Software Defined Cooperative Offloading for Mobile Cloudlets
Bibliographic record
Abstract
Device to Device communication enables the deployment of mobile cloudlets in LTE-advanced networks. The distributed nature of mobile users and dynamic task arrivals makes it challenging to schedule tasks fairly among multiple devices. Leveraging the idea of software defined networking, we propose a software defined cooperative offloading model (SDCOM), where the SDCOM controller is deployed at the PDN gateway and schedules tasks in a centralized manner to save the energy of mobile devices and reduce the traffic on access links. We formulate the minimum-energy task scheduling problem as a 0-1 knapsack problem and prove its NP-hardness. To compute the optimal solution as a benchmark, we design the conditioned optimal algorithm based on the aggregated analysis of energy consumption. The greedy algorithm with a polynominal-time complexity is proposed to solve large-scale problems efficiently. To address the problem without predicting future information on task arrivals, we further design an online task scheduling algorithm (OTS). It can minimize the energy consumption arbitrarily close to the optimal solution by appropriately setting the tradeoff coefficient. Moreover, we extend OTS to design a proportional fair online task scheduling algorithm to achieve the fair energy consumption among mobile devices. Extensive trace-based simulations demonstrate the effectiveness of SDCOM for a variety of typical mobile devices and applications.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".