In this paper we introduce ViRUS: Virtual function Replacement Under Stress. ViRUS allows the runtime system to switch between blocks of code that perform equivalent functionality at different Quality-of-Service levels when the system is under stress -- be it in the form of scarce energy resources, temperature emergencies, or various sources of environmental and process variability -- with the ultimate goal of energy efficiency. We demonstrate ViRUS with a framework for transparent function replacement in shared libraries and a polymorphic version of the standard C math library in Linux. Case studies show how ViRUS can tradeoff upwards of 4% degradation in application quality for a band of upwards of 50% savings in energy consumption.
Mobile apps connect with counterpart cloud services and pursue data transfer over wireless networks. The amount of mobile data transferred will only increase as app data needs and usage expand. Energy expenditure during periods of data transfer constitutes a significant portion of a mobile's battery usage. At the same time, due to the evolving nature of wireless technologies, there is a proliferation of networks (such as public Wifi hotspots, access points, and cellular technologies) that exhibit diverse energy and performance characteristics. As a user moves around (even within the same logical network) the hardware serving data transfer varies, often offering opportunities for data transfer with distinct bandwidth and latency capabilities. Apps and data services in smartphones however are oblivious to this diversity and the energy impact of using one opportunity vs. others. In this paper, we first present a study of Wifi characteristics in two domains, shopping malls and within an enterprise campus, demonstrating the fine-grained diversity of network opportunities in two commonplace scenarios. Next, we describe a system-level framework and set of interfaces that enable energy efficient app syncs by leveraging the right opportunities. Preliminary results using our approach show up to three times lower energy costs for popular mobile apps using media uploads.
Prior battery-aware systems research has focused on discharge power management in order to maximize the usable battery lifetime of a device. In order to achieve the vision of perpetual mobile device operation, we propose that software also needs to carefully consider the process of battery charging. This is because the power consumed by the system when plugged in can influence the rate of battery charging, and hence, the availability of the system to the user. We characterize the charging process of a Nexus 4 smartphone and analyze the charging behaviors of anonymous Nexus 4 users using the Device Analyzer dataset. We find that there is potential for software schedulers to increase device availability by distributing tasks across the charging period. We estimate that approximately 53% of the users we examined could benefit from up to 18.9% improvement in net energy gained by the battery while charging. Accordingly, we propose new threads of research in charging-aware power management and deferrable task scheduling that could improve overall availability for a significant portion of smartphone users.
Prior work has shown the benefits of Energy Storage Devices (ESDs), such as batteries, to smoothen/flatten power draws in Datacenters, for reducing demand during peak tariffs (for op-ex savings) and under-provisioning the power infrastructure (for cap-ex savings). Until now, all prior studies for such smoothening, referred to as Demand Response, have considered re-purposing existing UPS unit batteries for demand response. It is not clear if such dual usage - handling power outages and demand response - is the most effective option since the needs (energy and/or power), mandates (best effort vs. hard stipulations), costs, availability and health degradation considerations could be very different. In this paper, we study the design space of choices for provisioning ESDs for these dual purposes - separate ESDs for each purpose, common pool of ESDs for both purposes, and softreservations in this pool with possible re-purposing dynamically based on demand. Our evaluations show that: (i) provisioning lead-acid batteries for a peak "power" load needed to handle power outages already comes with sufficient energy capacity that is more than adequate to automatically supply the energy needs for demand response; (ii) this makes it economically attractive to use the same UPS batteries, originally intended for Power Outages, for Demand Response as well, despite any consequent health degradation (due to repeated discharges); (iii) the ability to handle the needs during a power outage is not compromised despite the dual-purposing of these UPS batteries; and (iv) the non-orthogonality of the power and energy capacities of these batteries (i.e. provisioning for the high power needs during an outage automatically comes with a lot of energy capacity) suggests the possibility of having different Energy Storage Technologies for the two purposes and we show that a heterogeneous/hybrid option using Ultra-capacitors or Flywheels for Power Backup and batteries for Demand Response is a more cost-effective option.
The emerging industry trend of ever-increasing display density on mobile devices has dramatically increased workload placed on a mobile GPU's. Because mobile GPU power consumption increases almost linearly with workload, increasing the display density directly decreases battery life of a device. While this tradeoff is acceptable if user experience is improved, display densities beyond that which the human eye can perceive would result in decreased device battery life for no perceptible gain. Further, the workload imposed by such high density displays may invalidate the previous requirement that the interface always run at high frame rates. In this paper, we show that the display on some modern devices already exceeds human perceptive capabilities, a feature which can be exploited at runtime in order to reduce GPU power consumption. Our proposed method includes both resolution and refresh rate scaling, which are shown to reduce mobile GPU power consumption by up to 33% and 38%, respectively. We conclude with a discussion of how such a system may be implemented on existing devices.
To curtail data centers' huge cooling power consumption and water demand (for cooling), air-side economizer has been increasingly adopted to cool down servers. Recently, another sustainability practice, rainwater harvesting, has also seen a growing adoption in data centers, potentially leading to water self-sufficiency without connecting to water utilities to supply cooling water. Nonetheless, various factors, e.g., unpredictable rain falls and limitations on water harvesting area, make water self-sufficiency challenging. In this paper, we present a first-of-its-kind study to evaluate whether it is feasible to achieve water self-sufficiency in data centers. We find that although water self-sufficiency depends on noncontrollable factors such as weather; improving power proportionality (via power management) and increasing water tank size will increase the feasibility, relieving the requirement on water harvesting area and making water self-sufficiency a reality in different locations.
Power and energy efficiency are major concerns in future supercomputing systems. We expect that applications will be constrained to operate under a power budget and achieving the expected levels of performance will be challenging. Understanding how power is consumed by an application throughout its different phases will be necessary to shift power to those resources on the critical path. In this paper, we identify opportunities for shifting power between components for a representative kernel of explicit hydrodynamics codes. Based on a linear regression model, we dynamically throttle the memory system in regions with low memory bandwidth requirements on an energy-efficient supercomputer. Our results show that we can save a significant amount of power that could be used on resources on the critical path and, thus, maximize performance under the operating power budget.
The recent availability of modern big. LITTLE ARM chips featuring heterogeneous processor cores has enabled a practical investigation of the heterogeneous chip concept and its implications for performance and energy. The ODROID XU+E platform produced by Hardkernel integrates hardware power monitors for the individual chip clusters, GPU and Memory. In this paper we investigate the detailed energy characteristics of the hardware components and applications to highlight the platform's potential for energy-aware resource management of the platform. We measure network, storage and CPU energy consumption as well as parallel application performance. We find that the platform offers interesting sweet-spots for fine tuning of resource scheduling, but also poses new challenges, for example for quantifying the cost of switching between clusters.
Despite that OLED screen has been increasingly adopted in smartphones to save power; screen is still one of the most energy-consuming modules in smartphones. Techniques such as local dimming are proposed to further reduce the power consumption of OLED screen, but it is hard to decide which part of the screen could be dimmed, and it often results in compromised user experience. Intuitively, when a user interacts with a smartphone via the touch screen, the screen areas are covered by the user's fingers and even some of the neighboring areas could be safely dimmed. Thus, in this paper, we propose FingerShadow, a new technique which does local dimming for the screen areas covered by user fingers to save more power, without compromising the user visual experience. We have studied 10 users' touch interaction behaviors and found that on average 11.14% of the screen were covered by fingers. For these 10 users, we estimate that FingerShadow can achieve 5.07%-22.32% power saving, averaging 12.96%, with negligible overhead. We discuss the challenges and future research work to implement Finger-Shadow in existing smartphone systems.
