Investigating performance optimization through balanced task scheduling heuristics and DVFS in hybrid fog-cloud computing platforms
Bibliographic record
Abstract
In the evolving landscape of fog-cloud hybrid computing, efficient task scheduling plays a vital role in meeting real-time requirements while optimizing resource utilization and energy consumption. This work proposes balanced minimum response time (MRT) and balanced minimum energy consumption (MEC) heuristics to address the challenges of task allocation on hybrid computing platforms in a balanced manner. These heuristics are employed to assess the efficacy of complementing cloud or fog networks, supported by dynamic voltage and frequency scaling (DVFS), leveraging deadline laxities without compromising user satisfaction. Three hybrid computing sub-scenarios are introduced: cloud-oriented, fog-oriented, and balanced-hybrid, based on the relative compute capacity of fog and cloud networks. The results reveal that a cloud network complementing a fog network is more beneficial for achieving lower makespan, while for lower energy consumption, it is preferable for fog network to complement the cloud network. The research emphasizes the advantages of balanced heuristics under different computing scenarios to optimize makespan, energy consumption, and workload allocation, leading to overall computing cost reduction using realistic workloads. Further, judiciously applying DVFS to low-capacity fog nodes is more fruitful than powerful cloud nodes due to extravagant idle energy consumption in the generated scheduling holes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 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".