A Survey on Content Placement Algorithms for Cloud-Based Content Delivery Networks <s> VI. DESIGN CRITERIA OF CP ALGORITHMS BASED ON CCDN INFRASTRUCTURE <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> BIB001 </s> A Survey on Content Placement Algorithms for Cloud-Based Content Delivery Networks <s> VI. DESIGN CRITERIA OF CP ALGORITHMS BASED ON CCDN INFRASTRUCTURE <s> Changing network conditions pose severe problems to video streaming in the Internet. HTTP adaptive streaming (HAS) is a technology employed by numerous video services that relieves these issues by adapting the video to the current network conditions. It enables service providers to improve resource utilization and Quality of Experience (QoE) by incorporating information from different layers in order to deliver and adapt a video in its best possible quality. Thereby, it allows taking into account end user device capabilities, available video quality levels, current network conditions, and current server load. For end users, the major benefits of HAS compared to classical HTTP video streaming are reduced interruptions of the video playback and higher bandwidth utilization, which both generally result in a higher QoE. Adaptation is possible by changing the frame rate, resolution, or quantization of the video, which can be done with various adaptation strategies and related client- and server-side actions. The technical development of HAS, existing open standardized solutions, but also proprietary solutions are reviewed in this paper as fundamental to derive the QoE influence factors that emerge as a result of adaptation. The main contribution is a comprehensive survey of QoE related works from human computer interaction and networking domains, which are structured according to the QoE impact of video adaptation. To be more precise, subjective studies that cover QoE aspects of adaptation dimensions and strategies are revisited. As a result, QoE influence factors of HAS and corresponding QoE models are identified, but also open issues and conflicting results are discussed. Furthermore, technical influence factors, which are often ignored in the context of HAS, affect perceptual QoE influence factors and are consequently analyzed. This survey gives the reader an overview of the current state of the art and recent developments. At the same time, it targets networking researchers who develop new solutions for HTTP video streaming or assess video streaming from a user centric point of view. Therefore, this paper is a major step toward truly improving HAS. <s> BIB002
In this section, we discuss the design criteria that significantly impact the efficiency of the CP algorithms due to the intrinsic cost model of CCDNs. Generally, CP algorithm must be designed to reduce operational cost, maximize SLA-defined QoS and resource utilization and provision resources to meet the dynamically changing popularity of content and end-user content access patterns. Operational costs or OPEX are the cost of consuming resources in the cloud. These costs include the cost of storing content on the surrogate servers in datacenters and the cost of consuming bandwidth for the retrieval and update of content. These are costs incurred by CCDN providers using third party cloud infrastructure. For these CCDN providers, it is important to minimize these operational costs without compromising end-user perceived QoS. Quality of service for end-users is defined as soft or hard guarantee on one or more QoS metrics. A hard QoS guarantee will ensure that the guaranteed QoS metric is never violated. However, soft QoS is implicit and is achieved indirectly, for example, by maximizing traffic between the surrogate servers and end-users and minimizing traffic between the origin server and surrogate servers. However, soft QoS is tolerant to QoS violations. The QoS metrics can be network health and/or communication metrics quality, such as end-user perceived latency, end-to-end delay, geodesic distance, jitter in delay, hop count, round trip time and/or other network distance functions. Recently, there is a shift from QoS to QoE BIB001 , which is user-centric and subjective. It is hard to monitor and guarantee QoE ( BIB001 , BIB002 ). It should be noted that QoS and QoE cannot be used interchangeably since they are inherently different parameters for capturing the end-user satisfaction with CCDN service. CP algorithms for CCDNs also greatly benefit from resource utilization and dynamic resource allocation. Resource utilization is intrinsic in maximizing the use of already leased resources, rather than leasing new resources. Though it is tightly coupled with operational cost, it is different than just minimizing the cost of resource allocation. Therefore, resource utilization and operational cost are not interchangeable objectives. Explicitly, resource utilization is defined as delivering maximum content from the same leased surrogate server in the cloud before leasing new resources in the cloud. Resource provisioning is the ability to lease or release resources to meet the changes in the end-user demands. It leverages the elasticity of the cloud to adapt the leased resources such that they can be released to meet lower than expected end-user demands or more resources can be leased to meet a sudden surge in end-user demands. It directly impacts operational costs and QoS design criteria. This design criterion intersects with designing a CP algorithm that dynamically adapts to the popularity of content. This is one major benefit of moving CDNs into the cloud to leverage the elasticity and flexibility of the cloud to meet the evolving interest of end-users in the published content. This enables CP algorithms to move correlated content into and out of the surrogate servers based on the referred content since they affect each other's popularity. Therefore, critical design criteria based on the cloud for content placement algorithms are operational cost, QoS, resource utilization and resource provisioning, as shown in Fig. 14. Though these criteria are tightly coupled, to simplify the implementation and maintenance and to reduce the overhead of the CP algorithms, they are often decoupled. 