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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This paper describes Haystack, an object storage system optimized for Facebook's Photos application. Facebook currently stores over 260 billion images, which translates to over 20 petabytes of data. Users upload one billion new photos (∼60 terabytes) each week and Facebook serves over one million images per second at peak. Haystack provides a less expensive and higher performing solution than our previous approach, which leveraged network attached storage appliances over NFS. Our key observation is that this traditional design incurs an excessive number of disk operations because of metadata lookups. We carefully reduce this per photo metadata so that Haystack storage machines can perform all metadata lookups in main memory. This choice conserves disk operations for reading actual data and thus increases overall throughput. Online Social Networks (OSN) are fun, popular, and socially significant. An integral part of their success is the immense size of their global user base. To provide a consistent service to all users, Facebook, the world's largest OSN, is heavily dependent on centralized U.S. data centers, which renders service outside of the U.S. sluggish and wasteful of Internet bandwidth. In this paper, we investigate the detailed causes of these two problems and identify mitigation opportunities. Because details of Facebook's service remain proprietary, we treat the OSN as a black box and reverse engineer its operation from publicly available traces. We find that contrary to current wisdom, OSN state is amenable to partitioning and that its fine grained distribution and processing can significantly improve performance without loss in service consistency. Through simulations of reconstructed Facebook traffic over measured Internet paths, we show that user requests can be processed 79 faster and use 91 less bandwidth. We conclude that the partitioning of OSN state is an attractive scaling strategy for Facebook and other OSN services. Facebook is one of the most famous social network sites hosting a number of users approaching a billion [1]. Facebook is a cloud-based web site which contains a number of advanced technologies behind the scene. Despite the fact that it is the web site that most users open for all-day and, in some places, all-night long, and its traffic is generally part of all types of networks — wired and wireless, PAN, LAN, MAN, and WAN —, there is virtually no research work or technical paper that report on client's perspective of what happen when users login onto their Facebook homepages. How many and in what order components loaded are, which and how many servers those components loaded are, or how many TCP streams used, are examples of questions which users and network administrators have a little knowledge of. Our work tries to answer the questions by examining every of over 2,000 packets per a single Facebook homepage retrieval using the Wireshark packet capturing software. From the captured packets, we thoroughly examined all objects that Facebook retrieved and categorized them into groups based on the characteristics. Our investigations found that Facebook retrieves these objects from both international and domestic servers, and over a number of parallel TCP streams. The results also showed that the Facebook traffic exhibited a two-spike bursty pattern, regardless of the loading time.