An intelligent load balancer for software defined networking (SDN) based cloud infrastructure
Bibliographic record
Abstract
Cloud technology is an emerging distributed computing paradigm. It provides Cloud consumers with on-demand computing, storage, and networking resources. A Cloud Resource Broker (CSB) acts as a mediator between cloud consumers and cloud providers. It effectively handles application requests and efficiently allocates cloud resources. Generally, CSB encounters large volumes of application requests. It is essential to properly and evenly distribute these application requests between the available cloud resources. The proposed work introduces a Particle Swarm Optimization (PSO) based load balancing technique, which distributes cloud consumer application requests in an optimal and balanced manner. In addition, the present study investigates the Software-Defined Networking (SDN) cloud infrastructure, which dynamically configures and provides network paths on-demand, based on the network load. The proposed system is simulated and tested on real-world application traces. The proposed PSO based load balancing mechanism is shown to minimize average application response time, while maximizing throughput, cloud consumer satisfaction value, and resource utilization.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".