User tracking has become ubiquitous practice on the Web, allowing services to recommend behaviorally targeted content to users. In this article, we design Alibi, a system that utilizes such readily available personalized content, generated by recommendation engines in real time, as a means to tame Sybil attacks. In particular, by using ads and other tracker-generated recommendations as implicit user “certificates,” Alibi is capable of creating meta-profiles that allow for rapid and inexpensive validation of users’ uniqueness, thereby enabling an Internet-wide Sybil defense service. We demonstrate the feasibility of such a system, exploring the aggregate behavior of recommendation engines on the Web and demonstrating the richness of the meta-profile space defined by such inputs. We further explore the fundamental properties of such meta-profiles, i.e., their construction, uniqueness, persistence, and resilience to attacks. By conducting a user study, we show that the user meta-profiles are robust and show important scaling effects. We demonstrate that utilizing even a moderate number of popular Web sites empowers Alibi to tame large-scale Sybil attacks.

Anycast is a popular tool for deploying global, widely available systems, including DNS infrastructure and content delivery networks (CDNs). The optimization of these networks often focuses on the deployment and management of anycast sites. However, such approaches fail to consider one of the primary configurations of a large anycast network: the set of networks that receive anycast announcements at each site (i.e., an announcement configuration). Altering these configurations, even without the deployment of additional sites, can have profound impacts on both anycast site selection and round-trip times.

Four major search engines, Google in particular, hold a unique position in enabling the use of the Internet, as they alone direct over 98% of Internet users to the content they seek, using proprietary indices. While the contribution of these companies is undeniable, their design is necessarily affected by their economic interests, which may or may not align with those of the users, raising concerns regarding their effect on the availability of information around the globe. While multiple academic and commercial projects aimed to distribute and democratize the Web search, they failed to gain much traction, mostly due to inferior results and lack of incentives for participation. In this paper, we show how complex networking-intensive tasks can be crowdsourced using Bitcoin's incentive model. We present Webcoin, a novel distributed digital-currency which utilizes networking resources rather then computational, and can only be mined through Web indexing. Webcoin provides both the incentives and the means to create Google-scale indices, freely available to competing services and the public. Webcoin's design overcomes numerous unique challenges, such as index verification, scalability, and nodes' ability to actively manipulate webpages. We deploy 200 fully-functioning Webcoin nodes and demonstrate their low bandwidth requirements.

Currently, the attempt to choose the "best" content replica server for a client is carried out solely by CDNs. While CDNs have a decent view of load distribution and content placement, they receive little input from the clients themselves. We propose a hybrid solution, subnet assimilation, where the client participates in the server selection process while still leaving the final say to the CDN. Subnet assimilation allows clients to declare their own "network location," different from the actual one, which in turn drives a CDN towards making better decisions. To demonstrate, we introduce Drongo, a client-side system, readily deployable on existing clients without any changes to the CDNs, that employs subnet assimilation to dramatically improve replica server selection. We implemented and extensively evaluated Drongo on a set of 429 clients spread across 177 countries and 6 major CDNs. We show that Drongo affects 69.93% of all clients, prompting better CDN replica choices which reduce the latency of affected requests by up to an order of magnitude and by 24.89% on average across six major providers, with Google's performance improving by 50% in the median case. Our results indicate that client participation holds great opportunities for the advancement of CDN performance.

Web performance has long proved to be one of the most sought after and difficult to achieve components for the web. Since the inception of the modern web infrastructure, the situation has been growing in complexity, adding remote hosts and objects, providing everything from computation infrastructure, content distribution capability, and targeted advertising. While many of these components provide improvements for some users, the complexity of the Internet often leaves other users suffering from poor performance. We propose Oak, a system which addresses client performance on the individual level, hence addressing challenges which may be unique to the user. Oak measures a user's performance for objects loading on a page, and determines which components are under-performing. Oak further provides an automated mechanism by which sites are able to replace resources with those provided by a better performing alternative service for a particular user. In this work, we demonstrate the prevalence of under-performing services on the web, finding that over 60% of the Alexa Top 500 have at least one under-preforming server. We further evaluate Oak on experimental and popular existing webpages, and demonstrate its effectiveness in making decisions in existing environments and with a distributed user base.

Large-scale cloud networks are constantly driven by the need for improved performance in communication between datacenters. Indeed, such back-office communication makes up a large fraction of traffic in many cloud environments. This communication often occurs frequently, carrying control messages, coordination and load balancing information, and customer data. However, ensuring such inter-datacenter traffic is delivered efficiently requires optimizing connections over large physical distances, which is non-trivial. Worse still, many large cloud networks are subject to complex configuration and administrative restrictions, limiting the types of solutions that can be implemented. In this paper, we propose improving the efficiency of datacenter to datacenter communication by learning the congestion level of links in between. We then use this knowledge to inform new connections made between the relevant datacenters, allowing us to eliminate the overhead associated with traditional slow-start processes in new connections. We further present Riptide, a tool which implements this approach. We present the design and implementation details of Riptide, showing that it can be easily executed on modern Linux servers deployed in the real world. We further demonstrate that it successfully reduces total transfer times in a production global-scale content delivery network (CDN), providing up to a 30% decrease in tail latency. We further show that Riptide is simple to deploy and easy to maintain within a complex existing network.

Enabling communication between routers and endpoints has long been sought after as an approach to congestion control in the Internet. However, the narrow-waist of TCP/IP has complicated the deployment of such communication. In this paper, we present Kick-Ass1, a congestion control mechanism that enables explicit rate congestion control protocols to be deployed within the TCP/IP stack. The key idea is to utilize packet lengths as a vehicle to communicate fine-grained explicit rate and other information from routers to endpoints and vice versa. Given that our approach (i) requires no explicit coordination among Kick-Ass routers, (ii) no explicit coordination among Kick-Ass routers and endpoints, and (iii) is effective on paths that include legacy routers, it provides a practical road towards a faster Internet, today. Using large-scale simulations, testbed experiments, and wide-area Internet evaluations, we demonstrate that (i) a basic explicit-rate protocol using the Kick-Ass mechanism improves flow completion times by up to an order of magnitude and outperforms endpoint-based approaches, including CUBIC and PCC. (ii) Kick-Ass is incrementally deployable on the Internet. (iii) Deploying Kick-Ass at end-hosts and edge routers can enable the above performance benefits, without waiting for universal adoption. (iv) Our packet-fragmentation mechanism is well behaved on the Internet.

FM radio, typically broadcast in the 87.5 to 108.0Mhz range, is widely available in urban areas and beyond. Contrary to GPS, it effectively penetrates buildings, contrary to 3G/4G or TV, FM radio receivers are becoming freely available in mobile devices. Indeed, nearly every smart phone and many other consumer electronics today have a built-in FM chip. In this paper, we demonstrate that this ubiquitous in-the-air and on-device FM radio availability presents a unique opportunity to address some of the fundamental wireless networking problems. In particular, we focus on the problem commonly arising in home networks where devices from neighboring, yet autonomous and non-collaborative, Wi-Fi networks systematically "step on each other's feet", i.e., interfere and degrade each other's performance. We show that the digital signal that accompanies broadcast FM radio has sufficient structure to enable effective scheduling relative to it. It thus provides a common reference for neighboring devices to harmonize their transmissions, yet without requiring any explicit communication among them. To the best of our knowledge, our system is the first to enable such mutually-beneficial, autonomous, and implicit harmonization among Wi-Fi devices across administrative network bounds.