Real-Time Service Migration in Edge Networks: A Survey
Abstract
1. Introduction
1.1. From Cloud to Edge: The Evolutionary Foundation for Service Migration
1.2. Service Migration in Edge Networks
1.2.1. What Is Service Migration in Edge Networks?
- Service Resources: These are the computing, storage, and networking assets distributed across the edge network that support service execution and migration. Unlike static allocation, service migration enables the temporal reallocation of these resources to optimize utilization and responsiveness. Typical resources include CPU/GPU cycles, memory, bandwidth, and cache, which are often subject to spatial and temporal constraints [12,13].
- Service Instances: These refer to the actual running units of services (e.g., containers, virtual machines (VMs), microservices) deployed on edge devices or servers. During migration, these instances are transferred—either via live migration, checkpoint restart, or state rehydration—to new nodes without disrupting service continuity [14,15].
- Participants: Multiple entities collaborate in service migration, including mobile end devices (e.g., smartphones, sensors, vehicles), edge servers (e.g., base stations, fog nodes), and cloud platforms. Coordination models may follow device–edge, edge–edge, or edge–cloud topologies. Effective migration depends on synchronization and negotiation among these participants [16,17].
- Objectives: Service migration aims to address several optimization goals, including minimizing response latency, avoiding overloaded nodes, maintaining service availability under user mobility, reducing energy consumption, and enhancing overall QoS. In latency-critical or mission-critical applications such as autonomous driving or remote healthcare, timely migration is key to meeting service-level agreements (SLAs) [18,19].
- Actions: The core operations involved in service migration include monitoring node loads and user positions, evaluating migration triggers (e.g., SLA violations, mobility prediction, energy thresholds), transferring the service state, and reinitializing execution at the target node. This also involves handling dependencies, preserving data integrity, and updating routing or session states [8,20].
- Methodologies: Various methodologies have been developed to enable intelligent and efficient service migration. These include heuristic and metaheuristic optimization, deep reinforcement learning for adaptive decision-making, and container-based orchestration technologies such as Kubernetes or KubeEdge. Moreover, distributed cooperative mechanisms, often supported by blockchain or federated learning, are used to ensure scalability and decentralization in multi-domain edge environments [19,21].
1.2.2. Why Is Service Migration Necessary in Edge Computing Environments?
- Users: Edge networks connect billions of geographically distributed devices—including stationary sensors, mobile phones, autonomous vehicles, and drones—each with diverse and evolving QoS needs. For example, autonomous driving scenarios require ultra-low-latency access to decision-making services, while battery-powered IoT devices prioritize energy-efficient offloading and minimal data transmission. As users move across network boundaries, static placements can lead to service delays or disruptions. Real-time service migration supports location-aware, demand-responsive relocation of services, ensuring seamless and low-latency experiences even under dynamic mobility patterns [22,23].
- Service Providers: The edge ecosystem includes various commercial stakeholders such as network operators, infrastructure providers, and third-party application vendors. These providers should balance performance, cost, and resource constraints while delivering high service quality. Static placement of services often leads to unbalanced loads, underutilized resources, or SLA violations in hotspots. Real-time service migration enables dynamic reassignment of services based on current traffic, resource availability, and user distribution. This enhances efficiency, reduces energy costs, and aligns with revenue-driven strategies such as demand-aware scaling and pricing differentiation [24,25]. In today’s MEC settings, AI-driven and cooperative migration frameworks have demonstrated success in reducing latency and increasing operational gains.
- Edge Network Infrastructure: Edge computing infrastructure is inherently decentralized and heterogeneous, often consisting of micro-data centers, access points, vehicular nodes, and even end devices with opportunistic computing capabilities. These resources exhibit varying computational power, energy availability, and connectivity quality. Many edge resources—such as parked autonomous vehicles or idle roadside units (RSUs)—remain underutilized unless they are actively integrated into the service ecosystem. Real-time service migration serves as the orchestrator, redistributing workloads across these fragmented units based on real-time availability. Through migration-aware scheduling, edge systems can proactively or reactively bypass overloaded nodes and utilize transient capacity, improving both efficiency and resilience across the network [26,27].
1.3. Contribution and Organization
- Architecture, Basic Model, Benchmark Datasets, and Open Platforms (Section 2): We present four representative edge computing architectures, i.e., cloud–edge–end, edge–edge, cloud–edge fusion, and edge–device collaboration, and analyze their respective support for real-time service migration. We also introduce four analytical models (network model, latency model, energy consumption model, and utility model) that collectively provide a theoretical foundation for service migration decision-making and performance evaluation.
- Migration Motivation (Section 3): We identify and elaborate on four primary motivations for real-time service migration in edge environments: (i) overload mitigation and resource rebalancing; (ii) user mobility and location awareness; (iii) energy efficiency and device lifespan management; and (iv) latency optimization and QoS enhancement. These motivations reflect the practical challenges faced by edge systems and drive the design of adaptive migration strategies.
- Key Techniques for Service Migration (Section 4): We categorize and examine six mainstream technical approaches for enabling real-time service migration: (i) approximate algorithms, (ii) heuristic algorithms, (iii) game-theoretic models, (iv) reinforcement learning, (v) deep learning, and (vi) deep reinforcement learning. We analyze how each technique contributes to efficient migration decisions under constraints such as delay, energy, load imbalance, and mobility uncertainty.
- Service Migration Application Scenarios (Section 5): We explore the deployment and effectiveness of real-time service migration in four key application domains: smart cities, smart homes, smart manufacturing, and smart healthcare. These scenarios demonstrate how timely and adaptive migration supports system scalability, responsiveness, and contextual awareness in real-world edge environments.
- Challenges and Future Directions (Section 6): We highlight critical open issues: inaccurate or delayed migration decisions that limit service awareness; scheduling and coordination challenges in large-scale heterogeneous edge networks; lack of comprehensive security and privacy protection during service migration; lack of adaptive and context-aware autonomous migration mechanisms; challenges and opportunities of AI-driven service migration; migration in the age of 6G: ultra-dense and high-mobility networks; and sustainable and energy-aware service migration.
- The strategy must support real-time or low-latency requirements, which are fundamental for delay-sensitive edge applications. Given our focus on real-time migration, this serves as the primary inclusion criterion.
- The strategy must be applicable to edge computing environments, such as MEC, IoT, and other distributed edge infrastructures, where computational offloading and proximity-aware scheduling are essential.
- The strategy should be compatible with the system models introduced in Section 2. This ensures comparability and analytical consistency across reviewed methods.
- The strategy must address practical challenges such as overload mitigation, user mobility support, energy efficiency, and QoS enhancement—all of which are elaborated in Section 3.
- The strategy should adopt one of the key technical approaches reviewed in Section 4, such as heuristic optimization, approximation methods, game-theoretic models, or reinforcement learning. We selected representative works from each category to reflect methodological diversity.
- The strategy should align with the architectural patterns outlined in Section 2.1. Only strategies where migration decisions and executions are primarily performed at the edge layer are considered, excluding those solely based on centralized cloud-side control.
2. Architecture, Basic Model, Benchmark Datasets, and Open Platforms
2.1. Network Architectures
2.1.1. Cloud–Edge–End Collaborative Architecture
2.1.2. Edge–Edge Collaborative Architecture
2.1.3. Cloud–Edge Fusion Architecture
2.1.4. Edge–Device Collaborative Architecture
2.1.5. Summary
- Cloud–Edge–End: Global orchestration and stable vertical migration.
- Edge–Edge: High-frequency, localized lateral migration for dynamic contexts.
- Cloud–Edge Fusion: Fine-grained hybrid service placement and joint orchestration.
- Edge–Device: Maximizes responsiveness and privacy via terminal computation.
2.2. Basic Model
2.2.1. Network Model
- : Data volume required for processing or transmission.
- : Computational demand (e.g., CPU cycles).
- : Latency constraint or deadline for subtask execution.
- Resource Heterogeneity: Edge nodes vary in computing power, storage, and connectivity.
- Mobility and Dynamism: Task execution environments may change due to user mobility or fluctuating load.
- Dependency-Aware Scheduling: Inter-task communication delays must be considered in dependent subtasks.
2.2.2. Latency Model
2.2.3. Energy Consumption Model
2.2.4. Utility Model
- : An aggregate score based on service availability, reliability, and responsiveness.
- : Ratio of completed to requested tasks.
- : Average task latency.
- : Total energy consumption.
- and : Scenario-specific weights.
- Pareto-Based Multi-Objective Optimization: Used when no single solution dominates all objectives (e.g., NSGA-II, multi-objective evolutionary algorithms (MOEAs)).
- RL: Agents learn optimal migration/scheduling policies by maximizing long-term utility over dynamic environments.
- Heuristic/Metaheuristic Search: Methods like genetic algorithms, simulated annealing, or ant colony optimization are used to explore the utility landscape efficiently.
2.2.5. Trade-Offs Between Latency Guarantees and Other System Metrics
2.3. Benchmark Datasets and Open Platforms
3. Migration Motivation
3.1. Overload Mitigation and Resource Rebalancing
3.2. User Mobility and Location Awareness
3.3. Energy Efficiency and Device Lifespan Management
3.4. Latency Optimization and QoS Enhancement
4. Key Techniques for Service Migration
4.1. Approximate Algorithms
4.2. Heuristic Algorithms
4.3. Game Theory
4.4. Reinforcement Learning
- Online Adaptation: RL dynamically adjusts migration decisions in response to real-time feedback, accommodating non-deterministic workloads and mobility patterns.
- Policy Generalization: Once trained, the learned policy can be reused or fine-tuned across similar edge environments, reducing retraining costs.
- QoS Awareness: RL inherently supports multi-objective optimization, enabling fine-grained control over delays, energy, and resource utilization.
4.5. Deep Learning
4.6. Deep Reinforcement Learning
5. Service Migration Application Scenarios
5.1. Smart Cities
5.2. Smart Homes
5.3. Smart Manufacturing
5.4. Smart Healthcare
6. Challenges and Future Directions
6.1. Inaccurate or Delayed Migration Decisions Limit Service Awareness
6.2. Scheduling and Coordination Challenges in Large-Scale Heterogeneous Edge Networks
6.3. Lack of Comprehensive Security and Privacy Protection During Service Migration
6.4. Lack of Adaptive and Context-Aware Autonomous Migration Mechanisms
6.5. Challenges and Opportunities of AI-Driven Service Migration
6.6. Migration in the Age of 6G: Ultra-Dense and High-Mobility Networks
6.7. Toward Sustainable and Energy-Aware Service Migration
6.8. Comparison and Discussion of Real-Time Service Migration Approaches
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Notation | Description |
---|---|
The i-th subtask of a service request | |
Data volume to be processed by subtask | |
Computational demand of , typically in CPU cycles | |
Maximum response time constraint for subtask | |
Binary decision variable: 1 if is assigned to node and 0 otherwise | |
Computing capacity of node , typically CPU processing speed | |
Bandwidth of node | |
Computation delay of on node , | |
Communication delay of between nodes, | |
Total delay of , | |
Total delay of all tasks, | |
Average delay of all tasks | |
Computation energy of , | |
Communication energy of , | |
Total energy of , | |
Total energy of all tasks, | |
Energy coefficient based on hardware characteristics | |
Power consumption for data transmission per unit time | |
Weights for components in the utility or QoS functions | |
Weight for energy consumption in the utility function | |
Weights for components in the quality of experience (QoE) function | |
Availability of task | |
Reliability of task | |
Stability score of task | |
System responsiveness to the user request of task | |
Task success rate, | |
Number of successfully completed tasks | |
Total number of tasks requested | |
Resources used on node j | |
Total available resources on node j | |
Resource utilization of node j, | |
Average resource utilization across all nodes | |
m | Total number of edge nodes in the system |
U | Utility function value representing overall system performance |
Paper | Key Technique | Advantages | Disadvantages |
---|---|---|---|
[55] | Predictive trajectory-aware migration; PSO algorithm | Simultaneous reduction in delay, energy consumption, and load fluctuation; fault-tolerant replication for improved service reliability | Assumes accurate trajectory prediction, which may not hold in highly dynamic environments; PSO-based model increases computational complexity |
[35] | DDPG for joint optimization | Learns optimal migration and mobility policies jointly; considers dynamic network states; improves latency and service continuity | Requires large training data and time; model interpretability is low; not optimized for energy consumption or load balance |
[122] | Energy-aware service migration based on DQN | Enables dynamic migration across base stations; balances energy efficiency with delay | Assumes stable wireless link during migration; performance sensitive to DQN training quality |
[60] | Transfer RL with communication and computation-aware reward function | Reduces migration cost via knowledge transfer from similar tasks; adapts to dynamic environments; jointly considers bandwidth and processing load | Requires pretrained source domain model; performance degrades if task similarity across domains is low |
[49] | Asynchronous FL with mobility-aware caching | Reduces model aggregation delay; improves content hit ratio and personalization | Assumes accurate mobility prediction; training stability can be affected by stragglers and delayed updates |
[59] | Predictive service placement using Lyapunov optimization | Achieves low latency through multi-timescale planning; considers both delay and reliability | Requires accurate mobility prediction; model complexity increases with prediction window size |
[37] | Time-segmented multi-level reconfiguration using cloud–edge collaboration and edge clustering | Adapts to fluctuating loads via hierarchical edge–cloud scheduling; minimizes load shedding under bursty traffic | Relies on predefined time segmentation granularity; scalability limited by edge clustering overhead |
[123] | Hierarchical cloud–edge–end collaborative inference with model partitioning, pruning, and early exiting | Reduces end-to-end latency via adaptive offloading and early exit; maintains robustness under dynamic network/device conditions | Requires accurate confidence estimation for early exit; system orchestration complexity increases with scale |
[57] | Collaborative and latency-aware microservice migration based on delay-aware cost graph optimization and DQN learning | Reduces end-to-end latency via joint service chain optimization; improves responsiveness under dynamic workloads; enhances adaptability through RL-based policy refinement | High graph construction and optimization overhead; sensitive to profiling accuracy; scalability challenged in large service meshes |
[124] | Context-aware neural configuration adaptation using latency-accuracy profiling and lightweight scheduling | Enables adaptive configuration migration with high success rate; balances inference latency and accuracy under mobility | Requires offline profiling for configuration library; targeted for video analytics and less generalizable to other domains |
[125] | Two-stage mobility-aware edge server placement based on NSGA-II offline optimization and game-theoretic online control | Balances long-term server deployment efficiency with short-term load changes; achieves low-latency query resolution under mobility; supports incremental remapping | Only optimizes placement and not runtime migration; relies on historical mobility data and tuned parameters |
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Zhang, Y.; Zhao, K.; Yang, Y.; Zhou, Z. Real-Time Service Migration in Edge Networks: A Survey. J. Sens. Actuator Netw. 2025, 14, 79. https://doi.org/10.3390/jsan14040079
Zhang Y, Zhao K, Yang Y, Zhou Z. Real-Time Service Migration in Edge Networks: A Survey. Journal of Sensor and Actuator Networks. 2025; 14(4):79. https://doi.org/10.3390/jsan14040079
Chicago/Turabian StyleZhang, Yutong, Ke Zhao, Yihong Yang, and Zhangbing Zhou. 2025. "Real-Time Service Migration in Edge Networks: A Survey" Journal of Sensor and Actuator Networks 14, no. 4: 79. https://doi.org/10.3390/jsan14040079
APA StyleZhang, Y., Zhao, K., Yang, Y., & Zhou, Z. (2025). Real-Time Service Migration in Edge Networks: A Survey. Journal of Sensor and Actuator Networks, 14(4), 79. https://doi.org/10.3390/jsan14040079