A Survey on Content Placement Algorithms for Cloud-Based Content Delivery Networks <s> 4) SIZE OF SURROGATE SERVER <s> Content distribution networks (CDNs) using storage clouds have recently started to emerge. Compared to traditional CDNs, storage cloud-based CDNs have the advantage of cost effectively offering hosting services to Web content providers without owning infrastructure. However, existing work on replica placement in CDNs does not readily apply in the cloud. In this paper, we investigated the joint problem of building distribution paths and placing Web server replicas in cloud CDNs to minimize the cost incurred on the CDN providers while satisfying QoS requirements for user requests. We formulate the cost optimization problem with accurate cost models and QoS requirements and show that the monthly cost can be as low as 2.62 US Dollars for a small Web site. We develop a suite of offline, online-static and online-dynamic heuristic algorithms that take as input network topology and work load information such as user location and request rates. We then evaluate the heuristics via Web trace-based simulation, and show that our heuristics behave very close to optimal under various network conditions. <s> BIB001 </s> A Survey on Content Placement Algorithms for Cloud-Based Content Delivery Networks <s> 4) SIZE OF SURROGATE SERVER <s> In this paper, we report YouTube traffic measurements from Orange IP backbone network connecting residential customers. We exhibit its salient features in relation to the performance of caching. By examining the file popularity distribution, we show that video requests are highly volatile in that a huge number of files are viewed only a few times; these files are therefore not relevant for caching. Nevertheless, there is a subset of files which are massively viewed by end users and are worth caching. On the basis of this experimental observation, we develop a mathematical model for estimating the efficiency of file caching in the presence of noise traffic composed of those files which are rarely requested and thus “pollute” the cache. We then proceed to trace-driven simulations in order to check the qualitative conclusions derived from the theoretical model. <s> BIB002 </s> A Survey on Content Placement Algorithms for Cloud-Based Content Delivery Networks <s> 4) SIZE OF SURROGATE SERVER <s> Popular online video-on-demand (VoD) services all maintain a large catalog of videos for their users to access. The knowledge of video popularity is very important for system operation , such as video caching on content distribution network (CDN) servers. The video popularity distribution at a given time is quite well understood. We study how the video popularity changes with time, for different types of videos, and apply the results to design video caching strategies. Our study is based on analyzing the video access levels over time, based on data provided by a large video service provider. Our main finding is, while there are variations, the glory days of a video’s popularity typically pass by quickly and the probability of replaying a video by the same user is low. The reason appears to be due to fairly regular number of users and view time per day for each user, and continuous arrival of new videos. All these facts will affect how video popularity changes, hence also affect the optimal video caching strategy. Based on the observation from our measurement study, we propose a mixed replication strategy (of LFU and FIFO) that can handle different kinds of videos. Offline strategy assuming tomorrow’s video popularity is known in advance is used as a performance benchmark. Through trace-driven simulation, we show that the caching performance achieved by the mixed strategy is very close to the performance achieved by the offline strategy. <s> BIB003
The efficiency of pull-based content placement is directly proportional to the size of storage on the surrogate server. Though cloud resources can be leased to adapt to the size dynamically, it is a challenge to find the balance between operational cost and efficiency of CP algorithms with respect to QoS. Therefore, for pull-based CP algorithms, the challenge that emerges is to select appropriate storage allocation for content placement ( BIB001 , BIB003 , BIB002 ).