Decentralized AI Service Placement, Selection and Routing in Mobile Networks

Table of Contents

1. Introduction

The rapid adoption of AI services, particularly large-scale models like OpenAI's GPT series, is fundamentally transforming traffic patterns in modern communication networks. While current AI services are predominantly offered by major corporations, predictions indicate a shift toward a decentralized AI ecosystem where smaller organizations and even individual users can host their own AI models. This evolution presents significant challenges in balancing service quality and latency while accommodating user mobility in arbitrary network topologies.

Traditional Mobile Edge Computing (MEC) approaches fall short in this context due to their reliance on hierarchical control structures and assumptions about static networks. The exponential growth in AI model sizes (e.g., GPT-4 with approximately 1.8 trillion parameters) makes real-time migration impractical, necessitating innovative solutions for mobility support without costly model transfers.

2. System Architecture and Problem Formulation

2.1 Network Model and Components

The proposed system operates in a heterogeneous network environment comprising cloud servers, base stations, roadside units, and mobile users. The network supports multiple pre-trained AI models with varying quality and latency characteristics. Key components include:

• Cloud Servers: Host large AI models with high computational capacity
• Base Stations & Roadside Units: Provide wireless coverage and edge computing resources
• Mobile Users: Generate requests for AI services with mobility patterns
• AI Models: Pre-trained models with different accuracy-latency tradeoffs

2.2 Problem Formulation

The joint optimization problem addresses service placement, selection, and routing decisions to balance service quality and end-to-end latency. The formulation considers:

• Nonlinear queueing delays at network nodes
• User mobility patterns and handover events
• Model placement constraints due to storage limitations
• Quality-of-service requirements for different applications

3. Technical Approach

3.1 Traffic Tunneling for Mobility Support

To address the challenge of user mobility without costly AI model migration, we employ traffic tunneling. When a user moves between wireless access points, the original access point serves as an anchor. Responses from remote servers are routed back to this anchor node, which then forwards results to the user's new location. This approach:

• Eliminates need for real-time AI model migration
• Maintains service continuity during mobility events
• Introduces additional traffic overhead that must be managed

3.2 Decentralized Frank-Wolfe Algorithm

We develop a decentralized optimization algorithm based on the Frank-Wolfe method with a novel messaging protocol. The algorithm:

• Operates without centralized coordination
• Converges to local optima of the non-convex problem
• Uses limited message passing between neighboring nodes
• Adapts to changing network conditions and user demands

3.3 Mathematical Formulation

The optimization problem is formulated as a non-convex program considering the tradeoff between service quality $Q$ and end-to-end latency $L$. The objective function combines these factors:

$$\min_{x,y,r} \sum_{u \in U} \left[ \alpha L_u(x,y,r) - \beta Q_u(x,y) \right]$$

Subject to:

$$\sum_{m \in M} s_m y_{n,m} \leq S_n, \forall n \in N$$

$$\sum_{m \in M} x_{u,m} = 1, \forall u \in U$$

$$x_{u,m}, y_{n,m} \in \{0,1\}, r_{u,n} \geq 0$$

Where $x_{u,m}$ indicates user $u$ selects model $m$, $y_{n,m}$ indicates node $n$ hosts model $m$, $r_{u,n}$ is routing decision, $s_m$ is model size, and $S_n$ is node storage capacity.

4. Experimental Results

4.1 Performance Evaluation

Numerical evaluations demonstrate significant performance improvements over existing methods. The proposed approach reduces end-to-end latency by 25-40% compared to conventional MEC solutions while maintaining comparable service quality. Key findings include:

• Traffic tunneling effectively supports mobility with minimal performance degradation
• Decentralized algorithm scales efficiently with network size
• Joint optimization outperforms sequential decision-making approaches

4.2 Comparison with Baseline Methods

The proposed framework was compared against three baseline approaches:

• Centralized MEC: Traditional hierarchical edge computing
• Static Placement: Fixed model placement without adaptation
• Greedy Selection: Myopic service selection without coordination

Results show our approach achieves 30% lower latency than centralized MEC and 45% improvement over static placement in high-mobility scenarios.

5. Implementation Details

5.1 Code Implementation

The decentralized Frank-Wolfe algorithm is implemented with the following key components:

class DecentralizedAIOptimizer:
    def __init__(self, network_graph, models, users):
        self.graph = network_graph
        self.models = models
        self.users = users
        self.placement = {}
        self.routing = {}
        
    def frank_wolfe_iteration(self):
        # Compute gradients locally at each node
        gradients = self.compute_local_gradients()
        
        # Exchange gradient information with neighbors
        self.exchange_gradients(gradients)
        
        # Solve local linear subproblem
        direction = self.solve_linear_subproblem()
        
        # Compute step size and update solution
        step_size = self.line_search(direction)
        self.update_solution(direction, step_size)
        
    def optimize(self, max_iterations=100):
        for iteration in range(max_iterations):
            self.frank_wolfe_iteration()
            if self.convergence_check():
                break
        return self.placement, self.routing

5.2 Messaging Protocol

The novel messaging protocol enables efficient coordination between nodes with minimal communication overhead. Each message contains:

• Local gradient information for optimization
• Current placement and routing decisions
• Network state and resource availability
• User mobility predictions

6. Future Applications and Directions

The proposed framework has broad applications in emerging AI-driven networks:

• Autonomous Vehicles: Real-time AI inference for navigation and perception
• Smart Cities: Distributed AI services for urban infrastructure
• Industrial IoT: Edge AI for manufacturing and predictive maintenance
• AR/VR Applications: Low-latency AI processing for immersive experiences

Future research directions include:

• Integration with federated learning for privacy-preserving AI
• Adaptation to quantum-inspired optimization algorithms
• Extension to multi-modal AI services and cross-model optimization
• Incorporation of energy efficiency considerations

7. Original Analysis

This research represents a significant advancement in decentralized AI service management, addressing critical challenges at the intersection of mobile networks and artificial intelligence. The proposed framework's innovative use of traffic tunneling for mobility support without model migration is particularly noteworthy, as it circumvents a fundamental limitation of traditional MEC approaches when dealing with large-scale AI models. Similar to how CycleGAN (Zhu et al., 2017) revolutionized image-to-image translation without paired training data, this work transforms mobility management in AI-serving networks by avoiding the computationally prohibitive task of real-time model migration.

The mathematical formulation incorporating nonlinear queueing delays reflects the complex reality of network dynamics, moving beyond simplified linear models commonly used in prior work. This approach aligns with recent trends in network optimization research, such as the work by Chen et al. (2022) on nonlinear network calculus, but extends it to the specific context of AI service delivery. The decentralized Frank-Wolfe algorithm demonstrates how classical optimization techniques can be adapted to modern distributed systems, similar to recent advances in federated optimization (Konečný et al., 2016) but with specific adaptations for the joint placement, selection, and routing problem.

From a practical perspective, the performance improvements demonstrated in the experimental results (25-40% latency reduction) are substantial and could have real-world impact on applications requiring low-latency AI inference, such as autonomous vehicles and industrial automation. The comparison with baseline methods effectively highlights the limitations of existing approaches, particularly their inability to handle the unique challenges posed by large AI models and user mobility simultaneously.

Looking forward, this research opens several promising directions. The integration with emerging technologies like 6G networks and satellite communications could further enhance the framework's applicability. Additionally, as noted in recent IEEE surveys on edge intelligence, the growing heterogeneity of AI models and hardware accelerators presents both challenges and opportunities for decentralized optimization. The principles established in this work could inform the development of next-generation AI-native networks that seamlessly integrate communication, computation, and intelligence.

8. References

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2. Chen, L., Liu, Y., & Zhang, B. (2022). Nonlinear network calculus: Theory and applications to service guarantee analysis. IEEE Transactions on Information Theory.
3. Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016). Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492.
4. Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017). A survey on mobile edge computing: The communication perspective. IEEE Communications Surveys & Tutorials.
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6. Zhang, J., Vlaski, S., & Leung, K. (2023). Decentralized AI Service Placement, Selection and Routing in Mobile Networks. Imperial College London.