Abstract-Recent development of sophisticated smartphones has made them indispensable part of our everyday life. However, advances in battery technology cannot keep up with the demand for longer battery life. Subsequently, energy efficiency has become one of the most important factors in designing smartphones. Multitasking and better multimedia features in the mobile applications continuously push memory requirements further, making energy optimizations for memory critical. Mobile RAM is already optimized for energy efficiency at the hardware level. It also provides power state switching interfaces to the operating system which enables the OS level energy optimizations. Many RAM optimizations have been explored for computer systems and in this paper we explore their applicability to smartphone hardware. In addition, we apply those optimizations to the newly emerging Phase Change Memory and study their energy efficiency and performance. Finally, we propose a hybrid approach to take the advantage of both Mobile RAM and Phase Change Memory. Results show that our hybrid mechanism can save more than 98% of memory energy as compared to the standard smartphone system with negligible impact on user experience.
Current trends in data-intensive applications increase the demand for larger physical memory, resulting in the memory subsystem consuming a significant portion of system's energy. Furthermore, data-intensive applications heavily rely on a large buffer cache that occupies a majority of physical memory. Subsequently, we are focusing on the power management for physical memory dedicated to the buffer cache. Several techniques have been proposed to reduce energy consumption by transitioning DRAM into low-power states. However, transitions between different power states incur delays and may affect whole system performance. We take advantage of the I/O handling routines in the OS kernel to hide the delay incurred by the memory state transition so that performance degradation is minimized while maintaining high memory energy savings. Our evaluation shows that the best of the proposed mechanisms hides almost all transition latencies while only consuming 3% more energy as compared to the existing on-demand mechanism, which can expose significant delays.
Wireless Network Interface Cards (WNICs) are part of every portable device, where efficient energy management plays a significant role in extending the device's battery life. The goal of efficient energy management is to match the performance of the WNIC to the network activity shaped by a running application. In the case of interactive applications on mobile systems, network I/O is largely driven by user interactions. Current solutions either require application modifications or lack a sufficient context of execution that is crucial in making accurate and timely predictions. This paper proposes a range of user-interaction-aware mechanisms that utilize a novel approach of monitoring a user's interaction with applications through the capture and classification of mouse events. This approach yields considerable improvements in energy savings and delay reductions of the WNIC, while significantly improving the accuracy, timeliness, and computational overhead of predictions when compared to existing state-of-the-art solutions.
Increasingly power-hungry processors have reinforced the need for aggressive power management. Dynamic voltage scaling has become a common design consideration allowing for energy efficient CPUs by matching CPU performance with the computational demand of running processes. In this paper, we propose Interaction-Aware Dynamic Voltage Scaling (IADVS), a novel fine-grained approach to managing CPU power during interactive workloads, which account for the bulk of the processing demand on modern mobile or desktop systems. IADVS is built upon a transparent, fine-grained interaction capture system. Able to track CPU usage for each user interface event, the proposed system sets the CPU performance level to the one that best matches the predicted CPU demand. Compared to the state-of-the-art approach of user-interactionbased CPU energy management, we show that IADVS improves prediction accuracy by 37%, reduces processing delays by 17%, and reduces energy consumed of the CPU by as much as 4%. The proposed design is evaluated with both a detailed trace-based simulation as well as implementation on a real system, verifying the simulation findings.
Wireless Network Interface Cards (WNICs) are part of every portable device, where efficient energy management plays a significant role in extending the device's battery life. The goal of efficient energy management is to match the performance of the WNIC to the network activity shaped by a running application. In the case of interactive applications on mobile systems, network I/O is largely driven by user interactions. Current solutions either require application modifications or lack a sufficient context of execution that is crucial in making accurate and timely predictions. This paper proposes a range of user-interaction-aware mechanisms that utilize a novel approach of monitoring a user's interaction with applications through the capture and classification of mouse events. This approach yields considerable improvements in energy savings and delay reductions of the WNIC, while significantly improving the accuracy, timeliness, and computational overhead of predictions when compared to existing state-of-the-art solutions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.