Time-and-Traffic-aware collaborative task offloading with service caching-replacement in cloud-assisted mobile edge computing
Bibliographic record
Abstract
Abstract The rapid growth of Internet of Things (IoT) applications has increased the demand for ultra-low-latency and energy-efficient computing. While Mobile Edge Computing (MEC) addresses these demands by shifting computation from the centralized cloud to edge servers, its limited resources pose a major challenge. In particular, making optimal decisions for service caching and task offloading under dynamic network conditions and energy constraints remains a critical issue. Efficient caching is essential for latency-sensitive IoT tasks, yet only a subset of services can be stored at MEC-enabled base stations (BSs) due to storage limitations. This paper proposes a Cloud-assisted MEC framework that jointly optimizes service caching, service replacement, and task offloading to enhance long-term system performance. A two-phase solution is developed: first, an Irregular Cellular Learning Automata (ICLA)-based algorithm classifies traffic patterns and timescales, and a Distributed Deep Reinforcement Learning (DDRL) algorithm performs adaptive, decentralized task offloading. To address caching constraints, a dynamic 0–1 knapsack approach selects services based on popularity, while a Q-learning-based policy handles service replacement. Simulation results validate the framework’s effectiveness, showing significant reductions in service latency and energy usage, with improved scalability and adaptability over traditional centralized approaches. The proposed method offers a robust and practical solution for next-generation MEC systems supporting real-time IoT services.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".