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Though the new OSN model, which deploys datacenters globally, helps reduce service latency, it causes higher inter-datacenter communication load. In Facebook, each datacenter has a full copy of all data, and the master datacenter updates all other datacenters, generating tremendous load in this new model. Distributed data storage, which only stores a user's data to his/her geographically closest datacenters mitigates the problem. However, frequent interactions between distant users lead to frequent inter-datacenter communication and hence long service latencies. In this paper, we aim to reduce inter-datacenter communications while still achieving low service latency. We first verify the benefits of the new model and present OSN typical properties that underlie the basis of our design. We then propose Selective Data replication mechanism in Distributed Datacenters (SD 3 ). Since replicas need inter-datacenter data updates, datacenters in SD 3 jointly consider update rates and visit rates to select user data for replication; furthermore, SD 3 atomizes users' different types of data (e.g., status update, friend post, music) for replication, ensuring that a replica always reduces inter-datacenter communication. SD 3 also incorporates three strategies to further enhance its performance: locality-aware multicast update tree, replica deactivation, and datacenter congestion control. The results of trace-driven experiments on the real-world PlanetLab testbed demonstrate the higher efficiency and effectiveness of SD 3 in comparison to other replication methods and the effectiveness of its three schemes. and require only the capacity to serve nearby users, not all users in the OSN. Assigning the geographically closest datacenter to a user to serve the user and store his/her master replica helps reduce service latency and service network load to the users of OSNs, since the network load of a package is related to both package size and transmission distance [4]. Indeed, Facebook is now building a datacenter in Sweden to make Facebook faster for Europeans [3]. However, this new model causes higher inter-datacenter communication load (i.e., network load, the resource consumption for data transmission [4]). In this new model, Facebook's single-master replication protocol obviously would generate a tremendously high load. Though the distributed data storage that maintains a user's data in his/her geographically closest datacenter mitigates the problem, the frequent interactions between distant users lead to frequent communication between datacenters.Thus, in this paper, we study how to replicate data in OSN distributed datacenters to minimize inter-datacenter communication load while still achieving low service latency. Increasing replication of user data can enable the resolution of more data requests locally, leading to lower service latencies; however, a rarely visited replica provides little benefit in terms of service latency reduction but increases network load for updates, leading to higher inter-datacenter communication l...

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No abstract

The past few years have seen an explosion in the popularity of online short-video sharing in YouTube. As the number of users continue to grow, the bandwidth required to maintain acceptable quality of service (QoS) has greatly increased. Peer-to-peer (P2P) architectures have shown promise in reducing the bandwidth costs; however, the previous works build one P2P overlay for each video, which provides limited availability of video providers and produces high overlay maintenance overhead. To handle these problems, in this work, we novelly leverage the existing social network in YouTube, where a user subscribes to another user’s channel to track all his/her uploaded videos. The subscribers of a channel tend to watch the channel’s videos and common-interest nodes tend to watch the same videos. Also, the popularity of videos in one channel varies greatly. We study real trace data to confirm these properties. Based on these properties, we propose SocialTube, which builds the subscribers of one channel into a P2P overlay and also clusters common-interest nodes in a higher level. It also incorporates a prefetching algorithm that prefetches higher-popularity videos. To enhance the system performance, we further propose the demand/supply-based cache management scheme and reputation-based neighbor management scheme. Extensive trace-driven simulation results and PlanetLab real-world experimental results verify the effectiveness of SocialTube at reducing server load and overlay maintenance overhead and at improving QoS for users.