Workshop Program

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 ofWifi 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.

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.

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 de-creases battery life of a device. While this tradeoff is ac-ceptable if user experience is improved, display densities beyond that which the human eye can perceive would re-sult in decreased device battery life for no perceptible gain. Further, the workload imposed by such high den-sity 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 re-duce GPU power consumption. Our proposed method in-cludes 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 dis-cussion of how such a system may be implemented on existing devices.

Despite the incredible market penetration of smartphones and the app market, the user experience has been plagued by the limited battery life. Worse, battery technology has barely improved in the past decade, while smartphone hardware is becoming more powerful and power hungry. Ultimately, it is the apps running on the smartphone that drain the battery, yet the first million or so apps released in the app market have competed in features and were largely developed in an energy-oblivious manner. As a result, energy drain of different versions of popular apps easily differ by 2x, suggesting significant room for improvement. Yet without automatic tools, finding energy problems in the app source code is like finding needles in a haystack.

In this talk, I will describe the journey we have taken since 2010 towards developing enabling technologies that help to shift app design from feature-centric to energy-centric. I will discuss the design and implementation of the first fine-grained energy profiler, eprof, that answers the very question "where was the energy spent in the app?" as well as the first set of automatic energy bug detection techniques. These software development tools empower app developers to pinpoint energy bottlenecks and energy bugs in the complex app source code.

Y. Charlie Hu is a Professor of Electrical and Computer Engineering and Computer Science (by courtesy) and a University Faculty Scholar at Purdue University. He received a Ph.D. in Computer Science from Harvard in 1997. His research interests lie broadly in mobile systems, distributed systems, operating systems, and computer networking. Since 2010, he has conducted pioneering work on energy profiling and energy debugging on smartphones which has been cited over 450 times, and widely covered by news media such as ABC News, NBC News, BBC, Times of India, MIT Tech Review, and Scientific American.

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.

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.