Dynamic allocation of microservices for virtual reality content delivery to provide quality of experience support in a fog computing architecture
Resumo
Virtual Reality (VR) content is gaining popularity, demanding solutions for its efficient distribution over the Internet. Microservices present an ideal model for deploying services at different levels of a Fog computing architecture for managing traffic and provide Quality of Experience (QoE) guarantees to VR content. However, it is crucial to efficiently find the fog node to allocate the microservices, which directly impact the QoE of VR services. This paper proposes INFORMER, an integer linear programming model aiming to optimize the placement of caching services according to delay, migration time, and resource utilization rate. Based on the insights from INFORMER, the paper presents Fog4VR, a mechanism for the dynamic allocation of content based on a heterogeneous microservice architecture. Results obtained with INFORMER serve as a baseline to evaluate Fog4VR on different scenarios using a simulation environment. Results demonstrate the efficiency of Fog4VR compared to existing mechanisms in terms of cost, migration time, fairness index, and QoE.
Referências
Alencar, D., Both, C., Antunes, R., Oliveira, H., Cerqueira, E., and Rosário, D. (2022). Dynamic microservice allocation for virtual reality distribution with qoe support. IEEE Transactions on Network and Service Management, 19(1):729–740.
Apostolopoulos, P. A., Tsiropoulou, E. E., and Papavassiliou, S. (2020). Risk-aware data offloading in multi-server multi-access edge computing environment. IEEE/ACM Transactions on Networking, 28(3):1405–1418.
de Alencar, D., Rosário, D., Cerqueira, E., Both, C., and Antunes, R. (2020). Distribuição de conteúdo sob demanda através da alocação de microserviços dinâmicos na borda e núcleo da rede. In Anais do XXXVIII SBRC, pages 575–588, Porto Alegre, RS, Brasil.
Lai, P. et al. (2020). QoE-aware user allocation in edge computing systems with dynamic QoS. Future Generation Computer Systems, 112:684–694.
Li, J. et al. (2018). Modeling QoE of Virtual Reality Video Transmission over Wireless Networks. In IEEE Global Communications Conference (GLOBECOM), pages 1–7.
Mahmud, R. et al. (2019). Quality of Experience (QoE)-aware placement of applications in Fog computing environments. JPDC, 132:190–203.
Mehrabi, A., Siekkinen, M., Kämäräinen, T., and yl Jski, A. (2021). Multi-tier cloudvr: Leveraging edge computing in remote rendered virtual reality. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 17(2):1–24.
Mishra, S. et al. (2020). Dynamic Resource Allocation in Fog-Cloud Hybrid Systems using Multi-criteria AHP Techniques. IEEE Internet of Things Journal.
Ni, L. et al. (2017). Resource allocation strategy in fog computing based on priced timed petrinets. IEEE Internet of Things Journal, 4(5):1216–1228.
Rigazzi, G. et al. (2019). An Edge and Fog Computing Platform for Effective Deployment of 360 Video Applications. In 2019 IEEE WCNCW, pages 1–6. IEEE.
Rosário, D. et al. (2018). Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support. Sensors, 18(2).
Santos, H., Alencar, D., Meneguette, R., Rosário, D., Nobre, J., Both, C., Cerqueira, E., and Braun, T. (2020). A multi-tier fog content orchestrator mechanism with quality of experience support. Computer Networks, 177:107288.
Tian, Y. et al. (2018). A new live video streaming approach based on Amazon S3 pricing model. In IEEE 8th CCWC, pages 321–328.
Yousefpour, A. et al. (2019). FogPlan: a lightweight QoS-aware dynamic fog service provisioning framework. IEEE Internet of Things Journal, 6(3):5080–5096.
