You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Mobile online social network services have seen a rapid increase, in which the huge amount of user-generated social media contents propagating between users via social connections has significantly challenged the traditional content delivery paradigm: First, replicating all of the contents generated by users to edge servers that well "fit" the receivers becomes difficult due to the limited bandwidth and storage capacities. Motivated by device-to-device (D2D) communication that allows users with smart devices to transfer content directly, we propose replicating bandwidth-intensive social contents in a device-to-device manner. Based on large-scale measurement studies on social content propagation and user mobility patterns in edge-network regions, we observe that (1) Device-to-device replication can significantly help users download social contents from nearby neighboring peers; (2) Both social propagation and mobility patterns affect how contents should be replicated; (3) The replication strategies depend on regional characteristics ( e.g. , how users move across regions). Using these measurement insights, we propose a joint content replication strategy for edge-network regions, in which social contents are assigned to users in edge-network regions according to a joint consideration of social graph, content propagation and user mobility. We formulate the replication scheduling as an optimization problem and design distributed algorithm only using historical, local and partial information to solve it. Trace-driven experiments further verify the superiority of our proposal: compared with conventional pure movement-based and popularity-based approach, our design can significantly ( @math times) improve the amount of social contents successfully delivered by device-to-device replication.

Cited Abstract(s):
[1]: Online social network has reshaped the way how video contents are generated, distributed and consumed on today's Internet. Given the massive number of videos generated and shared in online social networks, it has been popular for users to directly access video contents in their preferred social network services. It is intriguing to study the service provision of social video contents for global users with satisfactory quality-of-experience. In this paper, we conduct large-scale measurement of a real-world online social network system to study the propagation of the social video contents. We have summarized important characteristics from the video propagation patterns, including social locality, geographical locality and temporal locality. Motivated by the measurement insights, we propose a propagation-based social-aware replication framework using a hybrid edge-cloud and peer-assisted architecture, namely PSAR, to serve the social video contents. Our replication strategies in PSAR are based on the design of three propagation-based replication indices, including a geographic influence index and a content propagation index to guide how the edge-cloud servers backup the videos, and a social influence index to guide how peers cache the videos for their friends. By incorporating these replication indices into our system design, PSAR has significantly improved the replication performance and the video service quality. Our trace-driven experiments further demonstrate the effectiveness and superiority of PSAR, which improves the local download ratio in the edge-cloud replication by 30 , and the local cache hit ratio in the peer-assisted replication by 40 , against traditional approaches.

[2]: Online microblogging has been very popular in today's Internet, where users exchange short messages and follow various contents shared by people that they are interested in. Among the variety of exchanges, video links are a representative type on a microblogging site. More and more viewers of an Internet video service are coming from microblog recommendations. It is intriguing research to explore the connections between the patterns of microblog exchanges and the popularity of videos, in order to potentially use the propagation patterns of microblogs to guide proactive service deployment of a video sharing system. Based on extensive traces from Youku and Tencent Weibo, a popular video sharing site and a favored microblogging system in China, we explore how patterns of video link propagation in the microblogging system are correlated with video popularity on the video sharing site, at different times and in different geographic regions. Using influential factors summarized from the measurement studies, we further design neural network-based learning frameworks to predict the number of potential viewers of different videos and the geographic distribution of viewers. Experiments show that our neural network-based frameworks achieve better prediction accuracy, as compared to a classical approach that relies on historical numbers of views. We also briefly discuss how proactive video service deployment can be effectively enabled by our prediction frameworks.

[3]: Providers such as YouTube offer easy access to multimedia content to millions, generating high bandwidth and storage demand on the Content Delivery Networks they rely upon. More and more, the diffusion of this content happens on online social networks such as Facebook and Twitter, where social cascades can be observed when users increasingly repost links they have received from others. In this paper we describe how geographic information extracted from social cascades can be exploited to improve caching of multimedia files in a Content Delivery Network. We take advantage of the fact that social cascades can propagate in a geographically limited area to discern whether an item is spreading locally or globally. This informs cache replacement policies, which utilize this information to ensure that content relevant to a cascade is kept close to the users who may be interested in it. We validate our approach by using a novel dataset which combines social interaction data with geographic information: we track social cascades of YouTube links over Twitter and build a proof-of-concept geographic model of a realistic distributed Content Delivery Network. Our performance evaluation shows that we are able to improve cache hits with respect to cache policies without geographic and social information.

[4]: The difficulty of scaling Online Social Networks (OSNs) has introduced new system design challenges that has often caused costly re-architecting for services like Twitter and Facebook. The complexity of interconnection of users in social networks has introduced new scalability challenges. Conventional vertical scaling by resorting to full replication can be a costly proposition. Horizontal scaling by partitioning and distributing data among multiples servers - e.g. using DHTs - can lead to costly inter-server communication. We design, implement, and evaluate SPAR, a social partitioning and replication middle-ware that transparently leverages the social graph structure to achieve data locality while minimizing replication. SPAR guarantees that for all users in an OSN, their direct neighbor's data is co-located in the same server. The gains from this approach are multi-fold: application developers can assume local semantics, i.e., develop as they would for a single server; scalability is achieved by adding commodity servers with low memory and network I O requirements; and redundancy is achieved at a fraction of the cost. We detail our system design and an evaluation based on datasets from Twitter, Orkut, and Facebook, with a working implementation. We show that SPAR incurs minimum overhead, and can help a well-known open-source Twitter clone reach Twitter's scale without changing a line of its application logic and achieves higher throughput than Cassandra, Facebook's DHT based key-value store database.

[5]: This paper surveys the emerging paradigm of cloud mobile media. We start with two alternative perspectives for cloud mobile media networks: an end-to-end view and a layered view. Summaries of existing research in this area are organized according to the layered service framework: i) cloud resource management and control in infrastructure-as-a-service (IaaS), ii) cloud-based media services in platform-as-a-service (PaaS), and iii) novel cloud-based systems and applications in software-as-a-service (SaaS). We further substantiate our proposed design principles for cloud-based mobile media using a concrete case study: a cloud-centric media platform (CCMP) developed at Nanyang Technological University. Finally, this paper concludes with an outlook of open research problems for realizing the vision of cloud-based mobile media.

[6]: Peer-to-peer file sharing systems, most notably BitTorrent (BT), have achieved tremendous success among Internet users. Recent studies suggest that long-term relationships among BT peers could be explored for peer cooperation, so as to achieve better sharing efficiency. However, whether such long-term relationships exist remain unknown. From an 80-day trace of 100,000 real world swarms, we find that less than 5 peers can meet each other again throughout the whole period, which largely invalidates the fundamental assumption of these peer cooperation protocols. Yet the recent emergence of online social network applications sheds new light on this problem. In particular, a number of BT swarms are now triggered by Twitter, reflecting a new trend for initializing sharing among communities. In this paper, we for the first time examine the challenges and potentials of accelerating peer-to-peer file sharing with Twitter social networks. We show that the peers in such swarms have stronger temporal locality, thus offering great opportunity for improving their degree of sharing. Based on the Hadamard Transform of peers' online behaviors, we develop a social index to quickly locate peers of common patterns. We further demonstrate a practical cooperation protocol that identifies and utilizes the social relations with the index. Our PlanetLab experiments indicate that the incorporation of social relations remarkably accelerates the downloading time. The improvement remains noticeable even in a hybrid system with a small set of socially active peers only.
As online social networks are affecting for all types of online contents, conventional content delivery paradigms need improvement using social information. Pujol [4] designed a social partition and replication middleware where users' friends' data can be co-located in datacenter servers. [3] investigated using social information for content delivery over the edge networks. [2] [1] investigated the possibility to infer social propagation according to users' social profiles and behaviors, and allocate network resource at edge-cloud servers based on propagation predictions. [5] further proposed the cloud mobile media concept to utilize cloud-based resource for mobile media content processing and distribution. [6] proposed a novel peer-assisted paradigm using social relationship for improved social media distribution.