Konstantina Papagiannaki

Breaking for commercials: characterizing mobile advertising

Mobile phones and tablets can be considered as the first incarnation of the post-PC era. Their explosive adoption rate has been driven by a number of factors, with the most signifcant influence being applications (apps) and app markets. Individuals and organizations are able to develop and publish apps, and the most popular form of monetization is mobile advertising.n The mobile advertisement (ad) ecosystem has been the target of prior research, but these works typically focused on a small set of apps or are from a user privacy perspective. In this work we make use of a unique, anonymized data set corresponding to one day of traffic for a major European mobile carrier with more than 3 million subscribers. We further take a principled approach to characterize mobile ad traffic along a number of dimensions, such as overall traffic, frequency, as well as possible implications in terms of energy on a mobile device.n Our analysis demonstrates a number of inefficiencies in todays ad delivery. We discuss the benefits of well-known techniques, such as pre-fetching and caching, to limit the energy and network signalling overhead caused by current systems. A prototype implementation on Android devices demonstrates an improvement of 50 % in terms of energy consumption for offline ad-sponsored apps while limiting the amount of ad related traffic.

Congestion-Aware Rate Adaptation in Wireless Networks: A Measurement-Driven Approach

Traditional rate adaptation solutions for IEEE 802.11 wireless networks perform poorly in congested networks. Measurement studies show that congestion in a wireless network leads to the use of lower transmission data rates and thus reduces overall network throughput and capacity. The lack of techniques to reliably identify and characterize congestion in wireless networks has prevented development of rate adaptation solutions that incorporate congestion information in their decision framework. To this end, our main contributions in this paper are two-fold. First, we present a technique that identifies and measures congestion in an 802.11 network in real time. Second, we design Wireless congestion Optimized Fallback (WOOF), a measurement-driven rate adaptation scheme for 802.11 devices that uses the congestion measurement to identify congestion related packet losses. Through experimental evaluation, we show that WOOF achieves up to 300% higher throughput in congested networks, compared to other well-known adaptation algorithms.

The Cost of the "S" in HTTPS

Increased user concern over security and privacy on the Internet has led to widespread adoption of HTTPS, the secure version of HTTP. HTTPS authenticates the communicating end points and provides confidentiality for the ensuing communication. However, as with any security solution, it does not come for free. HTTPS may introduce overhead in terms of infrastructure costs, communication latency, data usage, and energy consumption. Moreover, given the opaqueness of the encrypted communication, any in-network value added services requiring visibility into application layer content, such as caches and virus scanners, become ineffective. This paper attempts to shed some light on these costs. First, taking advantage of datasets collected from large ISPs, we examine the accelerating adoption of HTTPS over the last three years. Second, we quantify the direct and indirect costs of this evolution. Our results show that, indeed, security does not come for free. This work thus aims to stimulate discussion on technologies that can mitigate the costs of HTTPS while still protecting the users privacy.

Multi-Context TLS (mcTLS): Enabling Secure In-Network Functionality in TLS

A significant fraction of Internet traffic is now encrypted and HTTPS will likely be the default in HTTP/2. However, Transport Layer Security (TLS), the standard protocol for encryption in the Internet, assumes that all functionality resides at the endpoints, making it impossible to use in-network services that optimize network resource usage, improve user experience, and protect clients and servers from security threats. Re-introducing in-network functionality into TLS sessions today is done through hacks, often weakening overall security. In this paper we introduce multi-context TLS (mcTLS), which extends TLS to support middleboxes. mcTLS breaks the current all-or-nothing security model by allowing endpoints and content providers to explicitly introduce middleboxes in secure end-to-end sessions while controlling which parts of the data they can read or write. We evaluate a prototype mcTLS implementation in both controlled and live experiments, showing that its benefits come at the cost of minimal overhead. More importantly, we show that mcTLS can be incrementally deployed and requires only small changes to client, server, and middlebox software.

TailGate: handling long-tail content with a little help from friends

Distributing long-tail content is an inherently difficult task due to the low amortization of bandwidth transfer costs as such content has limited number of views. Two recent trends are making this problem harder. First, the increasing popularity of user-generated content (UGC) and online social networks (OSNs) create and reinforce such popularity distributions. Second, the recent trend of geo-replicating content across multiple PoPs spread around the world, done for improving quality of experience (QoE) for users and for redundancy reasons, can lead to unnecessary bandwidth costs. We build TailGate, a system that exploits social relationships, regularities in read access patterns, and time-zone differences to efficiently and selectively distribute long-tail content across PoPs. We evaluate TailGate using large traces from an OSN and show that it can decrease WAN bandwidth costs by as much as 80% as well as reduce latency, improving QoE. We deploy TailGate on PlanetLab and show that even in the case when imprecise social information is available, TailGate can still decrease the latency for accessing long-tail YouTube videos by a factor of 2.

Staying online while mobile: the hidden costs

Mobile phones in the 3G/4G era enable us to stay connected not only to the voice network, but also to online services like social networks. In this paper, we study the energy and network costs of mobile applications that provide continuous online presence (e.g. WhatsApp, Facebook, Skype). By combining measurements taken on the mobile and the cellular access network, we reveal a detailed picture of the mechanisms selected to implement online presence, along with their effect on handset energy consumption and network signaling traffic. We are surprised to find that simply having idle online presence apps on a mobile (that maintain connectivity in the background, with no user interaction) can drain the handset battery nine times more quickly. This high cost is partly due to online presence apps that are excessively ``chatty, in particular when their design philosophy stems from a similar desktop version. However, we also find that the cost of background app traffic is disproportionately large because of cross-layer interactions in which the traffic unintentionally triggers the promotion of cellular network states. Our experiments show that both of these effects can be overcome with careful implementation. We posit that a two-way push notification system, with messages being sent at a low (regular) frequency and low volume by a network-aware sender, can alleviate many of the costs.

Is there a case for mobile phone content pre-staging?

Content caching is a fundamental building block of the Internet. Caches are widely deployed at network edges to improve performance for end-users, and to reduce load on web servers and the backbone network. Considering mobile 3G/4G networks, however, the bottleneck is at the access link, where bandwidth is shared among all mobile terminals. As such, per-user capacity cannot grow to cope with the traffic demand. Unfortunately, caching policies would not reduce the load on the wireless link which would have to carry multiple copies of the same object that is being downloaded by multiple mobile terminals sharing the same access link. In this paper we investigate if it is worth to push the caching paradigm even farther. We hypothesize a system in which mobile terminals implement a local cache, where popular content can be pushed/pre-staged. This exploits the peculiar broadcast capability of the wireless channels to replicate content for free on all terminals, saving the cost of transmitting multiple copies of those popular objects. Relying on a large data set collected from a European mobile carrier, we analyse the content popularity characteristics of mobile traffic, and quantify the benefit that the push-to-mobile system would produce. We found that content pre-staging, by proactively and periodically broadcasting bundles of popular objects to devices, allows to both greatly i) improve users performance and ii) reduce up to 20% (40%) the downloaded volume (number of requests) in optimistic scenarios with a bundle of 100 MB. However, some technical constraints and content characteristics could question the actual gain such system would reach in practice.

RILAnalyzer: a comprehensive 3G monitor on your phone

The popularity of smartphones, cloud computing, and the app store model have led to cellular networks being used in a completely different way than what they were designed for. As a consequence, mobile applications impose new challenges in the design and efficient configuration of constrained networks to maximize applications performance. Such difficulties are largely caused by the lack of cross-layer under- standing of interactions between different entities -applications, devices, the network and its management plane. In this paper, we describe RILAnalyzer, an open-source tool that provides mechanisms to perform network analysis from within a mobile device. RILAnalyzer is capable of recording low-level radio information and accurate cellular net- work control-plane data, as well as user-plane data. We demonstrate how such data can be used to identify previously overlooked issues. Through a small user study across four cellular network providers in two European countries we infer how different network configurations are in reality and explore how such configurations interact with application logic, causing network and energy overheads.

Best paper -- Follow the money: understanding economics of online aggregation and advertising

The large-scale collection and exploitation of personal information to drive targeted online advertisements has raised privacy concerns. As a step towards understanding these concerns, we study the relationship between how much information is collected and how valuable it is for advertising. We use HTTP traces consisting of millions of users to aid our study and also present the first comparative study between aggregators. We develop a simple model that captures the various parameters of todays advertising revenues, whose values are estimated via the traces. Our results show that per aggregator revenue is skewed (5% accounting for 90% of revenues), while the contribution of users to advertising revenue is much less skewed (20% accounting for 80% of revenue). Google is dominant in terms of revenue and reach (presence on 80% of publishers). We also show that if all 5% of the top users in terms of revenue were to install privacy protection, with no corresponding reaction from the publishers, then the revenue can drop by 30%.

Can user-level probing detect and diagnose common home-WLAN pathologies

Common Wireless LAN (WLAN) pathologies include low signal-to-noise ratio, congestion, hidden terminals or interference from non-802.11 devices and phenomena. Prior work has focused on the detection and diagnosis of such problems using layer-2 information from 802.11 devices and special purpose access points and monitors, which may not be generally available. Here, we investigate a user-level approach: is it possible to detect and diagnose 802.11 pathologies with strictly user-level active probing, without any cooperation from, and without any visibility in, layer-2 devices? In this paper, we present preliminary but promising results indicating that such diagnostics are feasible.