Nishanth Sastry

Delivery Properties of Human Social Networks

The recently proposed packet switched network paradigm takes advantage of human social contacts to opportunistically create data paths over time. Our goal is to examine the effect of the human contact process on data delivery. We find that the contact occurrence distribution is highly uneven: contacts between a few node-pairs occur too frequently, leading to inadequate mixing in the network, while the majority of contacts are rare, and essential for connectivity. This distribution of contacts leads to a significant variation in performance over short time windows. We discover that the formation of a large clique core during the window is correlated with the fraction of data delivered, as well as the speed of delivery. We then show that the clustering co-efficient of the contact graph over a time window is a good predictor of performance during the window. Taken together, our findings suggest new directions for designing forwarding algorithms in ad-hoc or delay-tolerant networking schemes using humans as data mules.

Social bootstrapping: how pinterest and last.fm social communities benefit by borrowing links from facebook

How does one develop a new online community that is highly engaging to each user and promotes social interaction? A number of websites offer friend-finding features that help users bootstrap social networks on the website by copying links from an established network like Facebook or Twitter. This paper quantifies the extent to which such social bootstrapping is effective in enhancing a social experience of the website. First, we develop a stylised analytical model that suggests that copying tends to produce a giant connected component (i.e., a connected community) quickly and preserves properties such as reciprocity and clustering, up to a linear multiplicative factor. Second, we use data from two websites, Pinterest and Last.fm, to empirically compare the subgraph of links copied from Facebook to links created natively. We find that the copied subgraph has a giant component, higher reciprocity and clustering, and confirm that the copied connections see higher social interactions. However, the need for copying diminishes as users become more active and influential. Such users tend to create links natively on the website, to users who are more similar to them than their Facebook friends. Our findings give new insights into understanding how bootstrapping from established social networks can help engage new users by enhancing social interactivity.

Buzztraq: predicting geographical access patterns of social cascades using social networks

Web 2.0 sites have made networked sharing of user generated content increasingly popular. Serving rich-media content with strict delivery constraints requires a distribution infrastructure. Traditional caching and distribution algorithms are optimised for globally popular content and will not be efficient for user generated content that often show a heavy-tailed popularity distribution. New algorithms are needed. This paper shows that information encoded in social network structure can be used to predict access patterns which may be partly driven by viral information dissemination, termed as a social cascade. Specifically, knowledge about the number and location of friends of previous users is used to generate hints that enable placing replicas of the content closer to future accesses.

ISP-friendly peer-assisted on-demand streaming of long duration content in BBC iPlayer

In search of scalable solutions, CDNs are exploring P2P support. However, the benefits of peer assistance can be limited by various obstacle factors such as ISP friendliness - requiring peers to be within the same ISP, bitrate stratification - the need to match peers with others needing similar bitrate, and partial participation - some peers choosing not to redistribute content. This work relates potential gains from peer assistance to the average number of users in a swarm, its capacity, and empirically studies the effects of these obstacle factors at scale, using a month-long trace of over 2 million users in London accessing BBC shows online. Results indicate that even when P2P swarms are localised within ISPs, up to 88% of traffic can be saved. Surprisingly, bitrate stratification results in 2 large sub-swarms and does not significantly affect savings. However, partial participation, and the need for a minimum swarm size do affect gains. We investigate improvements to gain from increasing content availability through two well-studied techniques: content bundling-combining multiple items to increase availability, and historical caching of previously watched items. Bundling proves ineffective as increased server traffic from larger bundles outweighs benefits of availability, but simple caching can considerably boost traffic gains from peer assistance.

CYRF: a theory of window-based unicast congestion control

This work presents a comprehensive theoretical framework for memoryless window-based congestion control protocols that are designed to converge to fairness and efficiency. We first derive a necessary and sufficient condition for stepwise convergence to fairness. Using this, we show how fair window increase/decrease policies can be constructed from suitable pairs of monotonically nondecreasing functions. We generalize this to smooth protocols that converge over each congestion epoch. The framework also includes a simple method for incorporating TCP-friendliness.Well-studied congestion control protocols such as TCP, GAIMD, and Binomial congestion control can be constructed using this method. Thus, we provide a common framework for the analysis of such window-based protocols. We also present two new congestion control protocols for streaming media-like applications as examples of protocol design in this framework: The first protocol, LOG, has the objective of reconciling the smoothness requirement of an application with the need for a fast dynamic response to congestion.The second protocol, SIGMOID, guarantees a minimum bandwidth for an application but behaves exactly like TCP for large windows.

A series of trials in the UK as part of the Ofcom TV white spaces pilot

TV White Spaces technology is a means of allowing wireless devices to opportunistically use locally-available TV channels (TV White Spaces), enabled by a geolocation database. The geolocation database informs the device of which channels can be used at a given location, and in the UK/EU case, which transmission powers (EIRPs) can be used on each channel based on the technical characteristics of the device, given an assumed interference limit and protection margin at the edge of the primary service coverage area(s). The UK regulator, Ofcom, has initiated a large-scale Pilot of TV White Spaces technology and devices. The ICT-ACROPOLIS Network of Excellence, teaming up with the ICT-SOLDER project and others, is running an extensive series of trials under this effort. The purpose of these trials is to test a number of aspects of white space technology, including the white space device and geolocation database interactions, the validity of the channel availability/powers calculations by the database and associated interference effects on primary services., and the performances of the white spaces devices, among others. An additional key purpose is to undertake a number of research investigations such as into aggregation of TV White Space resources with conventional (licensed/unlicensed) resources, secondary coexistence issues and means to mitigate such issues, and primary coexistence issues under challenging deployment geometries, among others. This paper describes our trials, their intentions and characteristics, objectives, and some early observations.

Data Delivery Properties of Human Contact Networks

Pocket Switched Networks take advantage of social contacts to opportunistically create data paths over time. This work employs empirical traces to examine the effect of the human contact process on data delivery in such networks. The contact occurrence distribution is found to be highly uneven: contacts between a few node pairs occur too frequently, leading to inadequate mixing in the network, while the majority of contacts occur rarely, but are essential for global connectivity. This distribution of contacts leads to a significant variation in the fraction of node pairs that can be connected over time windows of similar duration. Good time windows tend to have a large clique of nodes that can all reach each other. It is shown that the clustering coefficient of the contact graph over a time window is a good predictor of achievable connectivity. We then examine all successful paths found by flooding and show that though delivery times vary widely, randomly sampling a small number of paths between each source and destination is sufficient to yield a delivery time distribution close to that of flooding over all paths. This result suggests that the rate at which the network can deliver data is remarkably robust to path failures.

IARank: Ranking Users on Twitter in Near Real-Time, Based on Their Information Amplification Potential

This work introduces IARank, a novel, simple and accurate model to continuously rank influential Twitter users in real-time. Our model is based on the information amplification potential of a user, the capacity of the user to increase the audience of a tweet or another username that they find interesting, by retweets or mentions. We incorporate information amplification using two factors, the first of which indicates the tendency of a user to be retweeted or mentioned, and the second of which is proportional to the size of the audience of the retweets or mentions. We distinguish between cumulative influence acquired by a user over time, and an important tweet made by an otherwise not-important user, which deserves attention instantaneously, and devise our ranking scheme based on both notions of influence. We show that our methods produce rankings similar to PageRank, which is the basis for several other successful rankings of Twitter users. However, as opposed to PageRank-like algorithms, which take non-trivial time to converge, our method produces rankings in near-real time. We validate our results with a user-study, which shows that our method ranks top users similar to a manual ranking produced by the users themselves. Further, our ranking marginally outperformed PageRank, with 80% of the Top 5 most important users being classified as relevant to the event, whereas, PageRank had 60% of the Top 5 users marked as relevant. However, PageRank produces slightly better rankings, which correlates better with the user-produced rankings, when considering users beyond the top 5.

Network Slicing to Enable Scalability and Flexibility in 5G Mobile Networks

We argue for network slicing as an efficient solution that addresses the diverse requirements of 5G mobile networks, thus providing the necessary flexibility and scalability associated with future network implementations. We elaborate on the challenges that emerge when designing 5G networks based on network slicing. We focus on the architectural aspects associated with the coexistence of dedicated as well as shared slices in the network. In particular, we analyze the realization options of a flexible radio access network with focus on network slicing and their impact on the design of 5G mobile networks. In addition to the technical study, this article provides an investigation of the revenue potential of network slicing, where the applications that originate from this concept and the profit capabilities from the network operator�s perspective are put forward.

LENS: Leveraging social networking and trust to prevent spam transmission

In this paper we introduce LENS, a novel spam protection system based on the recipients social network, which allows correspondence within the social circle to directly pass to the mailbox and further mitigates spam beyond social circles. The key idea in LENS is to select legitimate and authentic users, called Gatekeepers (GKs), from outside the recipients social circle and within pre-defined social distances. Unless a GK vouches for the emails of potential senders from outside the social circle of a particular recipient, those e-mails are prevented from transmission. In this way LENS drastically reduces the consumption of Internet bandwidth by spam. Using extensive evaluations, we show that LENS provides each recipient reliable email delivery from a large fraction of the social network. We also evaluate the computational complexity of email processing with LENS deployed on two Mail Servers (MSs) and compared it with the most popular content-based filter i.e SpamAssassin. LENS proved to be fast in processing emails (around 2–3 orders of magnitude better than SpamAssassin) and scales efficiently with increasing community size and GKs.