The success of mobile devices and applications is directly linked to a user's satisfaction of the quality of service-a metric used to denote the user's perception of the quality of an application. The first and necessary building block to manage user satisfaction is to establish accurate performance and power models which are sensitive to the mobile device's controllable features such as scalable voltage and frequency. Traditionally, performance and power models have been developed with deterministic workloads in mind - assuming long term, stable operating conditions. However, this is insufficient for mobile workloads, which are subject to many sources of variability leading to unpredictable phases of computation. This work establishes the importance and value of modeling the many sources of variations in mobile workloads. A completely data-driven approach is presented that provides accurate estimates of a workload's statistical characteristics, without any assumptions regarding its underlying statistical distribution. The method is light-weight allowing for real-time model evaluation and update. To demonstrate the usefulness of the proposed approach, the design of a dynamic voltage and frequency scaling controller is presented and implemented on an existing mobile device. The proposed controller achieves an energy efficiency improvement of 19 percent over existing Android frequency governors.

This paper proposes DORA - a dynamic frequency controller that maximizes the energy efficiency of smartphones subject to user satisfaction demands in the presence of memory interference stemmed from background processes and coscheduled applications. The proposed algorithm predicts the optimal energy-efficient frequency setting at runtime using staticallytrained performance, dynamic power, and leakage power models. The parameters of the models represent web page characteristics and dynamically varying architecture and system conditions. The algorithm is designed, implemented and extensively evaluated on a Google Nexus 5 smartphone using a variety of mobile web browsing workloads. The results show high prediction accuracies for the performance and power models of 97.5% and 96%, respectively. Overall, DORA improves the smartphone's energy efficiency by an average of 16% compared to the default Android frequency governor, interactive, while maintaining the desired levels of user satisfaction (web page load time).

User satisfaction is pivotal to the success of a mobile application. A recent study has shown that 49% of users would abandon a web-based application if it failed to load within 10 seconds. At the same time, it is imperative to maximize energy efficiency to ensure maximum usage of the limited energy source available to smartphones while maintaining the necessary levels of user satisfaction. An important factor to consider, that has been previously neglected, is variability of execution times of an application, requiring them to be modeled as stochastic quantities. This changes the nature of the objective function and the constraints of the underlying optimization problem. In this paper, we present a new approach to optimal energy control of mobile applications running on modern smartphone devices, focusing on the need to ensure a specified level of user satisfaction. The proposed statistical models address both single and multi-stage applications and are used in the formulation of an optimization problem, the solution to which is a static, lightweight controller that optimizes energy efficiency of mobile applications, subject to constraints on the likelihood that the application execution time meets a given deadline. We demonstrate the proposed models and the corresponding optimization method on three common mobile applications running on a real Qualcomm Snapdragon 8074 mobile chipset. The results show that the proposed statistical estimates of application execution times are within 99.34% of the measured values. Additionally, on the actual Qualcomm Snapdragon 8074 mobile chipset, the proposed control scheme achieves a 29% power savings over commonly-used Linux governors while maintaining an average web page load time of 2 seconds with a likelihood of 90%.

Recent empirical studies have shown that multicore scaling is fast becoming power limited, and consequently, an increasing fraction of a multicore processor has to be under clocked or powered off. Therefore, in addition to fundamental innovations in architecture, compilers and parallelization of application programs, there is a need to develop practical and effective dynamic energy management (DEM) techniques for multicore processors. Existing DEM techniques mainly target reducing processor power consumption and temperature, and only few of them have addressed improving energy efficiency for multicore systems. With energy efficiency taking a center stage in all aspects of computing, the focus of the DEM needs to be on finding practical methods to maximize processor efficiency. Towards this, this article presents STEAM -- an optimal closed-loop DEM controller designed for multicore processors. The objective is to maximize energy efficiency by dynamic voltage and frequency scaling (DVFS). Energy efficiency is defined as the ratio of performance to power consumption or performance-per-watt (PPW). This is the same as the number of instructions executed per Joule. The PPW metric is actually replaced by P α PW (performance α -per-Watt), which allows for controlling the importance of performance versus power consumption by varying α. The proposed controller was implemented on a Linux system and tested with the Intel Sandy Bridge processor. There are three power management schemes called governors , available with Intel platforms. They are referred to as (1) Powersave (lowest power consumption), (2) Performance (achieves highest performance), and (3) Ondemand . Our simple and lightweight controller when executing SPEC CPU2006, PARSEC, and MiBench benchmarks have achieved an average of 18% improvement in energy efficiency (MIPS/Watt) over these ACPI policies. Moreover, STEAM also demonstrated an excellent prediction of core temperatures and power consumption, and the ability to control the core temperatures within 3 ˆ C of the specified maximum. Finally, the overhead of the STEAM implementation (in terms of CPU resources) is less than 0.25%. The entire implementation is self-contained and can be installed on any processor with very little prior knowledge of the processor.

Energy harvesting is a promising solution for reducing network maintenance and the overhead of replacing chemical batteries in sensor networks. In this article, problems related to controlling an active wireless sensor network comprised of nodes powered by both rechargeable batteries and solar energy are investigated. The objective of this control is to maximize the network's Quality of Coverage (QoC), defined as the minimum number of targets that can be covered by the network over a 24-hour period. Assuming a time-varying solar profile, the underlying problem is to optimally control the sensing range of each sensor so as to maximize the QoC. The problem is further constrained by requiring all active sensors to report any sensed data to a centralized base station, making connectivity a key factor in sensor management. Implicit in the solution is the allocation of solar energy during the day to sensing tasks and recharging of the battery so that a minimum coverage is guaranteed at all times. The problem turns out to be a nonlinear optimal control problem of high complexity. By exploiting the particular structure of the problem, we present a novel method for determining near-optimal sensing radii and routing paths as a series of quasiconvex (unimodal) optimization problems. The runtime of the proposed solution is 60X less than the standard optimal control method based on dynamic programming, while the worst-case error is less than 8%. The proposed method is scalable to large networks consisting of hundreds of sensors and targets. Several insights in the design of energy-harvesting networks are provided.

Energy harvesting in a sensor network is essential in situations where it is either difficult or not cost effective to access the network's nodes to replace the batteries. In this paper, we investigate the problems involved in controlling an active wireless sensor network that is powered both by rechargeable batteries and solar energy. The objective of this control is to maximize the network's quality of coverage (QoC), defined as the minimum number of targets that must be covered over a 24-hour period. Assuming a time varying solar profile, the problem is to optimally control the sensing range of each sensor so as to maximize the QoC. Implicit in the solution is the dynamic allocation of solar energy during the day to sensing tasks and to recharging the battery so that minimum coverage is guaranteed even during the night, when only the batteries can supply energy to the sensors. The problem turns out to be a nonlinear optimal control problem of high complexity. Exploiting the specific structure of the problem, we present a method to solve it as a series of quasiconvex (unimodal) optimization problems. The runtime of the proposed solution is 60X less than a naive method that is based on dynamic programming, while its worst-case error is less than 8%. Unlike the dynamic programming method, the proposed method is scalable to large networks consisting of hundreds of sensors and targets. This paper also offers several insights in the design of energy-harvesting networks, which result in minimum network setup cost through the determination of the optimal configuration of the number of sensors and the sampling time.