QoS‐Aware Load Balancing in Cloud Computing Based on Chronological Kookaburra Optimization Algorithm
Bibliographic record
Abstract
ABSTRACT In cloud computing environments, user requests often lead to varying load conditions across the system, resulting in underloaded, overloaded, or balanced states. Both underloading and overloading can cause system inefficiencies, including increased power consumption, prolonged execution times, and higher machine failure rates. Therefore, effective load balancing (LB) becomes a critical aspect of task scheduling in cloud systems, whether at the level of virtual machines (VMs) or independently. To address these challenges, this paper proposes the Chronological Kookaburra Optimization Algorithm (ChKOA) for efficient LB in cloud computing (CC). The proposed ChKOA is the combination of chronological concept with the Kookaburra Optimization Algorithm (KOA). Initially, tasks are assigned to VMs in a round‐robin manner. Based on specific VM parameters, the VMs are classified into overloaded and underloaded categories using deep embedded clustering (DEC). Tasks in overloaded VMs are prioritized and redistributed to underloaded VMs, considering factors such as supply, demand, capacity, predicted load, and key Quality of Service (QoS) metrics, including resource availability and reliability. Load prediction is performed using a Deep Residual Network (DRN). Simulation results demonstrate that the proposed ChKOA achieves a balanced load of 0.535, capacity utilization of 0.954, resource availability of 0.954, reliability of 0.936, and a computational cost of 0.327 s.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".