A Survey on Content Placement Algorithms for Cloud-Based Content Delivery Networks <s> A. CLOUD MODEL 1) OPERATIONAL COST VS. QoS <s> In recent years, technical challenges are emerging on how to efficiently distribute the rapid growing user-generated contents (UGCs) with long-tailed nature. To address this issue, we have previously proposed cloud centric media network (CCMN) for cost-efficient UGCs delivery. In this paper, we further study the content placement problem in CCMN. Our objective is to minimize the monetary cost incurred by using cloud resources to orchestrate an elastic and global content delivery network (CDN) service. In particular, this objective is achieved via a two-step method. First, for a single content, we map it into a k-center problem, and find a logarithmic relationship between the mean hop distance from users to contents, and the reciprocal of replica number. Second, for multiple contents, we formulate a convex optimization with storage and bandwidth capacity constraints, which can be solved by our proposed algorithm. Finally, we verify the algorithm based on real-world traces collected from a popular video website in China. Our numerical results suggest that, the optimal number of replica for each content follows a power law in respect to its popularity, under feasible storage and bandwidth constraints, in a set of deployed backbone networks. <s> BIB001 </s> A Survey on Content Placement Algorithms for Cloud-Based Content Delivery Networks <s> A. CLOUD MODEL 1) OPERATIONAL COST VS. QoS <s> YouTube is the most popular service in today's Internet. Google relies on its massive content delivery network (CDN) to push YouTube videos as close as possible to the end-users, both to improve their watching experience as well as to reduce the load on the core of the network, using dynamic server selection strategies. However, we show that such a dynamic approach can actually have negative effects on the end-user quality of experience (QoE). Through the comprehensive analysis of one month of YouTube flow traces collected at the network of a large European ISP, we report a real case study in which YouTube QoE-relevant degradation affecting a large number of users occurs as a result of Google's server selection strategies. We present an iterative and structured process to detect, characterize, and diagnose QoE-relevant anomalies in CDN distributed services such as YouTube. The overall process uses statistical analysis methodologies to unveil the root causes behind automatically detected problems linked to the dynamics of CDNs' server selection strategies. <s> BIB002
The dynamic and short-term resource provisioning in the cloud is also the underlying contributor to operational cost. There is a direct tradeoff between operational cost and QoS that a CP strategy offers. It is imperative to scrutinize and quantify the tradeoff or the cost of striking a balance between operational cost and QoS. For example, Jin et al. BIB001 have identified a novel relationship between the number of replicas of content and the mean hop distance between end-users and content. Critical research objectives include (i) the quantification of the effect of the number of replicas on QoS for end-users, (ii) analyzing the cost of offering hard and/or soft QoS, (iii) the effect of overhead pertaining to leasing and initializing resources in the cloud on QoS for end-users, with respect to delay. However, there are some key performance indicators and metrics for measuring and defining the subjective QoE BIB002 . The challenge lies in scrutinizing the impact of QoE parameters over QoS metrics for CP in CCDNs.