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Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learning
Jin Wang, Jia Hu, Geyong Min, Albert Y. Zomaya, Nektarios Georgalas
TL;DR
MEC task offloading must handle heterogeneous DAG-structured applications under dynamic environments, while existing approaches adapt inefficiently. MRLCO learns a meta offloading policy with a seq2seq network and achieves the lowest latency among three baselines within a small number of training steps.
Problem
MEC offloading requires efficient plans for heterogeneous DAG tasks in dynamic system states, but the resulting optimization problem is NP-hard and difficult to solve.
Method
MRLCO models diverse MEC environments as multiple MDPs, learns a shared meta-policy, and rapidly specializes it using local data with a seq2seq network.
Results
MRLCO achieves the lowest latency among three baseline algorithms within a small number of training steps across varied DAG topologies, task numbers, and transmission rates.
Takeaways & Limitations
MRLCO enables UEs to adapt offloading policies to new learning tasks with high sample efficiency, including under limited computation resources.
Takeaways & Limitations
The evaluation assumes stable wireless channels, reliable mobile devices, and sufficient computation resources; large-scale stragglers may impair synchronous meta-policy training.
Abstract
from arXiv · showhide
Multi-access edge computing (MEC) aims to extend cloud service to the network edge to reduce network traffic and service latency. A fundamental problem in MEC is how to efficiently offload heterogeneous tasks of mobile applications from user equipment (UE) to MEC hosts. Recently, many deep reinforcement learning (DRL) based methods have been proposed to learn offloading policies through interacting with the MEC environment that consists of UE, wireless channels, and MEC hosts. However, these methods have weak adaptability to new environments because they have low sample efficiency and need full retraining to learn updated policies for new environments. To overcome this weakness, we propose a task offloading method based on meta reinforcement learning, which can adapt fast to new environments with a small number of gradient updates and samples. We model mobile applications as Directed Acyclic Graphs (DAGs) and the offloading policy by a custom sequence-to-sequence (seq2seq) neural network. To efficiently train the seq2seq network, we propose a method that synergizes the first order approximation and clipped surrogate objective. The experimental results demonstrate that this new offloading method can reduce the latency by up to 25% compared to three baselines while being able to adapt fast to new environments.
1 INTRODUCTION
The paper addresses latency-minimizing offloading of dependent mobile-application tasks in MEC, where existing methods have limited adaptability and rely on heuristics or full retraining. It proposes MRLCO, an MRL-based, sequence-modeling approach designed for fast, sample-efficient adaptation across dynamic offloading scenarios.
- Problem: Dependent mobile-application tasks can be modeled as DAGs, making latency-minimizing offloading in MEC an NP-hard problem.Existing solutions rely heavily on expert knowledge or accurate environmental models.
- Motivation: DRL offers model-free learning for MEC offloading, but adapting policies to new environments remains challenging.DRL learns complex problems through trial and error without accurate environment models.
- Approach: MRLCO learns a meta offloading policy across user equipment and rapidly obtains user-specific policies from local data.Meta-policy training runs on MEC hosts, while specific-policy training runs on UEs.
- Approach: The method models dynamic offloading as multiple MDPs, separating meta-policy learning across MDPs from fast specific-policy learning for each MDP.This decomposition is intended to support fast adaptation and high sample efficiency for new learning tasks.
- Method: MRLCO converts offloading decisions into sequence prediction using a custom seq2seq policy network and stabilizes training by combining first-order approximation with a clipped surrogate objective.The DAG embedding represents task profiles and dependencies.
- Evaluation: MRLCO achieves the lowest latency within a small number of training steps against a fine-tuning DRL method, a greedy algorithm, and a HEFT-based heuristic.Experiments use synthetic DAGs generated from real-world applications across varied topologies, task numbers, and transmission rates.
2 BACKGROUND
The background frames MEC task offloading as an optimization problem for dependent mobile-application tasks and introduces RL and MRL as learning frameworks for adaptive policies. MRL targets rapid adaptation across related MDPs, while second-order MAML can be costly or intractable with complex seq2seq networks.
- MEC: MEC places computation and storage resources at the network edge to support intensive processing and alleviate backhaul-link burden.MEC extends cloud services toward the network edge.
- Task offloading: Task offloading partitions dependent application tasks between the UE and an MEC host to minimize total running cost.Mobile applications may contain multiple tasks with inner dependencies.
- Reinforcement learning: RL models learning tasks as MDPs and seeks policies that maximize accumulated reward through environment interaction.An MDP is defined by state and action spaces, transitions, initial-state distribution, rewards, and a discount factor.
- Meta reinforcement learning: MRL learns an algorithm that quickly finds effective policies for tasks drawn from a distribution, whose MDPs may differ in rewards or dynamics.The tasks typically share state and action spaces while differing in reward functions or transition and initial-state distributions.
- Gradient-based MRL: Gradient-based MRL learns initial policy parameters so one or a few policy-gradient updates on a new task yield an effective policy.This formulation follows model-agnostic meta-learning (MAML).
- Gradient-based MRL: Second-order MAML can impose huge training costs and become intractable with complex seq2seq architectures, motivating first-order approximations.The stated inefficiency arises from second-order derivatives during training.
3 PROBLEM FORMULATION
The problem formulation models application workflows as DAGs whose tasks are either executed locally on the UE or offloaded to an MEC host, with latency determined by task dependencies, resource availability, and system state. It seeks an offloading plan minimizing total DAG latency, but the resulting problem is NP-hard.
- Task and system model: MEC tasks are executed locally on the UE or offloaded to an MEC host through wireless transmission, remote processing, and result return.Face recognition is given as an application with dependent tasks such as tiler, detection, and feature mergence; MEC hosts run multiple VMs.
- Latency model: The latency of each execution step depends on task profiles and MEC system state, including required CPU cycles and wireless uplink and downlink rates.Local execution has only UE running latency, while offloading includes local processing, uplink, downlink, and remote processing components.
- DAG scheduling: A DAG scheduling plan A_1:n assigns each task an offloading decision, with tasks scheduled in sequence after all parent tasks precede their children.Scheduling tracks finish times and resource availability for the uplink channel, MEC host, downlink channel, and UE.
- Optimization objective: The objective is to find an effective DAG offloading plan that minimizes total latency across exit tasks.Task start and finish times are constrained by completed parent tasks and the availability of the relevant resource.
- Computational challenge: NP-hardness makes optimal offloading planning extremely challenging under highly dynamic DAG topologies and MEC system states.The paper introduces MRLCO in the next section to address this problem.
4 MRLCO: AN MRL-BASED COMPUTATION OFFLOADING SOLUTION
MRLCO integrates meta-reinforcement learning into MEC computation offloading through UE-level task adaptation and MEC-host meta-training. It models heterogeneous offloading as MDPs and uses a custom seq2seq policy network trained with PPO and first-order approximation.
- Architecture and training: MRLCO uses two training loops: task-specific inner-loop training on UEs and meta-policy outer-loop training on the MEC host.The UE downloads meta-policy parameters, adapts locally, uploads task-specific parameters, and the MEC host performs outer-loop training.
- MDP formulation: Computation offloading across varying MEC environments is formulated as a distribution of learning tasks, each represented by an MDP.Each task is defined as T_i = (S, A, P, P0, R, γ).
- MDP formulation: The state combines an encoded DAG with the partial offloading plan, capturing task profiles, DAG structure, wireless rate, and evolving MEC resources.Tasks are ordered by descending rank and embedded using task information plus immediate parent and child indices.
- Policy network: MRLCO represents the sequential offloading policy with a custom RNN-based seq2seq network whose encoder processes task embeddings and decoder outputs task decisions.The network’s shared encoder and decoder also support policy and value-function approximation by extracting common DAG features.
- MRLCO optimization: The inner-loop objective uses PPO instead of VPG for better exploration and training stability, while first-order approximation avoids costly gradients of gradients.The approximation addresses computation and implementation difficulties when optimizing a complex seq2seq network.
5 PERFORMANCE EVALUATION
The evaluation tests MRLCO across changing DAG topologies, task numbers, and transmission rates against fine-tuning DRL, HEFT-based, and Greedy baselines. Results show that meta-trained policies adapt rapidly and outperform heuristic methods after limited updates, with MRLCO outperforming all heuristic baselines after 20 updates.
- Experimental design: MRLCO is evaluated in three dynamic scenarios: varying application topologies, task numbers, and transmission rates.Each task uses 5–50 KB of data and requires 10^7–10^8 CPU cycles.
- Baselines: The evaluation compares MRLCO with fine-tuning DRL, HEFT-based, and Greedy algorithms.Fine-tuning DRL initializes task-specific policies from a pretrained network; HEFT-based prioritizes tasks and schedules them by earliest estimated finish time, while Greedy assigns tasks by estimated finish time.
- Different DAG topologies: MRLCO adapts to new tasks more quickly than fine-tuning DRL in the different-topology experiment.With 0 gradient updates, MRLCO and fine-tuning DRL already outperform HEFT-based and Greedy because both start from pretrained models rather than learning from scratch.
- Different task numbers: In the task-number experiment, MRLCO and fine-tuning DRL outperform HEFT-based after a few gradient updates, while remaining better than Greedy from step 0.Training task numbers are n∈{10, 15, 25, 35, 45, 50}, with testing task numbers n∈{20, 30, 40}.
- Different transmission rates: After 20 gradient update steps, MRLCO outperforms all heuristic baseline algorithms on testing datasets with different transmission rates.The experiment treats each transmission rate as an individual learning task and includes an Optimal algorithm found by exhaustive search.
6 RELATED WORK
Related MEC offloading research generally uses binary or partial task models, while recent work increasingly applies DRL to adapt offloading strategies in dynamic scenarios. Existing studies include deep Q-learning, PPO with convolutional neural networks, and joint offloading-resource allocation methods.
- Task models: Related work generally distinguishes binary offloading, with no inner task dependencies, from partial offloading.These are identified as the two task models used in related research.
- Deep reinforcement learning: DRL has been widely applied to MEC task offloading to adapt strategies for dynamic scenarios.The cited approaches include deep Q-learning and other deep reinforcement learning methods.
- Deep reinforcement learning: Deep Q-learning has been used for multi-user multi-edge-node offloading and ultra-dense networks with multiple selectable base stations.These applications are attributed to Dinh et al. and Chen et al., respectively.
- Deep reinforcement learning: Other studies combine PPO with convolutional neural networks, account for resource and mobility constraints, or jointly optimize task offloading and resource allocation.The related methods address efficient offloading, limited resources, vehicle mobility, delay, and computation-rate objectives.
7 DISCUSSION
The discussion highlights MRLCO’s fast adaptation and sample efficiency over existing RL-based offloading methods, while noting broader MEC applicability and challenges under large-scale, unreliable conditions.
- Advantages: MRLCO learns to fast adapt in dynamic environments and offers high sample efficiency compared with existing RL-based task offloading methods.The framework is presented as having multiple advantages over existing reinforcement-learning approaches.
- Broader applicability: Beyond MEC task offloading, MRLCO could address other MEC decision-making problems, including content caching for QoS and reduced network traffic.Content caching stores popular content at MEC hosts to improve mobile-user QoS and reduce network traffic.
- Challenges: Large-scale operation may be challenged when UEs become stragglers because broken network connections or insufficient power can cause dropouts.The paper assumes stable wireless channels, reliable mobile devices, and sufficient computation resources; the cited passage then identifies dropout risks at scale.
8 CONCLUSION
The paper proposes MRLCO, a meta-reinforcement-learning approach for computation offloading in MEC that quickly adapts to new environments with few gradient updates and samples. It models applications as DAGs, formulates offloading as sequence prediction, and represents policies with a seq2seq network while reducing MRL training cost through first-order approximation.
- MRLCO applies meta reinforcement learning to the computation offloading problem in MEC.
- MRLCO quickly adapts to new MEC environments using a small number of gradient updates and samples.
- Target mobile applications are modeled as DAGs, and computation offloading is converted into a sequence prediction process.
- A seq2seq neural network represents the offloading policy, while first-order approximation reduces the MRL objective’s training cost.