A New Optimization Approach for Task Scheduling Problem Using Water Cycle Algorithm in Mobile Cloud Computing
Bibliographic record
Abstract
Mobile devices are used by numerous applications that continuously need computing power to grow. Due to limited resources for complex computing, offloading, a service offered for mobile devices, is commonly used in cloud computing. In Mobile Cloud Computing (MCC), offloading decides where to execute the tasks to efficiently maximize the benefits. Hence, we represent offloading as a Task Scheduling Problem (TSP). This latter is a Multi-Objective Optimization (MOO) problem where the goal is to find the best schedule for processing mobile source tasks, while minimizing both the average processor energy consumption and the average task processing time. Owing to the combinatorial nature of the problem, the TSP in MCC is known as NP-hard. To overcome this difficulty in practice, we adopt meta-heuristic search techniques as they offer a good trade-off between solution quality and scalability. More precisely, we introduce a new optimization approach, that we call Multi-objective Discrete Water Cycle Algorithm (MDWCA), to schedule tasks from mobile source nodes to processor resources in a hybrid MCC architecture, including public cloud, cloudlets, and mobile devices. To evaluate the performance of our proposed approach, we conducted several comparative experiments on many generated TSP instances in MCC. The simulation results show that MDWCA outperforms the state-of-the-art optimization algorithms for several quality metrics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".