We present Popper, a dataflow system for building Machine Learning (ML) workflows. A novel aspect of Popper is its built-in support for inflight error handling, which is crucial in developing effective ML workflows. Popper provides a convenient API that allows users to create and execute complex work-flows comprising traditional data processing operations (such as map, filter, and join) and user-defined error handlers. The latter enables inflight detection and correction of errors introduced by ML models in the workflows. Inside Popper, we model the workflow as a reactive dataflow, a directed cyclic graph, to achieve efficient execution through pipeline parallelization. We demonstrate the inflight error-handling capabilities of Popper, for which we have built a graphical interface, allowing users to specify workflows, visualize and interact with its reactive dataflow, and delve into the internals of Popper.

The rise of machine learning workload on smart-phones has propelled GPUs into one of the most power hungry components of modern smartphones. Optimizing the power consumption of mobile GPUs in turn requires accurate estimation of their power draw during app execution. We observe that the prior-art, utilization-frequency based GPU models cannot capture the diverse micro-architectural usage of modern mobile GPUs, and study whether performance monitoring counter (PMC)-based models recently proposed for desktop/server GPUs can be applied to accurately model mobile GPU power. Our study shows that the PMCs that come with dominating mobile GPUs used in modern smartphones are sufficient to model mobile GPU power, but exhibit multicollinearity if used altogether. We present APGPM, the first mobile GPU power modeling methodology that automatically selects an optimal set of PMCs that maximizes the GPU power model accuracy. Evaluation on the two representative mobile GPUs shows that APGPM-generated GPU power models reduce the MAPE modeling error of prior-art by 11.3% to 15.4% while using only 4.66% to 20.41 % of the total number of available PMCs.

Improving software performance through configuration parameter tuning is a common activity during software maintenance. Beyond traditional performance metrics like latency, mobile app developers are interested in reducing app energy usage. Some mobile apps have centralized locations for parameter tuning, similar to databases and operating systems, but it is common for mobile apps to have hundreds of parameters scattered around the source code. The correlation between these “deep” parameters and app energy usage is unclear. Researchers have studied the energy effects of deep parameters in specific modules, but we lack a systematic understanding of the energy impact of mobile deep parameters. In this paper we empirically investigate this topic, combining a developer survey with systematic energy measurements. Our motivational survey of 25 Android developers suggests that developers do not understand, and largely ignore, the energy impact of deep parameters. To assess the potential implications of this practice, we propose a deep parameter energy profiling framework that can analyze the energy impact of deep parameters in an app. Our framework identifies deep parameters, mutates them based on our parameter value selection scheme, and performs reliable energy impact analysis. Applying the framework to 16 popular Android apps, we discovered that deep parameter-induced energy inefficiency is rare. We found only 2 out of 1644 deep parameters for which a different value would significantly improve its app's energy efficiency. A detailed analysis found that most deep parameters have either no energy impact, limited energy impact, or an energy impact only under extreme values. Our study suggests that it is generally safe for developers to ignore the energy impact when choosing deep parameter values in mobile apps.

Blueprint is a declarative domain-specific language for document extraction. Users describe document layout using spatial, textual, semantic, and numerical fuzzy constraints, and the language runtime extracts the field-value mappings that best satisfy the constraints in a given document. We used Blueprint to develop several document extraction solutions in a commercial setting. This approach to the extraction problem proved powerful. Concise Blueprint programs were able to generate good accuracy on a broad set of use cases. However, a major goal of our work was to build a system that non-experts, and in particular non-engineers, could use effectively, and we found that writing declarative fuzzy constraint-based extraction programs was not intuitive for many users: a large up-front learning investment was required to be effective, and debugging was often challenging. To address these issues, we developed a no-code IDE for Blueprint, called Studio, as well as program synthesis functionality for automatically generating Blueprint programs from training data, which could be created by labeling document samples in our IDE. Overall, the IDE significantly improved the Blueprint development experience and the results users were able to achieve. In this paper, we discuss the design, implementation, and deployment of Blueprint and Studio. We compare our system with a state-of-the-art deep-learning based extraction tool and show that our system can achieve comparable accuracy results, with comparable development time, for appropriately-chosen use cases, while providing better interpretability and debuggability.

In this paper, we report on our 6-year experience developing Eagle Tester (eTester for short) - a mobile battery drain testing and diagnostic tool. We show how eTester evolved from an "academic" prototype to a fully automated tool usable by the mobile industry.

As new iterations of more powerful and better connected smartphones emerge, their limited battery life remains a leading factor adversely affecting the mobile experience of millions of smartphone users. While it is well-known that many apps can drain battery even while running in background, there has not been any study that quantifies the extent and severity of such background energy drain for users in the wild. To extend battery life, various new features are being incorporated within the phone, one of them being preventing applications from running in background, i.e., when the screen is off, but their impact is largely unknown. This paper makes several contributions. First, we present a large-scale measurement study that performs an in-depth analysis of the activities of various apps running in background on thousands of phones in the wild. Second, we quantify the amount of battery drain by all such background activities and possible energy saving. Third, we develop a metric to measure the usefulness of background activities that is personalized to each user. Finally, we present a system called HUSH (screen-off optimizer) that monitors the metric online and automatically identifies and suppresses background activities during screen-off periods that are not useful to the user experience. In doing so, our proposed HUSH saves screen-off energy of smartphones by 15.7% on average while incurring minimal impact on the user experience with the apps.

The limited battery life of modern smartphones remains a leading factor adversely affecting the mobile experience of millions of smartphone users. In order to extend battery life, it is critical to understand where and how is energy drain happening on users' phones under normal usage, for example, in a one-day cycle.

Most of today's mobile devices come equipped with both cellular LTE and WiFi wireless radios, making radio bundling (simultaneous data transfers over multiple interfaces) both appealing and practical. Despite recent studies documenting the benefits of radio bundling with MPTCP, many fundamental questions remain about potential gains from radio bundling, or the relationship between performance and energy consumption in these scenarios. In this study, we seek to answer these questions using extensive measurements to empirically characterize both energy and performance for radio bundling approaches. In doing so, we quantify potential gains of bundling using MPTCP versus an ideal protocol. We study the links between traffic partitioning and bundling performance, and use a novel componentized energy model to quantify the energy consumed by CPUs (and radios) during traffic management. Our results show that MPTCP achieves only a fraction of the total performance gain possible, and that its energy-agnostic design leads to considerable power consumption by the CPU. We conclude that not only there is room for improved bundling performance, but an energy-aware bundling protocol is likely to achieve a much better tradeoff between performance and power consumption.

To maximally conserve the critical resource of battery energy, smartphone OSes implement an aggressive system suspend policy that suspends the whole system after a brief period of user inactivity. This burdens developers with the responsibility of keeping the system on, or waking it up, to execute time-sensitive code. Developer mistakes in using the explicit power management unavoidably give rise to energy bugs, which cause significant, unexpected battery drain.

Despite their immense popularity in recent years, smartphones are and will remain severely limited by their battery life. Preserving this critical resource has driven smartphone OSes to undergo a paradigm shift in power management: by default every component, including the CPU, stays off or in an idle state, unless the app explicitly instructs the OS to keep it on! Such a policy encumbers app developers to explicitly juggle power control APIs exported by the OS to keep the components on, during their active use by the app and off otherwise. The resulting power-encumbered programming unavoidably gives rise to a new class of software energy bugs on smartphones called no-sleep bugs, which arise from mis-handling power control APIs by apps or the framework and result in significant and unexpected battery drainage.