AURA: An application and user interaction aware middleware framework for energy optimization in mobile devices

Abstract

Mobile battery-operated devices are becoming an essential instrument for business, communication, and social interaction. In addition to the demand for an acceptable level of performance and a comprehensive set of features, users often desire extended battery lifetime. In fact, limited battery lifetime is one of the biggest obstacles facing the current utility and future growth of increasingly sophisticated "smart" mobile devices. This paper proposes a novel application-aware and user-interaction aware energy optimization middleware framework (AURA) for pervasive mobile devices. AURA optimizes CPU and screen backlight energy consumption while maintaining a minimum acceptable level of performance. The proposed framework employs a novel Bayesian application classifier and management strategies based on Markov Decision Processes to achieve energy savings. Real-world user evaluation studies on a Google Android based HTC Dream smartphone running the AURA framework demonstrate promising results, with up to 24% energy savings compared to the baseline device manager, and up to 5× savings over prior work on CPU and backlight energy co-optimization.

Reducing energy consumption of smartphones using user-perceived response time analysis

“…In particular, SmartCap [8] has shown that the neural network-based inference model can be useful to decide the minimal acceptable frequency without degrading user experience. The technique proposed in AURA [9] is also similar to our approach. In their approach, AURA can effectively decide the CPU frequency for each interactive session by exploiting an app classification scheme based on the user interaction intensity.…”

User-Centric Energy-Efficient Scheduling on Multi-Core Mobile Devices

“…Studies of human computer interaction indicate that people usually expect to receive feedback from their devices within a few hundred milliseconds after each touch interaction [3,13]. To avoid an adverse impact on user experience, if the user is interacting with the foreground application, we should provide as many CPU resources as possible to ensure a timely response.…”

“…Following the measurements reported in[13], our implementation adopts 500 ms 3. In Samsung Galaxy S3, each scheduling period is 6.6 milliseconds, which is not relevant to our design.…”

A middleware framework for application-aware and user-specific energy optimization in smart mobile devices

“…This work is a significantly extended version of our previously published conference paper [15] with the following major additions: (i) additional power models, algorithm analysis, and results for a new phone architecture-the Google Nexus One; (ii) analysis and results for a new power management algorithm based on Q-Learning; (iii) a finer-grained app classification that allows the algorithms to be tuned to each application more precisely; (iv) a more extensive user-device interaction field study in which we examine the variability of user interaction patterns in Section 2; (v) further details in Section 3.2 about the activation of DVFS during idle periods; (vi) a study of the effect of successful prediction rates on actual user satisfaction and definition of a minimum acceptable performance level, in Section 5; (vii) a study on the performance of the power management algorithms when more mispredictions are allowed, in Section 5; and (viii) more comprehensive related work in Section 6 explaining how our work is different and novel in comparison with previously published work.…”