Source-linked AI summary
DMRO:A Deep Meta Reinforcement Learning-based Task Offloading Framework for Edge-Cloud Computing
Guanjin Qu, Huaming Wu
TL;DR
MEC offloading must overcome NP-hard decision-making, slow learning, and poor portability under changing environments. The paper proposes DMRO, combining distributed deep reinforcement learning with meta-learning, and reports better offloading decisions, portability, and rapid convergence.
Problem
MEC offloading is NP-hard, while existing methods can learn slowly and lose effective network parameters when environments change.
Method
DMRO combines distributed deep reinforcement learning with meta-learning that trains initial neural-network parameters for rapid adaptation.
Results
DMRO performs better than full offloading and conventional reinforcement-learning methods, with stronger portability and rapid environment learning.
Takeaways & Limitations
After MEC changes, DMRO can quickly converge and provide low-cost offloading solutions using only a small number of learning steps.
Takeaways & Limitations
The study focuses on one edge server and one cloud server, although the authors describe the model as scalable to multiple servers.
Abstract
from arXiv · showhide
With the continuous growth of mobile data and the unprecedented demand for computing power, resource-constrained edge devices cannot effectively meet the requirements of Internet of Things (IoT) applications and Deep Neural Network (DNN) computing. As a distributed computing paradigm, edge offloading that migrates complex tasks from IoT devices to edge-cloud servers can break through the resource limitation of IoT devices, reduce the computing burden and improve the efficiency of task processing. However, the problem of optimal offloading decision-making is NP-hard, traditional optimization methods are difficult to achieve results efficiently. Besides, there are still some shortcomings in existing deep learning methods, e.g., the slow learning speed and the failure of the original network parameters when the environment changes. To tackle these challenges, we propose a Deep Meta Reinforcement Learning-based offloading (DMRO) algorithm, which combines multiple parallel DNNs with Q-learning to make fine-grained offloading decisions. By aggregating the perceptive ability of deep learning, the decision-making ability of reinforcement learning, and the rapid environment learning ability of meta-learning, it is possible to quickly and flexibly obtain the optimal offloading strategy from the IoT environment. Simulation results demonstrate that the proposed algorithm achieves obvious improvement over the Deep Q-Learning algorithm and has strong portability in making real-time offloading decisions even in time-varying IoT environments.
1 INTRODUCTION
IoT edge offloading must make timely task-placement decisions under changing network and computing conditions while balancing delay and energy consumption. DMRO combines distributed deep reinforcement learning with meta-learning to improve decision quality, learning speed, and portability.
- Edge offloading decisions must account for user habits, wireless channels, connection quality, device availability, and server performance.
- Changing MEC environments make recalculation costly, while conventional intelligent methods learn slowly and may lose useful network parameters.
- The decision problem is NP-hard when dependent-task workflows must balance IoT delay and energy consumption.
- DMRO combines multiple parallel DNNs with deep Q-learning to make offloading decisions.
- Meta-learning trains neural-network initial parameters across environments so the offloading model can adapt quickly to new conditions.
2 RELATED WORK
Related work spans MEC architectures, optimization and heuristic methods, and AI-based offloading. Traditional approaches can require complex iterative computation, while deep reinforcement learning addresses decision-making but may learn slowly and restart when environments change.
- MEC connects IoT devices to cloud centers through nearby edge servers, with edge servers deciding task placement under limited computing resources.
- Task offloading commonly partitions applications into associated or independent subtasks, then allocates computing power, bandwidth, and energy after placement decisions.
- Traditional and heuristic offloading methods address NP-hard placement problems but may require many iterations and complex calculations.
- Deep learning and reinforcement learning provide intelligent alternatives, including distributed DNNs that generate offloading decisions.
- Deep reinforcement learning models offloading as state, action, and reward interaction, but existing methods may omit cloud servers and relearn when environments change.
- DRL-based offloading remains limited by slow learning and repeated recalculation, motivating methods that provide better decisions more quickly.
3 SYSTEM MODEL AND PROBLEM FORMULATION
The paper models IoT workflows across local, edge, and cloud execution, then formulates offloading as a joint delay–energy optimization problem. The system accounts for task dependencies, transmission paths, computing locations, and weighted energy consumption.
- 3.1 System Model: The system comprises a cloud server, an edge server, and multiple IoT devices that execute workflows locally or offload them to either server.
- 3.1 System Model: Each device program is represented as a sequential workflow containing tasks and data flows between dependent tasks.
- 3.1 System Model: Each workflow task is assigned a local, edge-server, or cloud-server execution decision using a matrix variable.
- 3.2 Delay Model: Task-offloading delay includes computation and transmission delay, while decision-making delay is omitted because it is considered short.
- 3.2 Delay Model: The total workflow delay aggregates task execution and inter-task transmission effects across the workflow's N associated tasks.
- 3.3 Energy Consumption Model: Workflow energy consumption weights local, edge, and cloud energy components through α and β, while task-transmission energy is ignored for simplicity.
- 3.4 Problem Formulation: The system utility Q(x, b) is defined as a weighted sum of energy consumption and workflow completion delay across M workflows.
- 3.4 Problem Formulation: The resulting optimization problem seeks to minimize workflow completion delay and corresponding energy consumption simultaneously.
4 DEEP META REINFORCEMENT LEARNING-BASED OFFLOADING FRAMEWORK
DMRO combines a distributed parallel deep reinforcement-learning offloading model with an outer meta-learning model. The framework assigns dependent workflow tasks to execution locations and adapts neural-network initialization when the MEC environment changes.
- DMRO contains an inner distributed reinforcement-learning decision model and an outer meta-learning training model to improve neural-network portability.
- 4.2 Outer Model: The outer model learns initial parameters and can adjust the inner model when edge-server performance or bandwidth changes.
- 4.1 Inner Model: The inner model applies parallel Q-learning to environmental parameters, initial parameters, and workflow information.
- 4.1 Inner Model: Each subtask uses an offloading action coded as 0 for local execution, 1 for edge execution, or 2 for cloud execution.
- 4.1 Inner Model: The state for subtask i includes the previous action and the workflow's task and data-flow information, then feeds a neural network to obtain action Q values.
- 4.1 Inner Model: The distributed architecture uses parallel DNN units, each pairing an updating network with a frozen target network to reduce learning correlation.
- 4.1 Inner Model: Actions from different DNNs are compared using a weighted delay-and-energy objective, and the action with the lowest value is selected.
- 4.1 Inner Model: Training stores state transitions in memory, periodically replays randomly sampled experiences, and updates network parameters using current and frozen networks.
5 PERFORMANCE EVALUATION
The evaluation examines DMRO’s convergence, weighted delay–energy cost, and adaptation across MEC environments. Results identify a learning rate, compare offloading schemes, and show benefits from meta-parameter initialization.
- 5 PERFORMANCE EVALUATION: The simulation uses four IoT users, five workflows per user, variable subtask sizes and computation demands, and a fully connected DNN.The DNN has one input layer, two hidden layers, and one output layer; α and β are both set to 1.
- 5.2 Convergence Performance: A learning rate of 0.01 gives the best convergence effect, while rates that are too low fail to converge.The learning-rate experiment plots training steps against neural-network loss.
- 5.2 Convergence Performance: Batch size has little effect on convergence, although larger batches reduce curve volatility and parameter freezing causes fluctuations every 200 steps.The target-network parameters are updated every 200 steps, and these fluctuations do not affect convergence.
- 5.3 Comparison Experiments: DMRO achieves the minimum total cost across the five offloading methods as the delay-to-energy weight ratio varies.DQN follows the same trend and outperforms local-only, edge-only, and cloud-only schemes; local execution becomes more costly as energy weight increases.
- 5.4 Fast Learning: Meta parameters converge faster initially and produce better total-cost decisions than traditional initialization parameters in the test environment.A low-cost offloading decision is obtained after a few training rounds, supporting adaptation to the new environment.
6 CONCLUSION
The DMRO framework combines distributed deep reinforcement learning with deep meta-learning to support task offloading in heterogeneous IoT-edge-cloud environments. It outperforms full offloading and conventional reinforcement-learning methods, while offering rapid adaptation to changing MEC environments, but is evaluated only with one edge and one cloud server.
- DMRO combines a distributed deep reinforcement-learning task-offloading model with a deep meta-learning initial-parameter model.The design targets task offloading in heterogeneous IoT-edge-cloud computing environments and improves neural-network portability.
- DMRO achieves better task-offloading decisions than full offloading and conventional reinforcement-learning-based methods.
- Meta parameters give DMRO stronger portability and rapid environment-learning ability, enabling quick convergence after MEC changes with few learning steps.The resulting solutions are described as low-cost offloading solutions.
- The evaluation focuses on one edge server and one cloud server, although the authors describe the model as scalable to multiple edge and cloud servers.