Content Delivery Network Operator Research Articles

Tapping the Knowledge of Dynamic Traffic Demands for Optimal CDN Design

The content delivery network (CDN) intensively uses cache to push the content close to end users. Over both traditional Internet architecture and emerging cloud-based framework, cache allocation has been the core problem that any CDN operator needs to address. As the first step for cache deployment, CDN operators need to discover or estimate the distribution of user requests in different geographic areas. This step results in a statistical spatial model for the user requests, which is used as the key input to solve the optimal cache deployment problem. More often than not, the temporal information in user requests is omitted to simplify the CDN design. In this paper, we disclose that the spatial request model alone may not lead to truly optimal cache deployment and revisit the problem by taking the dynamic traffic demands into consideration. Specifically, we model the time-varying traffic demands and formulate the distributed cache deployment optimization problem with an integer linear program (ILP). To solve the problem efficiently, we transform the ILP problem into a scalable form and propose a greedy diagram to tackle it. Via experiments over the North American ISPs points of presence (PoPs) network, our new solution outperforms traditional CDN design method and saves the overall delivery cost by 16% to 20%. We also study the impact of various traffic demand patterns to the CDN design cost, via experiments with both real-world traffic demand patterns and extensive synthetic trace data.

Dynamic Deployment and Optimization of Virtual Content Delivery Networks

Today, over-the-top video streaming is gaining a lot of popularity. In this respect, the virtual content delivery network (CDN) is perceived as a key enabler to circumvent the technical challenges faced by content providers to deliver high-quality content over the Internet. Here, the authors investigate how the two main actors of the video delivery chain--the CDN operator and the ISP--can benefit from network and server virtualization to negotiate dynamic service-level agreements that reduce CDN capital expenditures and operating expenses, while generating more revenue for the ISP. First, the authors present a dataset used to simulate dynamic distributed traffic consumption. Second, they discuss the steps required to deploy and operate a virtual CDN deployed on an ISP's network. Furthermore, they present evaluation results of the proposed solution, based on simple models. Lastly, they elaborate on operational parameters that are used to further optimize the solution. This article is part of a special issue on advancing multimedia distribution.

Dynamic Deployment and Optimization of Virtual Content Delivery Networks

Today, over-the-top video streaming is gaining a lot of popularity. In this respect, the virtual content delivery network (CDN) is perceived as a key enabler to circumvent the technical challenges faced by content providers to deliver high-quality content over the Internet. Here, the authors investigate how the two main actors of the video delivery chain--the CDN operator and the ISP--can benefit from network and server virtualization to negotiate dynamic service-level agreements that reduce CDN capital expenditures and operating expenses, while generating more revenue for the ISP. First, the authors present a dataset used to simulate dynamic distributed traffic consumption. Second, they discuss the steps required to deploy and operate a virtual CDN deployed on an ISP's network. Furthermore, they present evaluation results of the proposed solution, based on simple models. Lastly, they elaborate on operational parameters that are used to further optimize the solution. This article is part of a special issue on advancing multimedia distribution.

Optimal planning of virtual content delivery networks under uncertain traffic demands

Content Delivery Networks (CDNs) have been identified as one of the relevant use cases where the emerging paradigm of Network Functions Virtualization (NFV) will likely be beneficial. In fact, virtualization fosters flexibility, since on-demand resource allocation of virtual CDN nodes can accommodate sudden traffic demand changes. However, there are cases where physical appliances should still be preferred, therefore we envision a mixed architecture in between these two solutions, capable to exploit the advantages of both of them. Motivated by these reasons, in this paper we formulate a two-stage stochastic planning model that can be used by CDN operators to compute the optimal long-term network planning decision, deploying physical CDN appliances in the network and/or leasing resources for virtual CDN nodes in data centers. Key findings demonstrate that for a large range of pricing options and traffic profiles, NFV can significantly save network costs spent by the operator to provide the content distribution service.

Prediction-based Federated Management of Multi-scale Resources in Cloud

The connotation of the cloud resources have been extended to be multi-scale resources, which includes central resources as presented by data center, edge resources as presented by Content Delivery Network (CDN) and end resources as presented by Peer-to-Peer (P2P). Under the development situation of the scale of the cloud services, it is difficult to provide services (e.g. streaming distribution) with guaranteed QoS only relying on single type of resource (e.g. central resources) to geo-distributed users. Therefore, making multi-resources cooperative to provide reliable services is necessary. However, it is a great challenge to realize Federated Management of Multi-scale Resources (FMMR). In this research, we propose the idea of prediction-based FMMR, and present the problem formulation introducing economic profit from the perspective of CDN operators. Then, we present the method of Time-series Prediction based on Wavelet Analysis (TPWA) to predict the resource requirements of streaming cloud services in CDN. Finally, the predictability of the resource requirement pattern of the streaming cloud service and the effectiveness of our proposed method have been verified, based on the traces collected from a real CDN entity.

CDN Scalability Improvement using a Moderate Peer-assisted Method

Content Delivery Networks (CDN) server loads that fluctuant necessitate CDN to improve its service scalability especially when the peak load exceeds its service capacity. The peer assisted scheme is widely used in improving CDN scalability. However, CDN operators do not want to lose profit by overusing it, which may lead to the CDN resource utilization reduced. Therefore, improving CDN scalability moderately and guarantying CDN resource utilization maximized is necessary. However, when and how to use the peer-assisted scheme to achieve such improvement remains a great challenge. In this paper, we propose a new method called Dynamic Moderate Peer-assisted Method (DMPM), which uses time series analysis to predict and decide when and how many server loads needs to offload. A novel peer-assisted mechanism based on the prediction designed, which can maximize the profit of the CDN operators without influencing scalability. Extensive evaluations based on an actual CDN load traces have shown the effectiveness of DMPM.