Distribution of Content On Demand Over the Allocation of Dynamic Microservices at the Edge and Core of the Network

Abstract


Video-on-Demand (VoD) content distribution is growing on popularity and will be the dominant application on the Internet. To manage traffic and provide QoE guarantees to VoD, microservices become an ideal model to explore the deployment of caching services at different levels of a Fog computing architecture.
The use of microservices for video content distribution is key to enable the dynamic allocation of resources on fog nodes according to client demands. This paper presents Fog4MS, a mechanism for dynamic allocation of VoD caching miroservices for Fog computing environments. The mechanism cosiders delay, microservice migration time, and utilization level of the fog node to determine the best location for caches considering a multi-layer fogcomputing architecture. Experiments in a simulated environment demonstrate the eficiency of the proposal in comparison of existing mechanisms when considering cost, time to migrate, fariness index, and QoE.

Keywords: Video on demand, Fog Computing, Microservices, Quality of Experience

References

Aazam, M. et al. (2016). MeFoRE: QoE based resource estimation at Fog to enhance QoS in IoT. In 23rd International Conference on Telecommunications (ICT), pages 1–5.

Aazam, M. and Huh, E.-N. (2015). Fog computing micro datacenter based dynamic resource estimation and pricing model for IoT. In 29th International Conference on Advanced Information Networking and Applications, pages 687–694.

Araújo, F., Rosário, D., Cerqueira, E., and Villas, L. A. (2019). A hybrid energy-aware video bitrate adaptation algorithm for mobile networks. In 15th Annual Conference on Wireless On-demand Network Systems and Services (WONS), pages 146–153. IEEE.

Bhardwaj, K. et al. (2019). Addressing the Fragmentation Problem in Distributed and Decentralized Edge Computing: A Vision. In IEEE International Conference on Cloud Engineering (IC2E), pages 156–167.

Chunlin, L. et al. (2019). Optimal media service selection scheme for mobile users in mobile cloud. Wireless Networks, 25(6):3179–3192.

Cisco (2019). Cisco visual networking index: Forecast and trends, 2017–2022. Technical report, Cisco.

He, Q. et al. (2017). Fog-based transcoding for crowdsourced video livecast. IEEE Communications Magazine, 55(4):28–33.

Jain, R. K., Chiu, D.-M. W., and Hawe, W. R. (1984). A quantitative measure of fairness and discrimination. Eastern Research Laboratory, Digital Equipment Corporation, Hudson, MA.

Jalali, F. et al. (2016). Fog computing may help to save energy in cloud computing. IEEE Journal on Selected Areas in Communications, 34(5):1728–1739.

Juluri, P., Tamarapalli, V., and Medhi, D. (2016). Measurement of Quality of Experience of Video-on-Demand Services: A Survey. IEEE Communications Surveys & Tutorials, 18(1):401–418.

Mao, Y. et al. (2017). A Survey on Mobile Edge Computing: The Communication Perspective. IEEE Communications Surveys Tutorials, 19(4):2322–2358.

Masip-Bruin, X. et al. (2016). Foggy Clouds and Cloudy Fogs: a Real Need for Coordinated Management of Fog-to-Cloud Computing Systems. IEEE Wireless Communications, 23(5):120–128.

Menchaca-Mendez, R. et al. (2018). Opportunistic Mobile Sensing in the Fog. Wireless Communications and Mobile Computing, 2018:1–18.

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.

Padhye, J. et al. (2000). Modeling tcp reno performance: a simple model and its empirical validation. IEEE/ACM transactions on Networking, 8(2):133–145.

Riley, G. F. and Henderson, T. R. (2010). The ns-3 network simulator. In Wehrle, K., Günes, M., and Gross, J., editors, Modeling and Tools for Network Simulation, pages 15–34. Springer.

Rosário, D. et al. (2018). Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support. Sensors, 18(2).

Salahuddin, M. A. et al. (2018). A survey on content placement algorithms for cloud-based content delivery networks. IEEE Access, 6:91–114.

Siavoshani, M. J., Pourmiri, A., and Shariatpanahi, S. P. (2017). Storage, communication, and load balancing trade-off in distributed cache networks. IEEE Transactions on Parallel and Distributed Systems, 29(4):943–957.

Tang, J., Tay, W. P., and Wen, Y. (2014). Dynamic request redirection and elastic service scaling in cloud-centric media networks. IEEE Transactions on Multimedia, 16(5):1434–1445.

Tian, Y. et al. (2018). A new live video streaming approach based on Amazon S3 pricing model. In IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), pages 321–328.

Wang, S. et al. (2015). Dynamic service migration in mobile edge-clouds. In IFIP Networking Conference (IFIP Networking), pages 1–9.

Xiao, W. et al. (2015). Dynamic request redirection and resource provisioning for cloud-based video services under heterogeneous environment. IEEE Transactions on Parallel and Distributed Systems, 27(7):1954–1967.

Zhang, Z.-H., Jiang, X.-F., and Xi, H.-S. (2016). Optimal content placement and request dispatching for cloud-based video distribution services. International Journal of Automation and Computing, 13(6):529–540.
Published
2020-12-07
DE ALENCAR, Derian Fernando Alves; ROSÁRIO, Denis Lima; CERQUEIRA, Eduardo Coelho; BOTH, Cristiano Bonato; ANTUNES, Rodolfo Stoffel. Distribution of Content On Demand Over the Allocation of Dynamic Microservices at the Edge and Core of the Network. In: BRAZILIAN SYMPOSIUM ON COMPUTER NETWORKS AND DISTRIBUTED SYSTEMS (SBRC), 38. , 2020, Rio de Janeiro. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 575-588. ISSN 2177-9384. DOI: https://doi.org/10.5753/sbrc.2020.12310.