Optimizing Communication Energy Efficiency for a Multimedia Application

Abstract: Mobile devices have evolved rapidly in recent years and increased usage and performance are pushing contemporary battery technology to its limits. The constrained battery resources mean that the importance of energy-efficient application design is growing and in this regard wireless network accesses are a major contributor to a mobile device's overall energy consumption. Additionally, the energy consumption characteristics of modern cellular technologies make small volumes of poorly scheduled traffic account for a substantial share of a device's total energy consumption. However, quantifying the communication energy footprint is cumbersome, making it difficult for developers to profile applications from an energy consumption perspective and optimize traffic patterns. This thesis examines the traffic patterns of the Android client of the popular multimedia streaming service Spotify with the intention to reduce its energy footprint, in terms of 3G energy consumption. The application's automated test environment is extended to capture network traffic, which is used to estimate energy consumption. Automated system tests are designed and executed on a physical Android device connected to a 3G network, shedding light on the traffic patterns of different application features. All traffic between the Spotify client application and the backend servers is encrypted. To extract information about the traffic, the application code is instrumented to output supplementary information to the Android system log. The system log is then used as a source of information to attribute data traffic to different application modules and specific lines of code. Two simple traffic shaping techniques, traffic aggregation and piggybacking, are implemented in the application to provide more energy-efficient traffic patterns. As a result, 3G energy consumption during normal music playback is reduced by 22-54%, and a more contrived scenario achieves a 60% reduction. The reductions are attained by rescheduling a small class of messages, most notably data tracking application usage. These messages were found to account for a small fraction of total traffic volume, but a large portion of the application's overall 3G energy consumption.