Today's cloud service architectures follow a “one size fits all” deployment strategy where the same service version instantiation is provided to the end users. However, consumers are broad and different applications have different accuracy and responsiveness requirements, which as we demonstrate renders the “one size fits all” approach inefficient in practice. We use a production grade speech recognition engine, which serves several thousands of users, and an open source computer vision based system, to explain our point. To overcome the limitations of the “one size fits all” approach, we recommend Tolerance Tiers where each MLaaS tier exposes an accuracy/responsiveness characteristic, and consumers can programmatically select a tier. We evaluate our proposal on the CPU-based automatic speech recognition (ASR) engine and cutting-edge neural networks for image classification deployed on both CPUs and GPUs. The results show that our proposed approach provides a MLaaS cloud service architecture that can be tuned by the end API user or consumer to outperform the conventional “one size fits all” approach.

Abstract Mobile devices are equipped with increasingly smart batteries designed to provide responsiveness and extended lifetime. However, such smart batteries may present a threat to users’ privacy. We demonstrate that the phone’s power trace sampled from the battery at 1KHz holds enough information to recover a variety of sensitive information. We show techniques to infer characters typed on a touchscreen; to accurately recover browsing history in an open-world setup; and to reliably detect incoming calls, and the photo shots including their lighting conditions. Combined with a novel exfiltration technique that establishes a covert channel from the battery to a remote server via a web browser, these attacks turn the malicious battery into a stealthy surveillance device. We deconstruct the attack by analyzing its robustness to sampling rate and execution conditions. To find mitigations we identify the sources of the information leakage exploited by the attack. We discover that the GPU or DRAM power traces alone are sufficient to distinguish between different websites. However, the CPU and power-hungry peripherals such as a touchscreen are the primary sources of fine-grain information leakage. We consider and evaluate possible mitigation mechanisms, highlighting the challenges to defend against the attacks. In summary, our work shows the feasibility of the malicious battery and motivates further research into system and application-level defenses to fully mitigate this emerging threat.

While many methods have been proposed to ensure data quality for objective tasks (in which a single correct response is presumed to exist for each item), estimating data quality with subjective tasks remains largely unexplored. Consider the popular task of collecting instance ratings from human judges: while agreement tends be high for instances having extremely good or bad properties, instances with more middling properties naturally elicit a wider variance in opinion. In addition, because such subjectivity permits a valid diversity of responses, it can be difficult to detect if a judge does not undertake the task in good faith. To address this, we propose a probabilistic, heteroskedastic model in which the means and variances of worker responses are modeled as functions of instance attributes. We derive efficient Expectation Maximization (EM) learning and variational inference algorithms for parameter estimation. We apply our model to a large dataset of 24,132 Mechanical Turk ratings of user experience in viewing videos on smartphones with varying hardware capabilities. Results show that our method is effective at both predicting user ratings and in detecting unreliable respondents.

In this paper, we assess the past, present, and future of mobile CPU design. We study how mobile CPU designs trends have impacted the end-user, hardware design, and the holistic mobile device. We analyze the evolution often cutting-edge mobile CPU designs released over the past seven years. Specifically, we report measured performance, power, energy and user satisfaction trends across mobile CPU generations. A key contribution of our work is that we contextualize the mobile CPU's evolution in terms of user satisfaction, which has largely been absent from prior mobile hardware studies. To bridge the gap between mobile CPU design and user satisfaction, we construct and conduct a novel crowdsourcing study that spans over 25,000 survey participants using the Amazon Mechanical Turk service. Our methodology allows us to identify what mobile CPU design techniques provide the most benefit to the end-user's quality of user experience. Our results quantitatively demonstrate that CPUs play a crucial role in modern mobile system-on-chips (SoCs). Over the last seven years, both single-and multicore performance improvements have contributed to end-user satisfaction by reducing user-critical application response latencies. Mobile CPUs aggressively adopted many power-hungry desktop-oriented design techniques to reach these performance levels. Unlike other smartphone components (e.g. display and radio) whose peak power consumption has decreased over time, the mobile CPU's peak power consumption has steadily increased. As the limits of technology scaling restrict the ability of desktop-like scaling to continue for mobile CPUs, specialized accelerators appear to be a promising alternative that can help sustain the power, performance, and energy improvements that mobile computing necessitates. Such a paradigm shift will redefine the role of the CPU within future SoCs, which merit several design considerations based on our findings.

Mobile Web applications have become an integral part of our society. They pose a high demand for application quality of service (QoS). However, the energy-constrained nature of mobile devices makes optimizing for QoS difficult. Prior art on energy efficiency optimizations has only focused on the trade-off between raw performance and energy consumption, ignoring the application QoS characteristics. In this paper, we propose the concept of energy-efficient QoS (eQoS) to capture the trade-off between QoS and energy consumption. Given the fundamental event-driven nature of mobile Web applications, we further propose event-based scheduling as an optimization framework for eQoS. The event-based scheduling automatically reasons about users' QoS requirements, and accurately slacks the events' execution time to save energy without violating end users' experience. We demonstrate a working prototype using the Google Chromium and V8 framework on the Samsung Exynos 5410 SoC (used in the Galaxy S4 smartphone). Based on real hardware and software measurements, we achieve 41.2% energy saving with only 0.4% of QoS violations perceptible to end users.

In contrast to traditional computing systems, such as desktops and servers, that are programmed to perform “compute-bound” and “run-to-completion” tasks, mobile applications are designed for user interactivity. Factoring user interactivity into computer system design and evaluation is important, yet possesses many challenges. In particular, systematically studying interactive mobile applications across the diverse set of mobile devices available today is difficult due to the mobile device fragmentation problem. At the time of writing, there are 18,796 distinct Android mobile devices on the market and will only continue to increase in the future. Differences in screen sizes, resolutions and operating systems impose different interactivity requirements, making it difficult to uniformly study these systems. We present Mosaic, a cross-platform, timing-accurate record and replay tool for Android-based mobile devices. Mosaic overcomes device fragmentation through a novel virtual screen abstraction. User interactions are translated from a physical device into a platform-agnostic intermediate representation before translation to a target system. The intermediate representation is human-readable, which allows Mosaic users to modify previously recorded traces or even synthesize their own user interactive sessions from scratch. We demonstrate that Mosaic allows user interaction traces to be recorded on emulators, smartphones, tablets, and development boards and replayed on other devices. Using Mosaic we were able to replay 45 different Google Play applications across multiple devices, and also show that we can perform cross-platform performance comparisons between two different processors under identical user interactions.

Enterprise Web applications are moving towards server-side scripting using managed languages. Within this shifting context, event-driven programming is emerging as a crucial programming model to achieve scalability. In this paper, we study the microarchitectural implications of server-side scripting, JavaScript in particular, from a unique event-driven programming model perspective. Using the Node.js framework, we come to several critical microarchitectural conclusions. First, unlike traditional server-workloads such as CloudSuite and BigDataBench that are based on the conventional thread-based execution model, event-driven applications are heavily single-threaded, and as such they require significant single-thread performance. Second, the single-thread performance is severely limited by the front-end inefficiencies of today's server processor microarchitecture, ultimately leading to overall execution inefficiencies. The front-end inefficiencies stem from the unique combination of limited intra-event code reuse and large inter-event reuse distance. Third, through a deep understanding of event-specific characteristics, architects can mitigate the front-end inefficiencies of the managed-language-based event-driven execution via a combination of instruction cache insertion policy and prefetcher.

The traditional guardbanding approach to ensure processor reliability is becoming obsolete because it always over-provisions voltage and wastes a lot of energy. As a next-generation alternative, adaptive guardbanding dynamically adjusts chip clock frequency and voltage based on timing margin measured at runtime. With adaptive guardbanding, voltage guardband is only provided when needed, thereby promising significant energy efficiency improvement.