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.
As online social networks are affecting for all types of online contents, conventional content delivery paradigms need improvement using social information. Pujol @cite_27 designed a social partition and replication middleware where users' friends' data can be co-located in datacenter servers. @cite_19 investigated using social information for content delivery over the edge networks. @cite_3 @cite_32 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. @cite_12 further proposed the cloud mobile media concept to utilize cloud-based resource for mobile media content processing and distribution. @cite_17 proposed a novel peer-assisted paradigm using social relationship for improved social media distribution.
@cite_32 @cite_3 @cite_19 @cite_27 @cite_12 @cite_17
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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. 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. The emergence of smart edge-network content item hotspots, which are equipped with huge storage space (e.g., several GBs), opens up the opportunity to study the possibility of delivering videos at the edge network. Different from both the conventional content item delivery network (CDN) and the peer-to-peer (P2P) scheme, this new delivery paradigm, namely edge video CDN, requires up to millions of edge hotspots located at users’ homes offices to be coordinately managed to serve mobile video content item. Specifically, two challenges are involved in building edge video CDN, including how edge content item hotspots should be organized to serve users, and how content items should be replicated to them at different locations to serve users. To address these challenges, we propose our data-driven design as follows. First, we formulate an edge region partition problem to jointly maximize the quality experienced by users and minimize the replication cost, which is NP-hard in nature, and we design a Voronoi-like partition algorithm to generate optimal service cells. Second, to replicate content items to edge-network content item hotspots, we propose an edge request prediction based replication strategy, which carries out the replication in a server peak offloading manner. We implement our design and use trace-driven experiments to verify its effectiveness. Compared with conventional centralized CDN and popularity-based replication, our design can significantly improve users’ quality of experience, in terms of users’ perceived bandwidth and latency, up to 40 . We study the dissemination of dynamic content, such as news or traffic information, over a mobile social network. In this application, mobile users subscribe to a dynamic-content distribution service, offered by their service provider. To improve coverage and increase capacity, we assume that users share any content updates they receive with other users they meet. We make two contributions. First, we determine how the service provider can allocate its bandwidth optimally to make the content at users as "fresh" as possible. More precisely, we define a global fairness objective (namely, maximizing the aggregate utility over all users) and prove that the corresponding optimization problem can be solved by gradient descent. Second, we specify a condition under which the system is highly scalable: even if the total bandwidth dedicated by the service provider remains fixed, the expected content age at each user grows slowly (as log(n)) with the number of users n. To the best of our knowledge, our work is the first to address these two aspects (optimality and scalability) of the distribution of dynamic content over a mobile social network.