTSO-TS), derived from the TSO algorithm.DTSO-TS algorithm's goal is to efficiently distribute tasks among virtual machines, balance workloads and improve resource utilization to minimize Makespan while increasing throughput.A fitness function provides optimal solutions to this goal.Creates a swarm before evaluating and refining solutions which have proven their worth.By contrasting it with well-known scheduling algorithms such as Ant-Colony-Based, Particle Swarm Optimisation, Genetic Algorithm, First Come First Serve, Round Robin, and Shortest Job First, we may evaluate DTSO-TS's effectiveness.According to the comparison results, DTSO-TS is the best option for scheduling tasks in cloud computing contexts.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".