Markov decision process (MDP) framework for optimizing software on mobile phones

Cheung, Tang Lung; Okamoto, Kari; Maker, Frank; Liu, Xin; Akella, Venkatesh

doi:10.1145/1629335.1629338

Cited by 25 publications

(19 citation statements)

References 15 publications

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“…But they only build a simple MDP model for display and GPS, and it is a high cost project if they build a model for all the components of smarphone. Tang Lung Cheung et al [13] proposed a WiFi radio power optimization strategy which focused on the energy consumption of interface states, but had not taken the signal strength into consideration which also has great impact on user experience. And Bo Zhao et al [14] proposed an energy-aware approach for web browsing in 3G based smartphones by reconstructing the computation sequence for opening webpage and building the DOM tree.…”

Section: Related Work and Conclusionmentioning

confidence: 99%

Energy-Aware Download Method in LTE Based Smartphone

Ren

Gao

Wang

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYMobile traffic is experiencing tremendous growth, and this growing wave is no doubt increasing the use of radio component of mobile devices, resulting in shorter battery lifetime. In this paper, we present an Energy-Aware Download Method (EDM) based on the Markov Decision Process (MDP) to optimize the data download energy for mobile applications. Unlike the previous download schemes in literature that focus on the energy efficiency by simply delaying the download requests, which often leads to a poor user experience, our MDP model learns off-line from a set of training download workloads for different user patterns. The model is then integrated into the mobile application to deal the download request at runtime, taking into account the current battery level, LTE reference signal receiving power (RSRP), reference signal signal to noise radio (RSSNR) and task size as input of the decision process, and maximizes the reward which refers to the expected battery life and user experience. We evaluate how the EDM can be used in the context of a real file downloading application over the LTE network. We obtain, on average, 20.3%, 15% and 45% improvement respectively for energy consumption, latency, and performance of energy-delay trade off, when compared to the Android default download policy (Minimum Delay).

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Section: Related Work and Conclusionmentioning

confidence: 99%

Energy-Aware Download Method in LTE Based Smartphone

Ren

Gao

Wang

et al. 2017

IEICE Trans. Inf. & Syst.

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“…Markov Decision Processes (MDP) [22] are discrete time stochastic control processes that are widely used as decision-making models for systems in which outcomes are partly random and partly controlled. MDPs consist of a four-tuple (S, A, P A , R S,A ), where S is a finite number of states, A is a finite set of actions available from each state, P A is the probability that action a in state s at time t will lead to state s * at time t + 1, and R S,A is a reward from transitioning to state s * from state s. All three of our MDP based algorithms make use of the state flow diagram illustrated in Fig.…”

Section: Power Manager 261 Markov Decision Process (Mdp) Based Manmentioning

confidence: 99%

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

Pasricha

Donohoo²,

Ohlsen

2015

Pervasive and Mobile Computing

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Pasricha, Sudeep; Donohoo, Brad K.; and Ohlsen, Chris, "A middleware framework for application-aware and user-specific energy optimization in smart mobile devices" (2015 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 and Q-Learning to achieve energy savings. Real-world user evaluation studies on Google Android based HTC Dream and Google Nexus One smartphones running the AURA framework demonstrate promising results, with up to 29% energy savings compared to the baseline device manager, and up to 5×savings over prior work on CPU and backlight energy co-optimization.

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“…Many results exist about how to optimise energy performance of a single interface without load balancing on multiple interfaces [1,9,10]. In this line of work, MDP-based algorithms to optimise energy consumption of mobile devices taking time variability into account are widely used.…”

Section: Related Workmentioning

confidence: 99%

“…In this line of work, MDP-based algorithms to optimise energy consumption of mobile devices taking time variability into account are widely used. For example, [1] solves a energy optimisation problem for a simplified Wifi interface consisting of only of startup and transmission energy. However, while most of the bytes generated by browsing are received, this optimisation framework only takes sending into account.…”

Section: Related Workmentioning

confidence: 99%

Saving mobile device energy with multipath TCP

Pluntke

Eggert

Kiukkonen

2011

Proceedings of the Sixth International Workshop on MobiArch

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Multipath TCP is a backwards-compatible TCP extension that enables using multiple network paths between two end systems for a single TCP connection, increasing performance and reliability. It can also be used to "shift" active connections from one network path to another without breakage. This feature is especially attractive on mobile devices with multiple radio interfaces, because it can be used to continuously shift active connections to the most energyefficient network path. This paper describes a novel method for deriving such a multipath scheduler using MPTCP that maximises energy savings. Based on energy models for the different radio interfaces as well as a continuously accumulated communication history of the device user, we compute schedulers for different applications by solving a Markov decision process offline. We evaluate these schedulers for a large number of random application models and selected realistic applications derived from measurements. Evaluations based on energy models for a mobile device with Wifi and 3G radio interfaces show that it performs comparably in terms of energy efficiency to a theoretically optimal omniscient oracle scheduler.

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Markov decision process (MDP) framework for optimizing software on mobile phones

Cited by 25 publications

References 15 publications

Energy-Aware Download Method in LTE Based Smartphone

Energy-Aware Download Method in LTE Based Smartphone

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

Saving mobile device energy with multipath TCP

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