Dynamic Voltage and Frequency Scaling (DVFS) is a key technology for enhancing power efficiency in computing devices. However, conventional DVFS methods struggle with the unique demands of mobile devices. Recent reinforcement learning (RL)-based approaches address this by tailoring to mobile-specific thermal and workload characteristics. Yet, these solutions make frequency adjustments that ignore device-specific configurations calibrated by vendors, neglect the impact of ambient and non-processor components—such as battery, display, and integrated circuits—that significantly affect processor thermal management, and rely on fixed environment-dependent parameters, limiting adaptability across different environments. To address these limitations, we propose EarDVFS, an environment-adaptable RL-based solution that employs proactive throttling to combine the strengths of traditional and RL-based methods, considers the temperatures of ambient and non-processor components for better thermal management, and features an environment-robust RL parameter design. Extensive experiments across varying ambient temperatures, devices, and workloads demonstrate that EarDVFS consistently enhances power efficiency by an average of 21.6% and up to 49.6% compared to default DVFS while maintaining performance. Furthermore, we conduct comprehensive ablation studies on the action, state, and reward elements of our RL model, confirming that each element significantly contributes to EarDVFS’s adaptability and effectiveness across diverse thermal environments.

With the increasing popularity of electric vehicles (EVs), drivers want their vehicle batteries to be charged in a few minutes; at present, this is feasible only if battery service stations replace the EV battery pack with a fully charged battery pack. In this paper, we formulate the scheduling problem for battery swap stations, aiming to provide the drivers timing guarantees for different types of EVs, each with its own sporadic/periodic arrival pattern and deadline constraint. To solve this problem, we analyze its unique characteristics from a real-time scheduling perspective, with the main challenge being the circular timing dependency between two distinct scheduling processes: the swapping operation and the charging operation. We first derive a sufficient condition that decouples the dependency and then develop scheduling policies and timing guarantee techniques, designed for not only being specialized for the problem but also accommodating the sufficient condition in a time-predictable and resource-efficient manner. While the problem formulation and solution hold significance as the first attempt to establish real-time scheduling principles for battery swap stations, we also address how to accommodate real-world EV arrivals at a swapping station that do not necessarily follow a sporadic/periodic pattern. Finally, we evaluate the effectiveness of the proposed principles not only in addressing the formulated problem but also in accommodating real-world EV arrival patterns via simulation and a case study.

While most existing battery cell balancing approaches were posttreatment (i.e., handling diverse voltage levels originating from different battery cell status), a pretreatment approach, called reconfiguration-assisted charging (RAC), was developed, which dynamically attaches a proper number of resistor arrays to each group of battery cells with similar status, preventing battery cell imbalance; note that this pretreatment approach can be used orthogonally with existing posttreatment approaches such as active/passive balancing. Relaxing its impractical assumptions of RAC (e.g., all necessary resistor arrays are deployed in the target system), this article proposesRAC$^+$, which realizes its practical and efficient use for the pretreatment concept of RAC. The experiment results demonstrate thatRAC$^+$achieves the same balancing performance asRACwhile reducing the number of required resistors by 69% compared toRAC. The extensive experiment results also show thatRAC$^+$is not only robust to various charging environments, but also proven to be effective in terms of minimizing power loss.

Even with advances in single-cell batteries, mobile users still experience low battery anxiety. By analyzing 19,855 hours of user behavior, we proposeMixMax, a heterogeneous battery system consisting of three complementary battery types tailored to minimizing low battery time. While the heterogeneous battery system offers an opportunity to simultaneously improve capacity and charging speed, one must face non-trivial challenges to design charge/discharge policies during runtime and determine the ratio of enclosed batteries. They are highly dependent on each other, which entails almost infinite candidates for the choice.MixMaxsimplifies this by reformulating the problem as an optimization problem, breaking it down into manageable sub-problems. However,MixMaxstill faces the challenge of catering to all users due to their diverse battery usage patterns. To address this, we introduce a customizedMixMaxthat groups users based on their usage patterns and provides tailored battery solutions. In evaluatingMixMax, we fabricate coin-cell batteries, develop a precise battery emulator using the fabricated batteries, and prototypeMixMaxon a real-world smartphone. Our evaluation shows thatMixMaxreduces low battery time by up to 24.6% without compromising capacity, volume, weight, or user behavior, and its customized version can further reduce it by up to 46.2%.

Low-battery anxiety has emerged as a result of growing dependence on mobile devices, where the anxiety arises when the battery level runs low. While battery life can be extended through power-efficient hardware and software optimization techniques, low-battery anxiety will still remain a phenomenon as long as mobile devices rely on batteries. In this paper, we investigate how an accurate real-time energy consumption prediction at the application-level can improve the user experience in low-battery situations. We present Serenus, a mobile system framework specifically tailored to predict the energy consumption of each mobile application and present the prediction in a user-friendly manner. We conducted user studies using Serenus to verify that highly accurate energy consumption predictions can effectively alleviate low-battery anxiety by assisting users in planning their application usage based on the remaining battery life. We summarize requirements to mitigate users’ anxiety, guiding the design of future mobile system frameworks.

Despite the physical advance of an existing single-cell battery system, mobile users are still suffering from low battery anxiety. With a careful analysis of users' battery usage behavior collected for 19,855 hours, we propose a heterogeneous battery system, MixMax, consisting of three complementary battery types tailored to minimizing the low battery time. While composing a heterogeneous battery system opens up a chance to simultaneously improve the capacity and the charging speed, one must face non-trivial challenges to determine the ratio of enclosed batteries and charge/discharge policies during the run-time. They are highly dependent on each other, which entails almost infinite candidates for the choice. MixMax gracefully unwinds the dependencies as it formulates the decision-making problem into an optimization problem and decomposes it into multiple sub-problems instead. To evaluate MixMax, we fabricate coin-cell batteries and experiment with them to model an accurate battery emulator which sophisticatedly reproduces the dynamics of battery systems. Our experimental results demonstrate that MixMax can reduce the low battery time by up to 24.6% without compromising capacity, volume, weight, and more importantly, users' battery usage behavior. In addition, we prototype MixMax on a smartphone, presenting the practicality of MixMax on mobile systems.

Although essential for Inherently non-preemptive tasks and favorable to tasks with large preemption/migration overheads, non-preemptive scheduling has not been thoroughly studied compared to preemptive scheduling. In particular, existing studies for non-preemptive scheduling could not effectively exploit being non-work-conserving (i.e., idling processor(s) intentionally), failing to achieve its full schedulability capability. In this paper, we propose the first non-preemptive scheduling framework that covers work-conserving-infeasible task sets (each of which is proven unschedulable by every work-conserving non-preemptive scheduling), without knowledge of future release patterns of tasks (i.e., without clairvoyance). To this end, we first discover the following principle: without clairvoyance, it is impossible to generate a feasible schedule for work-conserving-infeasible task sets on a uniprocessor platform. To make it possible on a multi-processor platform, we design the NWC(N)-NP-* framework that systematically idles up to N processors so as to enable N designated tasks (that yield work-conserving-infeasibility) to be schedulable without clairvoyance, and derive important properties of the framework. We then target the framework associated with fixed- priority scheduling (as a prioritization policy), and develop its schedulability test by utilizing the framework's properties. Our simulation results demonstrate that the proposed framework successfully covers a number of work-conserving-infeasible task sets, none of which can be deemed schedulable by any previous approach.

Battery aging is one of the critical issues in battery-powered electric systems. However, this issue has not received much attention in the real-time systems community. In this paper, we present the first attempt to translate the problem of minimizing battery aging subject to timing requirements into a real-time scheduling problem, addressing the following issues. (i) Can scheduling make a systematic impact on battery aging? If so, which scheduling principles are favorable to minimizing battery aging? (ii) If there exists any, how can we build upon the scheduling principle to guarantee real-time requirements? For (i), we first illuminate the connection between task scheduling and battery aging minimization and then derive a principle for task scheduling from abstracting the complicated dynamics of battery aging, which is to minimize the variance of total power consumption over time. In addition, we implement a battery aging simulator and use it to verify the effectiveness of the proposed principle in minimizing battery aging and its impact on quantitative improvement. For (ii), we propose a scheduling framework that separates control for timing guarantees from that for battery aging minimization. Such a separation allows reducing the complexity significantly such that we can employ existing scheduling algorithm and schedulability analysis for real-time guarantee and tailor the proposed scheduling principle to decelerate battery aging without taking real-time guarantees into accounts. Our simulation results show that the proposed framework can extend the battery lifespan by up to 144.4%.