Joint Service Migration and Resource Allocation for DNN tasks using SA-DDQN-DDPG in Vehicular Edge Computing
Unlocking Real-time AI for Autonomous Vehicles
Deep Neural Network (DNN) tasks in Vehicular Edge Computing (VEC) face significant performance challenges due to high vehicle mobility, leading to increased backhaul delays and degraded Quality of Experience (QoE). The computational intensity and intricate inter-layer dependencies of DNNs exacerbate these issues, making efficient service migration and resource allocation critical yet complex.
We introduce SA-DDQN-DDPG, a novel deep reinforcement learning algorithm designed for joint service migration and resource allocation. This algorithm integrates a self-attention mechanism to enhance state representation and utilizes a hybrid Double Deep Q-Network (DDQN) for discrete migration decisions and Deep Deterministic Policy Gradient (DDPG) for continuous resource allocation.
The SA-DDQN-DDPG algorithm significantly minimizes average task completion delays and migration latencies. Experimental results show up to a 40.41% reduction in average migration latency compared to always-migrate strategies, and 14.50% compared to DDQN. It demonstrates faster convergence (around 300 iterations), higher final rewards, and superior adaptability across varying VEC conditions, thereby enhancing the overall QoE for real-time vehicular intelligence applications.
Executive Impact: Transforming VEC Performance
Our analysis reveals the SA-DDQN-DDPG algorithm's profound impact on key operational metrics within Vehicular Edge Computing:
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Migration Latency Reduction (vs. Always Migrate)
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Migration Latency Reduction (vs. DDQN)
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Iterations to Convergence
Deep Analysis & Enterprise Applications
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Core Algorithm: SA-DDQN-DDPG Workflow
The SA-DDQN-DDPG algorithm is a hybrid DRL approach for optimal service migration and resource allocation. It combines a self-attention mechanism to process dynamic VEC states, DDQN for discrete migration actions (migrate or not), and DDPG for continuous resource allocation. This structure enables intelligent decision-making by considering both latency and transmission overhead, enhancing system performance and adaptability.
Enterprise Process Flow
VEC Environment State Input (Self-Attention)
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DDQN Branch: Discrete Migration Action (0/1)
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DDPG Branch: Continuous Resource Allocation
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Joint Optimization for Minimized Delay
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Update Networks & Repeat
Performance Against Baselines
The proposed SA-DDQN-DDPG algorithm consistently outperforms baseline methods (Always Migrate, DQL, DDPG, SAC) across various metrics and VEC conditions. This includes lower average task completion delays, reduced migration latencies, and higher task completion rates, demonstrating its robustness and efficiency in dynamic scenarios.
| Metric | SA-DDQN-DDPG (Proposed) | DDQN-DDPG | DDPG | DQL | Always Migrate |
|---|---|---|---|---|---|
| Avg. Task Completion Delay Reduction (vs. Proposed) | Baseline | 14.50% lower | 20.7% lower | 29.62-37.17% lower | 28.33-40.41% lower |
| Avg. Migration Delay Reduction (vs. Proposed) | Baseline | Varies (e.g., 10.47-15.81% lower) | Varies (e.g., 15.34-19.69% lower) | Varies (e.g., 15.34-38.99% lower) | Varies (e.g., 38.06-72.74% lower) |
| Convergence Speed | Fastest (~300 Iterations) | Slower (~500 Iterations) | Slower (~450 Iterations) | Slower, less stable | N/A (Rule-based) |
| Hybrid Action Space Handling | Yes (Discrete migration, Continuous resource) | Yes (Hybrid, but lacks self-attention) | Continuous only | Discrete only | N/A |
Key Parameters & Their Impact
The algorithm's performance is sensitive to key hyperparameters and VEC environment variables. Optimal learning rates (η1=0.01 for DDQN, η2=0.0001/η3=0.0002 for DDPG) and a discount factor (λ=0.90) are crucial for rapid convergence and high rewards. Vehicle numbers, link bandwidth, vehicle speed, and edge server computing capacity also significantly influence task completion and migration delays.
λ=0.90
Optimal Discount Factor for Highest Reward
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Your AI Implementation Timeline
A phased approach ensures successful integration and maximum impact for your enterprise.
Phase 1: Problem Identification & Scope Definition
We begin by thoroughly understanding your current challenges in VEC with DNN tasks, including backhaul delays, QoE issues, and computational demands, to precisely scope the AI solution.
Phase 2: SA-DDQN-DDPG Algorithm Development & Customization
Our team will develop and customize the SA-DDQN-DDPG algorithm, tailoring its self-attention mechanism, DDQN for migration, and DDPG for resource allocation to your specific VEC environment and DNN task requirements.
Phase 3: Experimental Validation & Performance Benchmarking
We rigorously test the customized algorithm against your current systems and established baselines, demonstrating the quantifiable reductions in latency and improvements in task completion rates and QoE.
Phase 4: Integration into Enterprise VEC
Seamless integration of the SA-DDQN-DDPG solution into your existing vehicular edge computing infrastructure, ensuring real-time performance for autonomous driving and other intelligence applications.
Phase 5: Future Enhancements & Scalability
Continuous monitoring, optimization, and scaling of the solution to address evolving demands, ensuring robust and efficient performance in increasingly large-scale, heterogeneous, and dynamic vehicular networks.
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