Sustainable Multi-MEC Task Offloading for 5G-Enabled Internet of Things Devices
Bibliographic record
Abstract
Efficient task offloading strategies are essential in Multi-access Edge Computing (MEC) environments, particularly within fifth-generation (5G) networks, where inefficient management can lead to significant latency and increased rates of task drops. Given the rapid growth in connected devices and data-intensive applications, achieving scalability in MEC systems is vital for maintaining low latency, high reliability, and efficient data management. This study provides critical insights into the performance implications of varying the number of MEC servers, specifically examining dropped task ratios and latency. Utilizing Mixed Integer Linear Programming (MILP), we comprehensively assess how system performance scales with an increasing number of users and MEC servers. Our analysis demonstrates that MILP consistently yields superior results, effectively minimizing both latency and dropped task ratios even under substantial loads. In particular, scaling from 1 to 2 MEC servers yields a 57% reduction in the dropped task ratio, and further increasing from 2 to 4 MEC servers achieves an additional 53% reduction. Furthermore, in the scaling scenario of 1 to 2 MEC servers, MILP outperforms particle swarm optimization (PSO) by 33.3%, underscoring its effectiveness. In addition, our study investigates energy consumption by comparing two distinct scenarios: full offloading versus entirely local task processing. The analysis reveals significant energy savings through offloading, particularly for large image sizes, where offloading achieves up to a 52.44% reduction in energy consumption.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".