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Deep Learning Based Service Composition in Integrated Aerial-Terrestrial Networks

Mohammad Farhoudi, Masoud Shokrnezhad, Somayeh Kianpisheh, Tarik Taleb

TL;DR

This work tackles service placement and composition in integrated aerial-terrestrial edge-cloud networks under mobility and non-deterministic ABS behavior. It introduces INNOVATION, a prediction-driven orchestration framework that combines D3QL-based ABS and request predictions with an isomorphic Hungarian matching approach to map service graphs onto the network graph. The method yields near-optimal cost and low end-to-end latency, outperforming baselines in terms of service admission, latency, and robustness across varying loads. The framework supports scalable, latency-aware orchestration for future 6G-enabled aerial-terrestrial deployments.

Abstract

The explosive growth of user devices and emerging applications is driving unprecedented traffic demands, accompanied by stringent Quality of Service (QoS) requirements. Addressing these challenges necessitates innovative service orchestration methods capable of seamless integration across the edge-cloud continuum. Terrestrial network-based service orchestration methods struggle to deliver timely responses to growing traffic demands or support users with poor or lack of access to terrestrial infrastructure. Exploiting both aerial and terrestrial resources in service composition increases coverage and facilitates the use of full computing and communication potentials. This paper proposes a service placement and composition mechanism for integrated aerial-terrestrial networks over the edge-cloud continuum while considering the dynamic nature of the network. The service function placement and service orchestration are modeled in an optimization framework. Considering the dynamicity, the Aerial Base Station (ABS) trajectory might not be deterministic, and their mobility pattern might not be known as assumed knowledge. Also, service requests can traverse through access nodes due to users' mobility. By incorporating predictive algorithms, including Deep Reinforcement Learning (DRL) approaches, the proposed method predicts ABS locations and service requests. Subsequently, a heuristic isomorphic graph matching approach is proposed to enable efficient, latency-aware service orchestration. Simulation results demonstrate the efficiency of the proposed prediction and service composition schemes in terms of accuracy, cost optimization, scalability, and responsiveness, ensuring timely and reliable service delivery under diverse network conditions.

Deep Learning Based Service Composition in Integrated Aerial-Terrestrial Networks

TL;DR

This work tackles service placement and composition in integrated aerial-terrestrial edge-cloud networks under mobility and non-deterministic ABS behavior. It introduces INNOVATION, a prediction-driven orchestration framework that combines D3QL-based ABS and request predictions with an isomorphic Hungarian matching approach to map service graphs onto the network graph. The method yields near-optimal cost and low end-to-end latency, outperforming baselines in terms of service admission, latency, and robustness across varying loads. The framework supports scalable, latency-aware orchestration for future 6G-enabled aerial-terrestrial deployments.

Abstract

The explosive growth of user devices and emerging applications is driving unprecedented traffic demands, accompanied by stringent Quality of Service (QoS) requirements. Addressing these challenges necessitates innovative service orchestration methods capable of seamless integration across the edge-cloud continuum. Terrestrial network-based service orchestration methods struggle to deliver timely responses to growing traffic demands or support users with poor or lack of access to terrestrial infrastructure. Exploiting both aerial and terrestrial resources in service composition increases coverage and facilitates the use of full computing and communication potentials. This paper proposes a service placement and composition mechanism for integrated aerial-terrestrial networks over the edge-cloud continuum while considering the dynamic nature of the network. The service function placement and service orchestration are modeled in an optimization framework. Considering the dynamicity, the Aerial Base Station (ABS) trajectory might not be deterministic, and their mobility pattern might not be known as assumed knowledge. Also, service requests can traverse through access nodes due to users' mobility. By incorporating predictive algorithms, including Deep Reinforcement Learning (DRL) approaches, the proposed method predicts ABS locations and service requests. Subsequently, a heuristic isomorphic graph matching approach is proposed to enable efficient, latency-aware service orchestration. Simulation results demonstrate the efficiency of the proposed prediction and service composition schemes in terms of accuracy, cost optimization, scalability, and responsiveness, ensuring timely and reliable service delivery under diverse network conditions.

Paper Structure

This paper contains 6 sections, 5 equations, 3 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Optimization Framework
  4. Proposed Method
  5. Simulation Results
  6. Conclusion

Figures (3)

  • Figure 1: A conceptual diagram illustrating the integration of aerial and terrestrial networks in the 6G edge-cloud continuum.
  • Figure 2: INNOVATION learning algorithm receives environment responses, stores them, and updates the evaluation network.
  • Figure 3: (1) cost per request, (2) E2E latency incurred per request, and (3) unsupported request numbers, while the number of requests is set to expand.