Video analytics will drive a wide range of applications with great potential to impact society. A geographically distributed architecture of public clouds and edges that extend down to the cameras is ...the only feasible approach to meeting the strict real-time requirements of large-scale live video analytics.
IP multicast applications such as live lecture broadcasts are being increasingly used in enterprise and campus networks. In many cases, end hosts access these multicast streams using Wi-Fi networks. ...However, multicast over Wi-Fi suffers from several well-known problems such as low data rate, high losses and unfairness vis-a-vis other contending unicast transmissions. In this paper we present DirCast, a system to solve many of these problems. DirCast requires no changes to the 802.11 MAC protocol or the wireless access points. Software changes are required on clients only if they wish to participate in multicast sessions. The aim of DirCast system is to minimize the airtime consumed by the multicast traffic, while simultaneously improving client experience. To meet these goals, the DirCast converts multicast packets to unicast packets targeted to certain selected clients; other clients receive these packets by listening in promiscuous mode. The target clients are carefully selected to minimize loss rate experienced by the non-targeted clients. If necessary, clients are forced to change the AP they are associated with. In addition, DirCast uses proactive adaptive FEC to further reduce the loss rate and implements a novel virtual multicast interface in order to be compatible with the security needs of the enterprise. We demonstrate the effectiveness of DirCast using extensive experiments in a Wi-Fi prototype implementation and through large-scale simulations.
VTrack Thiagarajan, Arvind; Ravindranath, Lenin; LaCurts, Katrina ...
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems,
11/2009
Conference Proceeding
Traffic delays and congestion are a major source of inefficiency, wasted fuel, and commuter frustration. Measuring and localizing these delays, and routing users around them, is an important step ...towards reducing the time people spend stuck in traffic. As others have noted, the proliferation of commodity smartphones that can provide location estimates using a variety of sensors---GPS, WiFi, and/or cellular triangulation---opens up the attractive possibility of using position samples from drivers' phones to monitor traffic delays at a fine spatiotemporal granularity. This paper presents VTrack, a system for travel time estimation using this sensor data that addresses two key challenges: energy consumption and sensor unreliability. While GPS provides highly accurate location estimates, it has several limitations: some phones don't have GPS at all, the GPS sensor doesn't work in "urban canyons" (tall buildings and tunnels) or when the phone is inside a pocket, and the GPS on many phones is power-hungry and drains the battery quickly. In these cases, VTrack can use alternative, less energy-hungry but noisier sensors like WiFi to estimate both a user's trajectory and travel time along the route. VTrack uses a hidden Markov model (HMM)-based map matching scheme and travel time estimation method that interpolates sparse data to identify the most probable road segments driven by the user and to attribute travel times to those segments. We present experimental results from real drive data and WiFi access point sightings gathered from a deployment on several cars. We show that VTrack can tolerate significant noise and outages in these location estimates, and still successfully identify delay-prone segments, and provide accurate enough delays for delay-aware routing algorithms. We also study the best sampling strategies for WiFi and GPS sensors for different energy cost regimes.
The number of smartphones shipped in 2014 will be four times larger than the number of PCs. Compared to PCs, smartphones have limited computing resources, and smartphone applications are more prone ...to performance problems. Traditionally, developers use profilers to detect performance problems by running applications with relatively large inputs. Unfortunately, for smartphone applications, the developer cannot easily control the input, because smartphone applications interact heavily with the environment.
Given a run on a small input, how can a developer detect performance problems that would occur for a run with large input? We present SUNCAT, a novel technique that helps developers understand and predict performance problems in smartphone applications. The developer runs the application using a common input, typically small, and SUNCAT presents a prioritized list of repetition patterns that summarize the current run plus additional information to help the developer understand how these patterns may grow in the future runs with large inputs. We implemented SUNCAT for Windows Phone systems and used it to understand the performance characteristics of 29 usage scenarios in 5 popular applications. We found one performance problem that was confirmed and fixed, four problems that were confirmed, one confirmed problem that was a duplicate of an older report, and three more potential performance problems that developers agree may be improved.
Automatic and scalable fault detection for mobile applications Ravindranath, Lenin; Nath, Suman; Padhye, Jitendra ...
Proceedings of the 12th annual international conference on Mobile systems, applications, and services,
06/2014
Conference Proceeding
Odprti dostop
This paper describes the design, implementation, and evaluation of VanarSena, an automated fault finder for mobile applications (``apps''). The techniques in VanarSena are driven by a study of 25 ...million real-world crash reports of Windows Phone apps reported in 2012. Our analysis indicates that a modest number of root causes are responsible for many observed failures, but that they occur in a wide range of places in an app, requiring a wide coverage of possible execution paths. VanarSena adopts a ``greybox'' testing method, instrumenting the app binary to achieve both coverage and speed. VanarSena runs on cloud servers: the developer uploads the app binary; VanarSena then runs several app ``monkeys'' in parallel to emulate user, network, and sensor data behavior, returning a detailed report of crashes and failures. We have tested VanarSena with 3000 apps from the Windows Phone store, finding that 1108 of them had failures; VanarSena uncovered 2969 distinct bugs in existing apps, including 1227 that were not previously reported. Because we anticipate VanarSena being used in regular regression tests, testing speed is important. VanarSena uses two techniques to improve speed. First, it uses a ``hit testing'' method to quickly emulate an app by identifying which user interface controls map to the same execution handlers in the code. Second, it generates a ProcessingCompleted event to accurately determine when to start the next interaction. These features are key benefits of VanarSena's greybox philosophy.
Beacon-Stuffing: Wi-Fi without Associations Chandra, R.; Padhye, J.; Ravindranath, L. ...
Eighth IEEE Workshop on Mobile Computing Systems and Applications,
2007-March
Conference Proceeding
The design of Wi-Fi networks mimics the behavior of wired networks. For example, a Wi-Fi network interface can send and receive packets only after associating to a Wi-Fi access point (AP). In this ...paper, we show that the concept of association limits the capabilities of wireless networks. We present a scheme, called beacon-stuffing, that allows Wi-Fi clients to communicate without associating to any network. Beacon-stuffing enables several new applications over Wi-Fi networks, and we describe three of them in this paper: improved AP selection, delivering location-specific advertisements, and providing coupons over Wi-Fi networks without requiring the Wi-Fi client to associate to any AP.
With users increasingly dependent on their phones, tablets, and wearables, the mobile app ecosystem is more important today than ever before. Creating and distributing apps has never been more ...accessible. Even single developers can now reach global audiences. But mobile apps must cope with extremely varied and dynamic operating conditions due to factors like diverse device characteristics, wireless network heterogeneity, and varied user behavior. App developers and operators of app marketplaces both lack testing tools that can effectively account for such diversity and, as a result, app failures and performance bugs (like excessive energy consumption) are commonly found today. To address this challenge to mobile app development, we have developed key techniques for scalable automated mobile app testing within two prototype services --- VanarSena and Caiipa. In this paper, we describe our vision for SMASH, a unified cloud-based mobile app testing service that combines the strengths of both previous systems to tackle the complexities presently faced by testers of mobile apps.
AppInsight Ravindranath, Lenin; Padhye, Jitendra; Agarwal, Sharad ...
Proceedings of the 10th USENIX conference on Operating Systems Design and Implementation,
10/2012
Conference Proceeding
The mobile-app marketplace is highly competitive. To maintain and improve the quality of their apps, developers need data about how their app is performing in the wild. The asynchronous, ...multithreaded nature of mobile apps makes tracing difficult. The difficulties are compounded by the resource limitations inherent in the mobile platform. To address this challenge, we develop AppInsight, a system that instruments mobile-app binaries to automatically identify the critical path in user transactions, across asynchronous-call boundaries. AppInsight is lightweight, it does not require any input from the developer, and it does not require any changes to the OS. We used AppInsight to instrument 30 marketplace apps, and carried out a field trial with 30 users for over 4 months. We report on the characteristics of the critical paths that AppInsight found in this data. We also give real-world examples of how AppInsight helped developers improve the quality of their app.
Timecard Ravindranath, Lenin; Padhye, Jitendra; Mahajan, Ratul ...
Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles,
11/2013
Conference Proceeding
Odprti dostop
Providing consistent response times to users of mobile applications is challenging because there are several variable delays between the start of a user's request and the completion of the response. ...These delays include location lookup, sensor data acquisition, radio wake-up, network transmissions, and processing on both the client and server. To allow applications to achieve consistent response times in the face of these variable delays, this paper presents the design, implementation, and evaluation of the Timecard system. Timecard provides two abstractions: the first returns the time elapsed since the user started the request, and the second returns an estimate of the time it would take to transmit the response from the server to the client and process the response at the client. With these abstractions, the server can adapt its processing time to control the end-to-end delay for the request. Implementing these abstractions requires Timecard to track delays across multiple asynchronous activities, handle time skew between client and server, and estimate network transfer times. Experiments with Timecard incorporated into two mobile applications show that the end-to-end delay is within 50 ms of the target delay of 1200 ms over 90% of the time.
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