Design and optimization of Content Distribution Networks
https://doi.org/10.15514/ISPRAS-2019-31(2)-1
Abstract
This article presents the application of soft computing methods for solving the problem of designing and optimizing cloud-based Content Distribution Networks (CDN). A multi-objective approach is applied to solve the resource provisioning problem for building the infrastructure for the network, considering the objectives of minimizing the cost of the virtual machines, network, and storage, and the maximization of the quality-of-service provided to end-users. A specific brokering model is proposed to allow a single cloud-based CDN to be able to host multiple content providers applying a resource sharing strategy. Following the proposed brokering model, three multiobjective evolutionary approaches are studied for the offline optimization of resource provisioning and a greedy heuristic method is proposed for addressing the online routing of contents. The experimental evaluation of the proposed approach is performed over a set of realistic problem instances. The obtained experimental results indicate that the proposed approach is effective for designing and optimizing cloud-based Content Distribution Networks: total costs are reduced by up to 10.34% while maintaining high quality-of-service values.
About the Authors
Santiago Damian Iturriaga FabraUruguay
Full-time researcher and teaching assistant at the Instituto de Computación, Facultad de Ingeniería, Universidad de la República
Sergio Enrique Nesmachnow Cánovas
Uruguay
Aggregate Professor in the Numerical Center (CeCal) at Computer Science Institute, Engineering Faculty
Gerardo Goñi Bofrisco
Uruguay
Researcher in Computer Engineering at Servicio Central de Informática of Universidad de la República
Bernabé Dorronsoro Díaz
Spain
Researcher assistant at the Computer Science Engineering Department of the University of Cadiz
Andrei Tchernykh
Mexico
Full professor in computer science at CICESE Research Center, Ensenada, Baja California
References
1. Gao G., Zhang W., Wen Y., Wang Z., Zhu W. Towards Cost-Efficient Video Transcoding in Media Cloud: Insights Learned from User Viewing Patterns. IEEE Transactions on Multimedia, 17(8), 2015, pp. 1286–1296.
2. Hu M., Luo J., Wang Y., Veeravalli B. Practical resource provisioning and caching with dynamic resilience for cloud-based content distribution networks. IEEE Transactions on Parallel and Distributed Systems, 25(8), 2014, pp. 2169–2179.
3. Jokhio F., Ashraf A., Lafond S., Lilius J. A computation and storage trade-off strategy for cost-efficient video transcoding in the cloud. Proceedings of the 39th Euromicro Conference Series on Software Engineering and Advanced Applications, 2013, pp. 365–372.
4. Xiao W., Bao W., Zhu X., Wang C., Chen L., Yang L.T. Dynamic request redirection and resource provisioning for cloud-based video services under heterogeneous environment. IEEE Transactions on Parallel and Distributed Systems, 27(7), 2016, pp. 1954–1967.
5. Zhang J., Huang H., Wang X. Resource provision algorithms in cloud computing: A survey. Journal of Network and Computer Applications, 64, 2016, pp. 23–42.
6. Nesmachnow S., Iturriaga S., Dorronsoro B. Efficient heuristics for profit optimization of virtual cloud brokers. IEEE Computational Intelligence Magazine, 10(1), 2015, pp. 33-43.
7. Busari M., Williamson C. ProWGen: a synthetic workload generation tool for simulation evaluation of web proxy caches. Computer Networks, 38(6), 2002, pp. 779 – 794.
Review
For citations:
Iturriaga Fabra S., Nesmachnow Cánovas S., Goñi Bofrisco G., Dorronsoro Díaz B., Tchernykh A. Design and optimization of Content Distribution Networks. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2019;31(2):15-20. (In Russ.) https://doi.org/10.15514/ISPRAS-2019-31(2)-1