Oriana Riva

PocketWeb: instant web browsing for mobile devices

The high network latencies and limited battery life of mobile phones can make mobile web browsing a frustrating experience. In prior work, we proposed trading memory capacity for lower web access latency and a more convenient data transfer schedule from an energy perspective by prefetching slowly-changing data (search queries and results) nightly, when the phone is charging. However, most web content is intrinsically much more dynamic and may be updated multiple times a day, thus eliminating the effectiveness of periodic updates.n This paper addresses the challenge of prefetching dynamic web content in a timely fashion, giving the user an instant web browsing experience but without aggravating the battery lifetime issue. We start by analyzing the web access traces of 8,000 users, and observe that mobile web browsing exhibits a strong spatiotemporal signature, which is different for every user. We propose to use a machine learning approach based on stochastic gradient boosting techniques to efficiently model this signature on a per user basis. The machine learning model is capable of accurately predicting future web accesses and prefetching the content in a timely manner. Our experimental evaluation with 48,000 models trained on real user datasets shows that we can accurately prefetch 60% of the URLs for about 80-90% of the users within 2 minutes before the request. The system prototype we built not only provides more than 80% lower web access time for more than 80% of the users, but it also achieves the same or lower radio energy dissipation by more than 50% for the majority of mobile users.

Goldilocks and the two mobile devices: going beyond all-or-nothing access to a device's applications

Most mobile phones and tablets support only two access control device states: locked and unlocked. We investigated how well all or-nothing device access control meets the need of users by interviewing 20 participants who had both a smartphone and tablet. We find all-or-nothing device access control to be a remarkably poor fit with users preferences. On both phones and tablets, participants wanted roughly half their applications to be available even when their device was locked and half protected by authentication. We also solicited participants interest in new access control mechanisms designed specifically to facilitate device sharing. Fourteen participants out of 20 preferred these controls to existing security locks alone. Finally, we gauged participants interest in using face and voice biometrics to authenticate to their mobile phone and tablets; participants were surprisingly receptive to biometrics, given that they were also aware of security and reliability limitations.

Dynamic software deployment from clouds to mobile devices

With the functionality of mobile applications ever increasing, designers are often confronted with either the resource limitations of the devices or of the network. As pointed out by recent work, application partitioning between mobile devices and clouds, can be used to solve some of these issues, improving performance and/or battery life. In this paper, we argue that the static decisions made in existing work cannot leverage the full potential of application partitioning. Thus, to allow for variations in the execution environment, we have developed a system that dynamically adapts the application partition decisions. The system works by continuously profiling an applications performance and dynamically updating its distributed deployment to accommodate changes in the network bandwidth, devices CPU utilization, and data loads. Using several real applications, we show that our approach provides performance gains as high as 75% over traditional approaches and achieves lower power consumption by a factor close to 45%.

Prefetching mobile ads: can advertising systems afford it?

Mobile app marketplaces are dominated by free apps that rely on advertising for their revenue. These apps place increased demands on the already limited battery lifetime of modern phones. For example, in the top 15 free Windows Phone apps, we found in-app advertising contributes to 65% of the apps total communication energy (or 23% of the apps total energy). Despite their small size, downloading ads each time an app is started and at regular refresh intervals forces the network radio to be continuously woken up, thus leading to a high energy overhead, so-called tail energy problem. A straightforward mechanism to lower this overhead is to prefetch ads in bulk and serve them locally. However, the prefetching of ads is at odds with the real-time nature of modern advertising systems wherein ads are sold through real-time auctions each time the client can display an ad.n This paper addresses the challenge of supporting ad prefetching with minimal changes to the existing advertising architecture. We build client models predicting how many ad slots are likely to be available in the future. Based on this (unreliable) estimate, ad servers make client ad slots available in the ad exchange auctions even before they can be displayed. In order to display the ads within a short deadline, ads are probabilistically replicated across clients, using an overbooking model designed to ensure that ads are shown before their deadline expires (SLA violation rate) and are shown no more than required (revenue loss). With traces of over 1,700 iPhone and Windows Phone users, we show that our approach can reduce the ad energy overhead by over 50% with a negligible revenue loss and SLA violation rate.

Policy expressivity in the Anzere personal cloud

We present a technique for partially replicating data items at scale according to expressive policy specifications. Recent projects have addressed the challenge of policy-based replication of personal data (photos, music, etc.) within a network of devices, as an alternative to centralized online services. To date, the policies supported by such systems have been relatively simple, in order to facilitate scaling the policy calculation to large numbers of items.n In this paper, we show how such replication systems can scale while supporting much more expressive policies than previous schemes: item replication expressed as constraints, devices referred to by predicates rather than explicitly named, and replication to storage nodes acquired on-demand from the cloud. These extensions introduce considerable complexity in policy evaluation, but we show a system can scale well by using equivalence classes to reduce the problem space. We validate our approach via deployment on an ensemble of devices (phones, PCs, cloud virtual machines, etc.), and show that it supports rich policies and high data volumes using simulations and real data based on personal usage in our group.

uLink: Enabling User-Defined Deep Linking to App Content

Web deep links are instrumental to many fundamental user experiences such as navigating to one web page from another, bookmarking a page, or sharing it with others. Such experiences are not possible with individual pages inside mobile apps, since historically mobile apps did not have links equivalent to web deep links. Mobile deep links, introduced in recent years, still lack many important properties of web deep links. Unlike web links, mobile deep links need significant developer effort, cover a small number of predefined pages, and are defined statically to navigate to a page for a given link, but not to dynamically generate a link for a given page. We propose uLink, a novel deep linking mechanism that addresses these problems. uLink is implemented as an application library, which transparently tracks data- and UI-event-dependencies of app pages, and encodes the information in links to the pages; when a link is invoked, the information is utilized to recreate the target page quickly and accurately. uLink also employs techniques, based on static and dynamic analysis of the app, that can provide feedback to users about whether a link may break in the future due to, e.g., modifications of external resources such as a file the link depends on. We have implemented uLink on Android. Our evaluation with 34 (of 1000 most downloaded) Android apps shows that compared to existing mobile deep links, uLink requires minimal developer effort, achieves significantly higher coverage, and can provide accurate user feedback on a broken link.

Appstract: on-the-fly app content semantics with better privacy

Services like Google Now on Tap and Bing Snapp enable new user experiences by understanding the semantics of contents that users consume in their apps. These systems send contents of currently displayed app pages to the cloud to identify relevant entities (e.g., a movie) appearing in the current page and show information related to such entities (e.g., local theaters playing the movie). These new experiences come with privacy concerns as they can send sensitive on-screen data (bank details, medical data, etc.) to the cloud. We propose a novel approach that efficiently extracts app content semantics on the device, without exfiltrating user data. Our solution consists of two phases: an offline, user-agnostic, in-cloud phase that automatically annotates apps UI elements with stable semantics, and a lightweight on-device phase that assigns semantics to captured app contents on the fly, by matching the annotations. With this automatic approach we annotated 100+ food, dining, and music apps, with accuracy over 80%. Our system implementation for Android and Windows Phone---Appstract---incurs minimal runtime overhead. We built eight use cases on the Appstract framework.

PocketTrend: Timely Identification and Delivery of Trending Search Content to Mobile Users

Trending search topics cause unpredictable query load spikes that hurt the end-user search experience, particularly the mobile one, by introducing longer delays. To understand how trending search topics are formed and evolve over time, we analyze 21 million queries submitted during periods where popular events caused search query volume spikes. Based on our findings, we design and evaluate PocketTrend, a system that automatically detects trending topics in real time, identifies the search content associated to the topics, and then intelligently pushes this content to users in a timely manner. In that way, PocketTrend enables a client-side search engine that can instantly answer user queries related to trending events, while at the same time reducing the impact of these trends on the datacenter workload. Our results, using real mobile search logs, show that in the presence of a trending event, up to 13-17% of the overall search traffic can be eliminated from the datacenter, with as many as 19% of all users benefiting from PocketTrend.

Taking data exposure into account: how does it affect the choice of sign-in accounts?

Online services collect personal data from their users, sometimes with no clear need. We studied how users sign-in to web sites using federated IDs, and found that most survey respondents were not aware of the data they expose. However, when presented with the tradeoffs behind each sign-in option, respondents reported a willingness to change how they sign-in to reduce their data exposure or, in fewer cases, to increase it to receive more benefits from the service. Our findings suggest that data exposure is a concern for users, and that there is a need for finding clearer ways for communicating it for each sign-in option.

Kite: Building Conversational Bots from Mobile Apps

Task-oriented chatbots allow users to carry out tasks (e.g., ordering a pizza) using natural language conversation. The widely-used slot-filling approach for building bots of this type requires significant hand-coding, which hinders scalability. Recently, neural network models have been shown to be capable of generating natural chitchat conversations, but it is unclear whether they will ever work for task modeling. Kite is a practical system for bootstrapping task-oriented bots, leveraging both approaches above. Kites key insight is that while bots encapsulate the logic of user tasks into conversational forms, existing apps encapsulate the logic of user tasks into graphical user interfaces. A developer demonstrates a task using a relevant app, and from the collected interaction traces Kite automatically derives a task model, a graph of actions and associated inputs representing possible task execution paths. A task model represents the logical backbone of a bot, on which Kite layers a question-answer interface generated using a hybrid rule-based and neural network approach. Using Kite, developers can automatically generate bot templates for many different tasks. In our evaluation, it extracted accurate task models from 25 popular Android apps spanning 15 tasks. Appropriate questions and high-quality answers were also generated. Our developer study suggests that developers, even without any bot developing experience, can successfully generate bot templates using Kite.