Watt Wise Web

Mobile applications are increasingly being built using web technologies as a common substrate to achieve portability and to improve developer productivity. Unfortunately, web applications often incur large performance overhead, directly affecting the user quality-of-service (QoS) experience. Traditional techniques in improving mobile processor performance have mostly been adopting desktop-like design techniques such as increasing single-core microarchitecture complexity and aggressively integrating more cores. However, such a desktop-oriented strategy is likely coming to an end due to the stringent energy and thermal constraints that mobile devices impose. Therefore, we must pivot away from traditional mobile processor design techniques in order to provide sustainable performance improvement while maintaining energy efficiency. In this article, we propose to combine hardware customization and specialization techniques to improve the performance and energy efficiency of mobile web applications. We first perform design-space exploration (DSE) and identify opportunities in customizing existing general-purpose mobile processors, that is, tuning microarchitecture parameters. The thorough DSE also lets us discover sources of energy inefficiency in customized general-purpose architectures. To mitigate these inefficiencies, we propose, synthesize, and evaluate two new domain-specific specializations, called the Style Resolution Unit and the Browser Engine Cache. Our optimizations boost performance and energy efficiency at the same time while maintaining generalpurpose programmability. As emerging mobile workloads increasingly rely more on web technologies, the type of optimizations we propose will become important in the future and are likely to have a long-lasting and widespread impact.

Web computing is gradually shifting toward mobile devices, in which the energy budget is severely constrained. As a result, Web developers must be conscious of energy efficiency. However, current Web languages provide developers little control over energy consumption. In this paper, we take a first step toward language-level research to enable energy-efficient Web computing. Our key motivation is that mobile systems can wisely budget energy usage if informed with user quality-of-service (QoS) constraints. To do this, programmers need new abstractions. We propose two language abstractions, QoS type and QoS target, to capture two fundamental aspects of user QoS experience. We then present GreenWeb, a set of language extensions that empower developers to easily express the QoS abstractions as program annotations. As a proof of concept, we develop a GreenWeb runtime, which intelligently determines how to deliver specified user QoS expectation while minimizing energy consumption. Overall, GreenWeb shows significant energy savings (29.2% ⇠ 66.0%) over Android’s default Interactive governor with few QoS violations. Our work demonstrates a promising first step toward language innovations for energy-efficient Web computing. Categories and Subject Descriptors D.3.2 [Programming Language]: Language Classifications–Specialized application languages; D.3.3 [Programming Language]: Language Constructs and Features–Constraints Keywords Energy-efficiency, Web, Mobile computing

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.

THE MOBILE CPU IS STARTING TO NOTICEABLY IMPACT WEB BROWSING PERFORMANCE AND ENERGY CONSUMPTION. ACHIEVING ENERGY-EFFICIENT MOBILE WEB BROWSING REQUIRES CONSIDERING BOTH CPU AND NETWORK CAPABILITIES. RESEARCHERS MUST LEVERAGE INTERACTIONS BETWEEN THE CPU AND NETWORK TO DELIVER HIGH MOBILE WEB PERFORMANCE WHILE MAINTAINING A LOW ENERGY FOOTPRINT. DESIGNING FUTURE HIGH-PERFORMANCE AND ENERGY-EFFICIENT MOBILE WEB CLIENTS IMPLIES LOOKING BEYOND INDIVIDUAL COMPONENTS AND TAKING A FULL SYSTEM PERSPECTIVE.

The Web browser is undoubtedly the single most important application in the mobile ecosystem. An average user spends 72 minutes each day using the mobile Web browser. Web browser internal engines (e.g., WebKit) are also growing in importance because they provide a common substrate for developing various mobile Web applications. In a user-driven, interactive, and latency-sensitive environment, the browser’s performance is crucial. However, the battery-constrained nature of mobile devices limits the performance that we can deliver for mobile Web browsing. As traditional general-purpose techniques to improve performance and energy efficiency fall short, we must employ domain-specific knowledge while still maintaining general-purpose flexibility.

In this paper, we first perform design-space exploration to identify appropriate general-purpose architectures that uniquely fit the characteristics of a popular Web browsing engine. Despite our best effort, we discover sources of energy inefficiency in these customized general-purpose architectures. To mitigate these inefficiencies, we propose, synthesize, and evaluate two new domain-specific specializations, called the Style Resolution Unit and the Browser Engine Cache. Our optimizations boost energy efficiency and at the same time improve mobile Web browsing performance. As emerging mobile workloads increasingly rely more on Web browser technologies, the type of optimizations we propose will become important in the future and are likely to have lasting widespread impact.

Web browsing on mobile devices is undoubtedly the future. However, with the increasing complexity of webpages, the mobile device’s computation capability and energy consumption become major pitfalls for a satisfactory user experience. In this paper, we propose a mechanism to effectively leverage processor frequency scaling in order to balance the performance and energy consumption of mobile web browsing. This mechanism explores the performance and energy tradeoff in webpage loading, and schedules webpage loading according to the webpages’ characteristics, using the different frequencies. The proposed solution achieves 20.3% energy saving compared to the performance mode, and improves webpage loading performance by 37.1% compared to the battery saving mode.

Index Terms—Energy, EDP, Cutoff, Performance, Webpages

Internet web browsing has reached a critical tipping point. Increasingly, users rely more on mobile web browsers to access the Internet than desktop browsers. Meanwhile, webpages over the past decade have grown in complexity by more than tenfold. The fast penetration of mobile browsing and everricher webpages implies a growing need for high-performance mobile devices in the future to ensure continued end-user browsing experience. Failing to deliver webpages meeting hard cut-off constraints could directly translate to webpage abandonment or, for e-commerce websites, great revenue loss. However, mobile devices’ limited battery capacity limits the degree of performance that mobile web browsing can achieve. In this paper, we demonstrate the benefits of heterogeneous systems with big/little cores each with different frequencies to achieve the ideal trade-off between high performance and energy efficiency. Through detailed characterizations of different webpage primitives based on the hottest 5,000 webpages, we build statistical inference models that estimate webpage load time and energy consumption. We show that leveraging such predictive models lets us identify and schedule webpages using the ideal core and frequency configuration that minimizes energy consumption while still meeting stringent cut-off constraints. Real hardware and software evaluations show that our scheduling scheme achieves 83.0% energy savings, while only violating the cut-off latency for 4.1% more webpages as compared with a performance-oriented hardware strategy. Against a more intelligent, OS-driven, dynamic voltage and frequency scaling scheme, it achieves 8.6% energy savings and 4.0% performance improvement simultaneously.

The commoditization of hardware, data center economies of scale, and Internet-scale workload growth all demand greater power efficiency to sustain scalability. Traditional enterprise workloads, which are typically memory and I/O bound, have been well served by chip multiprocessors comprising of small, power-efficient cores. Recent advances in mobile computing have led to modern small cores capable of delivering even better power efficiency. While these cores can deliver performance-per-Watt efficiency for data center workloads, small cores impact application quality-of-service robustness, and flexibility, as these workloads increasingly invoke computationally intensive kernels. These challenges constitute the price of efficiency. We quantify efficiency for an industry-strength online web search engine in production at both the microarchitecture- and system-level, evaluating search on server and mobile-class architectures using Xeon and Atom processors.

Categories and Subject Descriptors

C.0 [Computer Systems Organization]: General—System architectures; C.4 [Computer Systems Organization]: Performance of Systems—Design studies, Reliability, availability, and serviceability

General Terms

Measurement, Experimentation, Performance