Towards Sustainable Edge Computing: Efficient Task Offloading for Energy Efficiency and Latency Reduction
Bibliographic record
Abstract
In networks with limited resources, the concept of offloading computation to Mobile Edge Computing (MEC) has emerged as a promising research direction with the advent of new services in fifth-generation (5G) networks. However, poorly designed offloading strategies can lead to excessive energy consumption and unpredictable latency, while the number of dropped tasks significantly impacts system efficiency. This paper presents a 5G-MEC task offloading scenario aimed at minimizing computation and communication latency, energy consumption, and the rate of dropped tasks. To achieve this, we employ Mixed Integer non-Linear Programming (MINLP) and Mixed Integer Linear Programming (MILP), comparing their performance with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Our analysis considers the impact of the quantity of tasks and User Equipment (UE) on network parameters, distinguishing between urgent and non-urgent tasks. We ensure a zero-dropped task rate for urgent tasks. The proposed approach outperforms baseline techniques such as First Come First Serve (FCFS), Shortest Deadline First (SDF), and Urgent Tasks First (UTF) in the context of 5G-MEC task offloading. Specifically, compared to MILP, PSO, and GA, the MINLP-based approach reduces total latency by 12%, 34%, and 44%, respectively. Moreover, it decreases energy consumption by 8%, 30%, and 47% compared to MILP, PSO, and GA, respectively. The dropped task ratio is also reduced by 17%, 42%, and 65% under the MINLP-based approach compared to MILP, PSO, and GA, respectively.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".