Online Learning and Placement Algorithms for Efficient Delivery of User Generated Contents in Telco-CDNs

• Published in: IEEE eTransactions on network and service management 2020-03, Vol.17 (1), p.637-651
• Main Authors: Safavi, Mohammadhassan, Bastani, Saeed, Landfeldt, Bjorn
• Format: Article
• Language: English
• Subjects: Algorithms; Annan elektroteknik och elektronik; combinatorial optimisation; Computer and Information Science; Content delivery network; Data- och informationsvetenskap (Datateknik); Distance learning; Electrical Engineering, Electronic Engineering, Information Engineering; Elektroteknik och elektronik; End users; Engineering and Technology; Information Systems; Integer programming; Machine learning; Natural Sciences; Naturvetenskap; Optimization; Other Electrical Engineering, Electronic Engineering, Information Engineering; Placement; Systemvetenskap, informationssystem och informatik; Teknik; User generated content
• ISSN: 1932-4537; 1932-4537
• DOI: 10.1109/TNSM.2019.2961560

User generated content (UGC) makes up a significant portion of Internet traffic. As opposed to other content, UGC has so far been left outside over-the-top providing network operators content distribution networks (telco-CDN) due to the difficulty in determining optimised placement of such content. The side effect of this is that UGC content is not placed close to end users and therefore occupy unnecessary network resources. The difficulty in determining optimal placement of UGC stems from the different geographical and dynamic behaviour of the content generators, and a further complication is that with UGC, it is necessary to place content in real-time which this has an impact on performance optimality. Even though CDNs have been widely studied in the literature, little attention has been given to the challenging case of UGC placement. In this paper, we propose an on-line placement algorithm and compare its performance with the off-line counterpart based on integer programming, both under the assumption that the popularity of content is known to the algorithms. In order to determine the popularity, we present an on-line learning model to predict spatial patterns in content requests. Furthermore, we couple the model with an algorithm for learning the early popularity of content, i.e., shortly after the content becomes known. We show that together, these approaches enable service providers to effectively place UGC and minimise the cost of serving UGC in their networks.