Facebook is today the most popular social network with more than one billion subscribers worldwide. To provide good quality of service (e.g., low access delay) to their clients, FB relies on Akamai, which provides a worldwide content distribution network with a large number of edge servers that are much closer to FB subscribers. In this article we aim to depict a global picture of the current FB network infrastructure deployment taking into account both native FB servers and Akamai nodes. Toward this end, we have performed a measurement-based analysis during a period of two weeks using 463 Planet- Lab nodes distributed across 41 countries. Based on the obtained data we compare the average access delay that nodes in different countries experience accessing both native FB servers and Akamai nodes. In addition, we obtain a wide view of the deployment of Akamai nodes serving FB users worldwide. Finally, we analyze the geographical coverage of those nodes, and demonstrate that in most of the cases Akamai nodes located in a particular country service not only local FB subscribers, but also FB users located in nearby countries.
There also exist some research works that carried out different performance analyses on Facebook Services. Authors in @cite_7 look at the established connections when FB users login in the system. In particular, they identify different sections in the FB wall page of a user, and analyze how the information filling those sections is retrieved. An earlier work from 2010 @cite_4 identified some performance degradation (e.g., delay, packet losses, etc.) for some users accessing FB from outside US. Finally, we have found another interesting study @cite_9 that states that photo view is the most critical service for FB, and presents a detailed description on how FB photos are distributed to CDN Akamai servers. However, it does not perform a geographical analysis to understand how different regions of the world are being served as we do in our article.
@cite_9 @cite_4 @cite_7
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With more than 250 million active users, Facebook (FB) is currently one of the most important online social networks. Our goal in this paper is to obtain a representative (unbiased) sample of Facebook users by crawling its social graph. In this quest, we consider and implement several candidate techniques. Two approaches that are found to perform well are the Metropolis-Hasting random walk (MHRW) and a re-weighted random walk (RWRW). Both have pros and cons, which we demonstrate through a comparison to each other as well as to the "ground-truth" (UNI - obtained through true uniform sampling of FB userIDs). In contrast, the traditional Breadth-First-Search (BFS) and Random Walk (RW) perform quite poorly, producing substantially biased results. In addition to offline performance assessment, we introduce online formal convergence diagnostics to assess sample quality during the data collection process. We show how these can be used to effectively determine when a random walk sample is of adequate size and quality for subsequent use (i.e., when it is safe to cease sampling). Using these methods, we collect the first, to the best of our knowledge, unbiased sample of Facebook. Finally, we use one of our representative datasets, collected through MHRW, to characterize several key properties of Facebook. Content distribution networks (CDNs) need to make decisions, such as server selection and routing, to improve performance for their clients. The performance may be limited by various factors such as packet loss in the network, a small receive buffer at the client, or constrained server CPU and disk resources. Conventional measurement techniques are not effective for distinguishing these performance problems: application-layer logs are too coarse-grained, while network-level traces are too expensive to collect all the time. We argue that passively monitoring the transport-level statistics in the server's network stack is a better approach. This paper presents a tool for monitoring and analyzing TCP statistics, and an analysis of a CoralCDN node in PlanetLab for six weeks. Our analysis shows that more than 10 of connections are server-limited at least 40 of the time, and many connections are limited by the congestion window despite no packet loss. Still, we see that clients in 377 Autonomous Systems (ASes) experience persistent packet loss. By separating network congestion from other performance problems, our analysis provides a much more accurate view of the performance of the network paths than what is possible with server logs alone. Recent advances in localization techniques have fundamentally enhanced social networking services, allowing users to share their locations and location-related contents, such as geo-tagged photos and notes. We refer to these social networks as location-based social networks (LBSNs). Location data bridges the gap between the physical and digital worlds and enables a deeper understanding of users' preferences and behavior. This addition of vast geo-spatial datasets has stimulated research into novel recommender systems that seek to facilitate users' travels and social interactions. In this paper, we offer a systematic review of this research, summarizing the contributions of individual efforts and exploring their relations. We discuss the new properties and challenges that location brings to recommender systems for LBSNs. We present a comprehensive survey analyzing 1) the data source used, 2) the methodology employed to generate a recommendation, and 3) the objective of the recommendation. We propose three taxonomies that partition the recommender systems according to the properties listed above. First, we categorize the recommender systems by the objective of the recommendation, which can include locations, users, activities, or social media. Second, we categorize the recommender systems by the methodologies employed, including content-based, link analysis-based, and collaborative filtering-based methodologies. Third, we categorize the systems by the data sources used, including user profiles, user online histories, and user location histories. For each category, we summarize the goals and contributions of each system and highlight the representative research effort. Further, we provide comparative analysis of the recommender systems within each category. Finally, we discuss the available data-sets and the popular methods used to evaluate the performance of recommender systems. Finally, we point out promising research topics for future work. This article presents a panorama of the recommender systems in location-based social networks with a balanced depth, facilitating research into this important research theme. The rapid growth of Location-based Social Networks (LBSNs) provides a vast amount of check-in data, which facilitates the study of point-of-interest (POI) recommendation. The majority of the existing POI recommendation methods focus on four aspects, i.e., temporal patterns, geographical influence, social correlations and textual content indications. For example, user's visits to locations have temporal patterns and users are likely to visit POIs near them. In real-world LBSNs such as Instagram, users can upload photos associating with locations. Photos not only reflect users' interests but also provide informative descriptions about locations. For example, a user who posts many architecture photos is more likely to visit famous landmarks; while a user posts lots of images about food has more incentive to visit restaurants. Thus, images have potentials to improve the performance of POI recommendation. However, little work exists for POI recommendation by exploiting images. In this paper, we study the problem of enhancing POI recommendation with visual contents. In particular, we propose a new framework Visual Content Enhanced POI recommendation (VPOI), which incorporates visual contents for POI recommendations. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework.