Thanks to the “on-demand” nature of cloud computing, a large number of applications have been recently migrated to the cloud. To take full advantage of superior connectivities between geo-distributed datacenters, application traffic can be “ferried” through the cloud to provide better service and user experience. However, implementing and deploying such inter-datacenter protocols for various applications, such as messaging, streaming and conferencing, are not without challenges, due to complex requirements of applications and unique characteristics of datacenters. In order to simplify the design and implementation of new inter-datacenter protocols, we design and implement a new system framework, called Bellini, in this paper. Bellini provides customizable elements shared by the new category of inter-datacenter protocols, including a variety of transport protocols, routing policies, and rate allocation strategies. Bellini is also optimized to perform well in virtual machines, utilizing available resources efficiently. With case studies on video conferencing and messaging, we demonstrate the benefits of Bellini when it comes to designing and evaluating new inter-datacenter protocols to serve the needs of cloud-based applications.

With the proliferation of mobile devices in both smartphone and tablet form factors, it is intuitive and natural for users to socially interact with their collaborators or competitors in multi-party conferencing, productivity, or gaming applications. In this paper, we make a case that such social interactions should be much more spontaneous to users in these applications. We design and implement a new system framework, Reflex, to provide the required system support to achieve spontaneous social interaction with other users in the same mobile application, be they in the same living room or around the world. Reflex features a simple and intuitive application programming interface (API), and uses cloud computing services from Google App Engine to offer the scalability and performance required to support spontaneous social networking at a large scale. Reflex is able to transparently switch to local interactions over Bluetooth or Wi-Fi interfaces, available on mobile devices, whenever possible. In order to evaluate Reflex in the iOS platform, we developed a real-world music composition application, called MusicScore, from scratch on the iPad, which takes advantage of Reflex to let music composers collaborate in real time.

In this paper, we present our design of MusicScore, a professional grade application on the iOS platform for music composition and live performance tracking, used by composers and amateurs alike. As its foundation, we have designed and implemented a high-quality music engraver, capable of real-time interactive rendering on mobile devices, as well as an intuitive user interface based on multi-touch, both built from scratch using Objective-C and Cocoa Touch. To make MusicScore appealing to the general population for their practice and fun, we have introduced a unique auditory capability to MusicScore, so that it can "listen" to and analyze live instrument performance in real time. In order to compensate for the imperfect audio sensing system on mobile devices, we have proposed a collaborative sensing solution to better capture music signals in real time. To maximize the accuracy of live progress tracking and performance evaluation using a mobile device, we have designed a collection of note detection and tempo-based note matching algorithms, using a combination of microphone and accelerometer sensors. Based on our real-world implementation of MusicScore, extensive evaluation results show that MusicScore can achieve acceptably low error ratios, even for music pieces performed by highly inexperienced players.

With the proliferation of multi-touch mobile devices, such as smartphones and tablets, users interact with devices in non-conventional gesture-intensive ways. As a new way to interact with mobile devices, gestures have been proven to be intuitive and natural with a minimal learning curve, and can be used in interactive multimedia applications. In order for multiple users to collaborate in an interactive manner, we propose that gestures can be streamed in multiple broadcast sessions, with each session corresponding to one of the users as the source of a gesture stream. During the interactive session, the Quality of Experience (QoE) of mobile users hinges upon delays from when gestures are entered by the source to when they are recognized by each of the receivers, which we refer to as gesture recognizing delays. In this paper, we present the design of GestureFlow, a gesture broadcast protocol designed specifically for concurrent gesture streams in multiple broadcast sessions, such that the gesture recognizing delay in each session is minimized. We motivate the effectiveness and practicality of using inter-session network coding, and address challenges introduced by the linear dependence of coded packets. We evaluate our protocol design using an extensive array of real-world experiments on mobile devices, involving a new gesture-intensive interactive multimedia application, called MusicScore, that we developed from scratch.

Multi-touch mobile devices (e.g. iPhone and iPad) and motion-sensing game controllers (e.g. Kinect for Xbox 360) share one common feature: users interact with computing devices in non-conventional gesture-intensive ways, be they multi-touch gestures on the iPad or body motion gestures with the Kinect. As a new way to interact with computing devices, gestures have been proven to be intuitive and natural, with very minimal learning curve. They can be used in applications beyond games, such as those that allow the creation of artistic and musical content in a collaborative fashion. In order for multiple users to collaborate or compete in real time, however, such gestures need to be streamed in multiple broadcast sessions with an “all-to-all” broadcast nature, with each session corresponding to one of users as a source of a gesture stream. These streams of gestures typically incur low yet bursty bit rates, but have unique requirements with respect to delay and loss. In this paper, we present the design of GestureFlow, a gesture broadcast protocol designed specifically for concurrent gesture streams in multiple broadcast sessions. We motivate the effectiveness and practicality of using inter-session network coding, and address challenges introduced by linear dependence, discovered in our extensive experiments involving a new gesture-intensive iPad application that we developed from scratch.

Peer-assisted on-demand video streaming services are extremely large-scale distributed systems on the Internet. Automated demand forecast and performance prediction, if implemented, can help with capacity planning and quality control so that sufficient server bandwidth can always be supplied to each video channel without incurring wastage. In this paper, we use time-series analysis techniques to automatically predict the online population, the peer upload and the server bandwidth demand in each video channel, based on the learning of both human factors and system dynamics from online measurements. The proposed mechanisms are evaluated on a large dataset collected from a commercial Internet video-on-demand system.

With the advent of mobile devices with large displays, it is intuitive and natural for users to interact with an application on a mobile device using multi-touch gestures. In this paper, we propose that these multi-touch gestures can be streamed on-the-fly among multiple participating users, making it possible to engage users in a collaborative or competitive experience. Such multi-touch streams, featuring very low streaming bit rates, can be rendered on receivers to precisely reconstruct the states of an application. We present the challenges, system framework, embedded algorithm design, and real-world evaluation of TouchTime, a new system that has been designed from scratch to facilitate the streaming of multi-touch gestures among multiple users. By seamlessly combining local computation on mobile devices and services from the "cloud," we explore the design space of suitable mechanisms to represent and packetize multi-touch gestures, and of practical protocols to transport concurrent live multi-touch streams over the Internet. Specifically, we propose an auction-based reflector selection algorithm to achieve the minimal end-to-end delay in a live multi-touch streaming session. To demonstrate TouchTime, we have developed a new real-world music composition application --- called MusicScore --- using the Apple iPad Programming SDK, and used it as our running example and experimental testbed to evaluate our design choices and implementation of TouchTime.

Since the inception of network coding in information theory, we have witnessed a sharp increase of research interest in its applications in communications and networking, where the focus has been on more practical aspects. However, thus far, network coding has not been deployed in real-world commercial systems in operation at a large scale, and in a production setting. In this paper, we present the objectives, rationale, and design in the first production deployment of random network coding, where it has been used in the past year as the cornerstone of a large-scale production on-demand streaming system, operated by UUSee Inc., delivering thousands of on-demand video channels to millions of unique visitors each month. To achieve a thorough understanding of the performance of network coding, we have collected 200 Gigabytes worth of real-world traces throughout the 17-day Summer Olympic Games in August 2008, and present our lessons learned after an in-depth trace-driven analysis.

In large-scale peer-to-peer (P2P) live streaming systems with a limited supply of server bandwidth, increasing the amount of upload bandwidth supplied by peers becomes critically important to the "well being" of streaming sessions in live channels. Intuitively, two types of peers are preferred to be kept up in a live session: peers that contribute a higher percentage of their upload capacities, and peers that are stable for a long period of time. The fundamental challenge is to identify, and satisfy the needs of, these types of "superior" peers in a live session, and to achieve this goal with minimum disruption to the traditional pull-based protocols that real-world live streaming protocols use. In this paper, we conduct a comprehensive and in-depth statistical analysis based on more than 130 GB worth of runtime traces from hundreds of streaming channels in a large- scale real-world live streaming system, UUSee (among the top three commercial systems in popularity in mainland China). Our objective is to discover critical factors that may influence the longevity and bandwidth contribution ratio of peers, using survival analysis techniques such as the Cox proportional hazards model and the Mantel-Haenszel test. Once these influential factors are found, they can be used to form a superiority index to distill superior peers from the general peer population. The index can be used in any way to favor superior peers, and we simulate the use of a simple ranking mechanism in a natural selection algorithm to show the effectiveness of the index, based on a replay of real-world traces from UUSee.