Dynamic Orchestration of Service Function Chaining for Multi-User Augmented Reality
Abstract
Multi-User Augmented Reality (MUAR) employs edge computing for collaborative 3D interactions on mobile devices. This service can be segmented into Service Function Chainings (SFCs) across edge servers to enable parallel user execution. We propose the Multi-Criteria and Mobility-Aware Service Function Chaining Orchestration (MMASFCO), which minimizes latencies and optimizes network resources. Results indicate that MMASFCO enhances session acceptance, resource efficiency, and latency reduction compared to existing methods.References
Akhtar et al. (2021). Managing chains of application functions over multi-technology edge networks. IEEE Trans. on Network and Service Management.
Andrew, S., Bos, H., et al. (2024). Sistemas operacionais modernos. Bookman Editora.
Huang et al (2021). Proactive edge cloud optimization for mobile augmented reality applications. In IEEE Wireless Communications and Networking Conference (WCNC). IEEE.
L. Wang et al. (2021). Change: Delay-aware service function chain orchestration at the edge. In IEEE International Conference on Fog and Edge Computing (ICFEC). IEEE.
Lin, I.-C., Yeh, Y.-H., and Lin, K. C.-J. (2021). Toward optimal partial parallelization for service function chaining. IEEE/ACM Transactions on Networking, 29(5):2033–2044.
Liu et al. (2018). An edge network orchestrator for mobile augmented reality. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications. IEEE.
Medeiros, A., Di Maio, A., Braun, T., and Neto, A. (2022). Service chaining graph: Latency-and energy-aware mobile vr deployment over mec infrastructures. In GLOBECOM 2022-2022 IEEE Global Communications Conference, pages 6133–6138. IEEE.
Ngo, M. et al. (2020). Coordinated container migration and base station handover in mobile edge computing. In IEEE Global Communications Conference, pages 1–6.
Perronnin et al. (2010). Large-scale image retrieval with compressed fisher vectors. In IEEE computer society conference on computer vision and pattern recognition. IEEE.
Santos, H., Martins, B., Rosário, D., Cerqueira, E., and Braun, T. (2023). Mobility-aware service function chaining orchestration for multi-user augmented reality. In 2023 IEEE 48th Conference on Local Computer Networks (LCN), pages 1–9. IEEE.
Santos, H., Rosario, D., Cerqueira, E., and Braun, T. (2022). Multi-criteria service function chaining orchestration for multi-user virtual reality services. In GLOBECOM 2022-2022 IEEE Global Communications Conference, pages 6360–6365. IEEE.
Santos, J. et al. (2021). Efficient orchestration of service chains in fog computing for immersive media. In 17th International Conference on Network and Service Management (CNSM), pages 139–145. IEEE.
T. Wang et al. (2020). Adaptive service function chain scheduling in mobile edge computing via deep reinforcement learning. IEEE Access.
Andrew, S., Bos, H., et al. (2024). Sistemas operacionais modernos. Bookman Editora.
Huang et al (2021). Proactive edge cloud optimization for mobile augmented reality applications. In IEEE Wireless Communications and Networking Conference (WCNC). IEEE.
L. Wang et al. (2021). Change: Delay-aware service function chain orchestration at the edge. In IEEE International Conference on Fog and Edge Computing (ICFEC). IEEE.
Lin, I.-C., Yeh, Y.-H., and Lin, K. C.-J. (2021). Toward optimal partial parallelization for service function chaining. IEEE/ACM Transactions on Networking, 29(5):2033–2044.
Liu et al. (2018). An edge network orchestrator for mobile augmented reality. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications. IEEE.
Medeiros, A., Di Maio, A., Braun, T., and Neto, A. (2022). Service chaining graph: Latency-and energy-aware mobile vr deployment over mec infrastructures. In GLOBECOM 2022-2022 IEEE Global Communications Conference, pages 6133–6138. IEEE.
Ngo, M. et al. (2020). Coordinated container migration and base station handover in mobile edge computing. In IEEE Global Communications Conference, pages 1–6.
Perronnin et al. (2010). Large-scale image retrieval with compressed fisher vectors. In IEEE computer society conference on computer vision and pattern recognition. IEEE.
Santos, H., Martins, B., Rosário, D., Cerqueira, E., and Braun, T. (2023). Mobility-aware service function chaining orchestration for multi-user augmented reality. In 2023 IEEE 48th Conference on Local Computer Networks (LCN), pages 1–9. IEEE.
Santos, H., Rosario, D., Cerqueira, E., and Braun, T. (2022). Multi-criteria service function chaining orchestration for multi-user virtual reality services. In GLOBECOM 2022-2022 IEEE Global Communications Conference, pages 6360–6365. IEEE.
Santos, J. et al. (2021). Efficient orchestration of service chains in fog computing for immersive media. In 17th International Conference on Network and Service Management (CNSM), pages 139–145. IEEE.
T. Wang et al. (2020). Adaptive service function chain scheduling in mobile edge computing via deep reinforcement learning. IEEE Access.
Published
2024-05-20
How to Cite
FLEXA, Rodrigo; SANTOS, Hugo; CERQUEIRA, Eduardo; ROSÁRIO, Denis.
Dynamic Orchestration of Service Function Chaining for Multi-User Augmented Reality. In: WORKSHOP ON SCIENTIFIC INITIATION AND GRADUATION - BRAZILIAN SYMPOSIUM ON COMPUTER NETWORKS AND DISTRIBUTED SYSTEMS (SBRC), 42. , 2024, Niterói/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 233-240.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc_estendido.2024.3388.
