De-Yu Chen

GamingAnywhere: The first open source cloud gaming system

We present the first open source cloud gaming system, called GamingAnywhere. In addition to its openness, we have designed, GamingAnywhere for high extensibility, portability, and reconfigurability. We implemented it on Windows, Linux, OS X, and Android. We conducted extensive experiments to evaluate its performance. Our experimental results indicate that GamingAnywhere is efficient, scalable, adaptable to network conditions, and achieves high responsiveness and streaming quality. GamingAnywhere can be employed by researchers, game developers, service providers, and end users for setting up cloud gaming testbeds, which we believe, will stimulate more research into innovations for cloud gaming systems and applications.

On the Quality of Service of Cloud Gaming Systems

Cloud gaming, i.e., real-time game playing via thin clients, relieves users from being forced to upgrade their computers and resolve the incompatibility issues between games and computers. As a result, cloud gaming is generating a great deal of interests among entrepreneurs, venture capitalists, general publics, and researchers. However, given the large design space, it is not yet known which cloud gaming system delivers the best user-perceived Quality of Service (QoS) and what design elements constitute a good cloud gaming system. This study is motivated by the question: How good is the QoS of current cloud gaming systems? Answering the question is challenging because most cloud gaming systems are proprietary and closed, and thus their internal mechanisms are not accessible for the research community. In this paper, we propose a suite of measurement techniques to evaluate the QoS of cloud gaming systems and prove the effectiveness of our schemes using a case study comprising two well-known cloud gaming systems: OnLive and StreamMyGame. Our results show that OnLive performs better, because it provides adaptable frame rates, better graphic quality, and shorter server processing delays, while consuming less network bandwidth. Our measurement techniques are general and can be applied to any cloud gaming systems, so that researchers, users, and service providers may systematically quantify the QoS of these systems. To the best of our knowledge, the proposed suite of measurement techniques have never been presented in the literature.

Placing Virtual Machines to Optimize Cloud Gaming Experience

Optimizing cloud gaming experience is no easy task due to the complex tradeoff between gamer quality of experience (QoE) and provider net profit. We tackle the challenge and study an optimization problem to maximize the cloud gaming providers total profit while achieving just-good-enough QoE. We conduct measurement studies to derive the QoE and performance models. We formulate and optimally solve the problem. The optimization problem has exponential running time, and we develop an efficient heuristic algorithm. We also present an alternative formulation and algorithms for closed cloud gaming services with dedicated infrastructures, where the profit is not a concern and overall gaming QoE needs to be maximized. We present a prototype system and testbed using off-the-shelf virtualization software, to demonstrate the practicality and efficiency of our algorithms. Our experience on realizing the testbed sheds some lights on how cloud gaming providers may build up their own profitable services. Last, we conduct extensive trace-driven simulations to evaluate our proposed algorithms. The simulation results show that the proposed heuristic algorithms: (i) produce close-to-optimal solutions, (ii) scale to large cloud gaming services with 20,000 servers and 40,000 gamers, and (iii) outperform the state-of-the-art placement heuristic, e.g., by up to 3.5 times in terms of net profits.

Quantifying User Satisfaction in Mobile Cloud Games

We conduct real experiments to quantify user satisfaction in mobile cloud games using a real cloud gaming system built on the open-sourced GamingAnywhere. We share our experiences in porting GamingAnywhere client to Android OS and perform extensive experiments on both the mobile and desktop clients. The experiment results reveal several new insights: (1) gamers are more satisfied with the graphics quality on mobile devices, while they are more satisfied with the control quality on desktops, (2) the bitrate, frame rate, and network delay significantly affect the graphics and smoothness quality, and (3) the control quality only depends on the client type (mobile versus desktop). To the best of our knowledge, such user studies have never been done in the literature.

GamingAnywhere: an open-source cloud gaming testbed

While cloud gaming opens new business opportunity, it also poses tremendous challenges as the Internet only provides best-effort service and gamers are hard to please. Although researchers have various ideas to improve cloud gaming systems, existing cloud gaming systems are closed and proprietary, and cannot be used to evaluate these ideas. We present GamingAnywhere, the first open-source cloud gaming system, which is extensible, portable, and configurable. GamingAnywhere may be used by: (i) researchers and engineers to implement and test their new ideas, (ii) service providers to develop cloud gaming services, and (iii) gamers to set up private cloud gaming systems. Details on GamingAnywhere are given in this paper. We firmly believe GamingAnywhere will stimulate future studies on cloud gaming and real-time interactive distributed systems.

Screencast in the Wild: Performance and Limitations

Displays without associated computing devices are increasingly more popular, and the binding between computing devices and displays is no longer one-to-one but more dynamic and adaptive. Screencast technologies enable such dynamic binding over ad hoc one-hop networks or Wi-Fi access points. In this paper, we design and conduct the first detailed measurement study on the performance of state-of-the-art screencast technologies. By varying the user demands and network conditions, we find that Splashtop and Miracast outperform other screencast technologies under typical setups. Our experiments also show that the screencast technologies either: (i) do not dynamically adjust bitrate or (ii) employ a suboptimal adaptation strategy. The developers of future screencast technologies are suggested to pay more attentions on the bitrate adaptation strategy, e.g., by leveraging cross-layer optimization paradigm.