A Hybrid Framework for Job Scheduling on the Cloud through Firefly and Cuckoo Search Algorithm
Bibliographic record
Abstract
Cloud Scheduler can reliably and effectively automate a lot of the time-consuming processes involved in maintaining cloud infrastructure. To deliver resources successfully, it is necessary to investigate and analyse the scheduling algorithms of recent trends. This problem can be solved by the use of metaheuristic scheduling algorithms. Nesterov's Accelerated Gradient can fix the problem of being stuck at a local minimum in the cuckoo search algorithm (CSA). In the proposed algorithm, Nesterov Accelerated Gradient (NAG) is used for the local search, and Levy Flights are used for the global search. The combination of NAG and CSA helps users save money and time. The simulation was done with the Clouds tool and three different real datasets. Multiple criteria will be used to schedule the different jobs that are on different servers. A hybrid optimisation algorithm is used to plan how the jobs will be done. Different requirements will be taken into account, and the environment will be simulated using the CloudSim tool.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".