期刊
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
卷 9, 期 2, 页码 769-782出版社
IEEE COMPUTER SOC
DOI: 10.1109/TNSE.2021.3132556
关键词
Costs; Content distribution networks; Profitability; Network slicing; Information-centric networking; Quality of service; Integer linear programming; Content Delivery Networks (CDNs); Information Centric Networks (ICNs); Network slicing; 5G
资金
- Canadian Natural Sciences and Engineering Research Council (NSERC)
- Ericsson through a CRD grant
- Concordia University through the Horizon postdoctoral program
This paper proposes a framework for content delivery using information-centric networks and IP slices. By leveraging in-network caching advantages, it addresses the creation of ICN and IP slices in a dynamic environment and achieves cost-efficient and profit-maximizing solutions through a dynamic network slicing heuristic algorithm.
Content Delivery Networks (CDNs) are becoming more critical due to the tremendous growth of video traffic. This paper proposes a complete framework targeting the creation of Information Centric Network (ICN) and IP slices for content delivery. Leveraging ICN's in-network caching advantages, our solution is tailored to a VNF placement context where ICN and IP slices are dynamically created over a physical infrastructure. Embedding delay-constrained slices with high profits requires adjustments of the available resources based on the traffic and slice demands, which leads to the reassigning of the slices. However, slice reassignment may lead to additional costs, content disruption in service delivery, and QoS violation. We formulate the problem as an Integer Linear Programming (ILP) and propose a cost-efficient dynamic network slicing heuristic. The objective is to maximize the profit of infrastructure providers while meeting the different QoS requirements of the slices. Our evaluation validates the effectiveness of profit maximization and analyzes the impact of QoS violation on infrastructure provider profit. The results are compared to a recent work from the literature and an optimal solution. The evaluation shows that our proposed heuristic is promising and offers a near-optimal solution while improving profitability and ensuring QoS.
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