EMDTORA: Energy-Aware Multi-User Dependent Task Offloading and Resource Allocation in MEC Using Graph-Enabled DRL
Bibliographic record
Abstract
The dawn of the 5G/6G networking era has led to the widespread adoption of Multi-Access Edge Computing (MEC), a paradigm shift that brings computational resources at the network’s edge to enhance device performance and longevity. Additionally, the proliferation of Internet of Things (IoT) has facilitated the development of complex multi-user, multi-edge server environments. In these settings, the interdependence of application tasks makes computational offloading and resource allocation decision-making challenging, but crucial for optimizing energy efficiency. As a response, in this paper, we propose an Energy-Aware Multi-user Dependent Task Offloading and Resource Allocation (EMDTORA) scheme for IoT-MEC infrastructures. First, we formulate an offline, task offloading, multi-objective optimization problem that aims to minimize the user devices’ energy consumption and experienced delay under given constraints. Given the NP-hardness of the problem, we devise an online framework that combines a Graph Attention Networks (GAT)-based mechanism, which captures the in-depth dependency structure of the applications, with an actor-critic off-policy Deep Reinforcement Learning (DRL) algorithm to approximate the optimal solution. Through extensive simulation we highlight the potency of the proposed scheme, as EMDTORA outperforms various baselines by successfully balancing the trade-off between energy consumption and delay minimization, under dynamic network conditions.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".