A Survey on Content Placement Algorithms for Cloud-Based Content Delivery Networks <s> 2) REQUEST PREDICTION <s> There has been an increasing deployment of content distribution networks (CDNs) that offer hosting services to Web content providers. CDNs deploy a set of servers distributed throughout the Internet and replicate provider content across these servers for better performance and availability than centralized provider servers. Existing work on CDNs has primarily focused on techniques for efficiently redirecting user requests to appropriate CDN servers to reduce request latency and balance load. However, little attention has been given to the development of placement strategies for Web server replicas to further improve CDN performance. We explore the problem of Web server replica placement in detail. We develop several placement algorithms that use workload information, such as client latency and request rates, to make informed placement decisions. We then evaluate the placement algorithms using both synthetic and real network topologies, as well as Web server traces, and show that the placement of Web replicas is crucial to CDN performance. We also address a number of practical issues when using these algorithms, such as their sensitivity to imperfect knowledge about client workload and network topology, the stability of the input data, and methods for obtaining the input. <s> BIB001 </s> A Survey on Content Placement Algorithms for Cloud-Based Content Delivery Networks <s> 2) REQUEST PREDICTION <s> Hosting a collection of millions of videos, YouTube offers several features to help users discover the videos of their interest. For example, YouTube provides video search, related video recommendation and front page highlight. The understanding of how these features drive video views is useful for creating a strategy to drive video popularity. In this paper, we perform a measurement study on data sets crawled from YouTube and find that the related video recommendation, which recommends the videos that are related to the video a user is watching, is one of the most important view sources of videos. Despite the fact that the YouTube video search is the number one source of views in aggregation, the related video recommendation is the main source of views for the majority of the videos on YouTube. Furthermore, our results reveal that there is a strong correlation between the view count of a video and the average view count of its top referrer videos. This implies that a video has a higher chance to become popular when it is placed on the related video recommendation lists of popular videos. We also find that the click through rate from a video to its related videos is high and the position of a video in a related video list plays a critical role in the click through rate. Finally, our evaluation of the impact of the related video recommendation system on the diversity of video views indicates that the current recommendation system helps to increase the diversity of video views in aggregation. <s> BIB002 </s> A Survey on Content Placement Algorithms for Cloud-Based Content Delivery Networks <s> 2) REQUEST PREDICTION <s> The exploding HD video streaming traffic calls for deploying content servers deeper inside network operators' infrastructures. Telco-CDN are new content distribution services that are managed by Internet Service Providers (ISP). Since the network operator controls both the infrastructure and the content delivery overlay, it is in a position to engineer telco-CDN so that networking resources are optimally utilized. In this paper, we show the following two findings: 1. it is possible to implement an efficient algorithm for the placement of video chunks into a telco-CDN. We present an algorithm, which is based on a genetic algorithm implemented on the MapReduce framework. We show that, for a national VoD service, computing a quasi-optimal placement is possible. 2. such push strategy makes sense because it allows to actually take into account fine-grain traffic management strategies on the underlying infrastructure. Our proposal re-opens the debate about the relevance of such "push" approach (where the manager of telco-CDN proactively pushes video content into servers) versus the traditional caching approach (where the content is pulled to the servers from requests of clients). Our proposal of a quasi-optimal tracker enables fair comparisons between both approaches for most traffic engineering policies. We illustrate the interest of our proposal in the context of a major European Telco-CDN with real traces from a popular Video-on-Demand (VoD) service. Our experimental results show that, given a perfect algorithm for predicting user preferences, our placement algorithm is able to keep aligned with LRU caching in terms of the traditional hit-ratio, but the workload on some troubled links (e.g., over-used links) in a push-based strategy is significantly alleviated. <s> BIB003
End-user request patterns are imperative in the design of efficient push-based CP algorithms. Though the performance of such prediction algorithms has significantly grown in recent years BIB003 , Qui et al. BIB001 show that even using imperfect estimates for content demand are sufficient for CP algorithms in traditional CDNs. However, with CCDNs, an open research challenge includes accurate and efficient demand prediction models and a scientific analysis of their effect on CP algorithms. For example, Zhou et al. BIB002 study prediction models that account for content access frequency, correlation, and popularity. Future research objective can account for the effect of social networking and its complex relationships on the prediction models for end-user requests for content.