物件導向軟體之架構(Object-Oriented Software Construction)探討

Motivated by the rapid deployment of 5G, we carry out an in-depth measurement study of the performance, power consumption, and application quality-of-experience (QoE) of commercial 5G networks in the wild. We examine different 5G carriers, deployment schemes (Non-Standalone, NSA vs. Standalone, SA), radio bands (mmWave and sub 6-GHz), protocol configurations (_e.g._ Radio Resource Control state transitions), mobility patterns (stationary, walking, driving), client devices (_i.e._ User Equipment), and upper-layer applications (file download, video streaming, and web browsing). Our findings reveal key characteristics of commercial 5G in terms of throughput, latency, handover behaviors, radio state transitions, and radio power consumption under the above diverse scenarios, with detailed comparisons to 4G/LTE networks. Furthermore, our study provides key insights into how upper-layer applications should best utilize 5G by balancing the critical tradeoff between performance and energy consumption, as well as by taking into account the availability of both network and computation resources. We have released the datasets and tools of our study at https://github.com/SIGCOMM21-5G/artifact.

A variegated look at 5G in the wild: performance, power, and QoE implications

The existing slowness of the web on mobile devices frustrates users and hurts the revenue of website providers. Prior studies have attributed high page load times to dependencies within the page load process: network latency in fetching a resource delays its processing, which in turn delays when dependent resources can be discovered and fetched.To securely address the impact that these dependencies have on page load times, we present Vroom, a rethink of how clients and servers interact to facilitate web page loads. Unlike existing solutions, which require clients to either trust proxy servers or discover all the resources on any page themselves, Vroom's key characteristics are that clients fetch every resource directly from the domain that hosts it but web servers aid clients in discovering resources. Input from web servers decouples a client's processing of resources from its fetching of resources, thereby enabling independent use of both the CPU and the network. As a result, Vroom reduces the median page load time by more than 5 seconds across popular News and Sports sites. To enable these benefits, our contributions lie in making web servers capable of accurately aiding clients in resource discovery and judiciously scheduling a client's receipt of resources.

https://dl.acm.org/doi/pdf/10.1145/3098822.3098851

Vroom: Accelerating the Mobile Web with Server-Aided Dependency Resolution

Despite the 4G LTE's 10X capacity improvement over 3G, mobile Web loading latency remains a major issue that hampers user experience. The root cause lies in the inefficient transport-layer that underutilizes LTE capacity, due to high channel dynamics, wireless link losses, and insufficient application traffic to propel the bandwidth probing. In this paper, we propose Cellular Link-Aware Web loading (CLAW), which boosts mobile Web loading using a physical-layer informed transport protocol. CLAW harnesses the limited PHY-layer statistics available on LTE phones to quantitatively model the LTE channel resource utilization, which is then translated into a transport window that best fits the bandwidth. Consequently, CLAW can estimate and fully utilize the available bandwidth almost within one RTT. In addition, CLAW can precisely differentiate LTE wireless loss from congestion loss, and identify the rare cases when the wireline backhaul becomes the bottleneck. We have prototyped CLAW on commodity LTE phones. Across a wide range of experimental settings, CLAW consistently reduces Web loading latency by more than 30%, compared to classical TCP variants and state-of-the-art congestion controls for cellular networks.

https://dl.acm.org/doi/pdf/10.1145/3081333.3081367

Accelerating Mobile Web Loading Using Cellular Link Information

Context. Mobile web apps represent a large share of the Internet today. However, they still lag behind native apps in terms of user experience. Progressive Web Apps (PWAs) are a new technology introduced by Google that aims at bridging this gap, with a set of APIs known as service workers at its core. Goal. In this paper, we present an empirical study that evaluates the impact of service workers on the energy efficiency of PWAs, when operating in different network conditions on two different generations of mobile devices. Method. We designed an empirical experiment with two main factors: the use of service workers and the type of network available (2G or WiFi). We performed the experiment by running a total of 7 PWAs on two devices (an LG G2 and a Nexus 6P) that we evaluated as blocking factor. Our response variable is the energy consumption of the devices. Results. Our results show that service workers do not have a significant impact over the energy consumption of the two devices, regardless of the network conditions. Also, no interaction was detected between the two factors. However, some patterns in the data show different behaviors among PWAs. Conclusions. This paper represents a first empirical investigation on PWAs. Our results show that the PWA and service workers technology is promising in terms of energy efficiency.

/pdf/assessing-the-impact-of-service-workers-on-the-energy-4kb7552equ.pdf

Assessing the impact of service workers on the energy efficiency of progressive web apps

Mobile page load times are an order of magnitude slower compared to non-mobile pages. It is not clear what causes the poor performance: the slower network, the slower computational speeds, or other reasons. Further, most Web optimizations are designed for non-mobile browsers and do not translate well to the mobile browser. Towards understanding mobile Web page load times, in this paper we: (1) perform an in-depth pairwise comparison of loading a page on a mobile versus a non-mobile browser, and (2) characterize the bottlenecks in the mobile browser {\em vis-a-vis} non-mobile browsers. To this end, we build a testbed that allows us to directly compare the low-level page load activities and bottlenecks when loading a page on a mobile versus a non-mobile browser. We find that computation is the main bottleneck when loading a page on mobile browsers. This is in contrast to non-mobile browsers where networking is the main bottleneck. We also find that the composition of the critical path during page load is different when loading pages on the mobile versus the non-mobile browser. A key takeaway of our work is that we need to fundamentally rethink optimizations for mobile browsers.

https://dl.acm.org/doi/pdf/10.1145/2872427.2883014

An In-depth Study of Mobile Browser Performance

Mobile Web page performance is critical to content providers, service providers, and users, as Web browsers are one of the most popular apps on phones. Slow Web pages are known to adversely affect profits and lead to user abandonment. While improving mobile web performance has drawn increasing attention, most optimizations tend to overlook an important factor, energy. Given the importance of battery life for mobile users, we argue that web page optimizations should be evaluated for their impact on energy consumption. However, examining the energy effects of a web optimization is challenging, even if one has access to power monitors, for several reasons. First, the page load process is relatively short-lived, ranging from several milliseconds to a few seconds. Fine-grained resource monitoring on such short timescales to model energy consumption is known to incur substantial overhead. Second, Web pages are complex. A Web enhancement can have widely varying effects on different page load activities. Thus, studying the energy impact of a Web enhancement on page loads requires understanding its effects on each page load activity. Existing approaches to analyzing mobile energy typically focus on profiling and modeling the resource consumption of the device during execution. Such approaches consider long-running services and apps such as games, audio, and video streaming, for which low-overhead, coarse-grained resource monitoring suffices. For page loads, however, coarse-grained resource monitoring is not sufficient to analyze the energy consumption of individual, short-lived, page load activities. We present RECON (REsource- and COmpoNent-based modeling), a modeling approach that addresses the above challenges to estimate the energy consumption of any Web page load. The key intuition behind RECON is to go beyond resource-level information and exploit application-level semantics to capture the individual Web page load activities. Instead of modeling the energy consumption at the full page load level, which is too coarse grained, RECON models at a much finer component level granularity. Components are individual page load activities such as loading objects, parsing the page, or evaluating JavaScript. To do this, RECON combines coarse-grained resource utilization and component-level Web page load information available from existing tools. During the initial training stage, RECON uses a power monitor to measure the energy consumption during a set of page load processes and juxtaposes this power consumption with coarse-grained resource and component information. RECON uses both simple linear regression and more complex neural networks to build a model of the power consumption as a function of the resources used and the individual page load components, thus providing benefits over individual models. Using the model, RECON can estimate the energy consumption of any Web page loaded as-is or upon applying any enhancement, without the monitor. We experimentally evaluate RECON on the Samsung Galaxy S4, S5, and Nexus devices using 80 Web pages. Comparisons with actual power measurements from a fine-grained power meter show that, using the linear regression model, RECON can estimate the energy consumption of the entire page load with a mean error of 6.3% and that of individual page load activity segments with a mean error of 16.4%. When trained as a neural network, RECON's mean error for page energy estimation reduces to 5.4% and the mean segment error is 16.5%. We show that RECON can accurately estimate the energy consumption of a Web page under different network conditions, such as lower bandwidth or higher RTT, even when the model is trained under a default network condition. RECON also accurately estimates the energy consumption of a Web page after applying popular Web enhancements including ad blocking, inlining, compression, and caching.

https://dl.acm.org/doi/pdf/10.1145/3084443

Deconstructing the Energy Consumption of the Mobile Page Load

Mobile web page load time depends on three key factors: (1) the complexity of the Webpage, (2) the underlying network conditions, and (3) the processing capability of the device. While there are several works focusing on the Web complexity and the network, there is a little work in understanding the hardware bottlenecks in the page load process. In this poster, we analyze the effect of hardware bottlenecks of Web pages. We also analyze the effect of GPU offloading, a commonly used solution to speed up Web page loads.

https://dl.acm.org/doi/pdf/10.1145/3123878.3131980

Demystifying Hardware Bottlenecks in Mobile Web Quality of Experience

Web page performance is crucial in today's Internet ecosystem, and Web developers use various developer tools to analyze their page load performance. However, existing tools cannot be used to identify the critical bottlenecks during the page load process. In this work, we design an online tool called WProfX that allows Web developers to visually identify bottlenecks in their page structure. The key to WProfX is that unlike existing Web performance tools, WProfX not only visualizes the page load activity timings, but also extracts the dependencies between the activities. Using the dependency structure, WProfX identifies the critical bottleneck activities. This lets a developer quickly identify why their page is loading slow and conduct what-if analyses to study the effect of different optimizations. WProfX uses low-level tracing information exposed by most major browsers to extract the relationship between page load activities. The result is that WProfX works with most major browsers and newer browser versions. WProfX visualizes the page load process as a dependency graph of semantically meaningful Web activities and identifies the critical bottlenecks. We evaluate WProfX with 14 Web developers who perform three what-if analysis tasks involving identifying the page load bottleneck and evaluating the effect of a page optimization. All the participants were able to complete the tasks with WProfX, compared to less than 60% when using the popular developer tools available today. WProfX is currently being used by Web developers in a large telecom and at a Silicon Valley startup.

WProfX: A Fine-grained Visualization Tool for Web Page Loads

Given the growing significance of network performance, it is crucial to examine how to make the most of available network options and protocols. We propose ECON, a model that predicts performance of applications under different protocols and network conditions to scalably make better network choices. ECON is built on an analytical framework to predict TCP performance, and uses the TCP model as a building block for predicting application performance. ECON infers a relationship between loss and congestion using empirical data that drives an online model to predict TCP performance. ECON then builds on the TCP model to predict latency and HTTP performance. Across four wired and one wireless network, our model outperforms seven alternative TCP models. We demonstrate how ECON (i) can be used by a Web server application to choose between HTTP/1.1 and HTTP/2 for a given Web page and network condition, and (ii) can be used by a video application to choose the optimal bitrate that maximizes video quality without rebuffering.

https://dl.acm.org/doi/pdf/10.1145/3355369.3355578

Javad Nejati

Papers

An In-depth Study of Mobile Browser Performance

Deconstructing the Energy Consumption of the Mobile Page Load

Demystifying Hardware Bottlenecks in Mobile Web Quality of Experience

WProfX: A Fine-grained Visualization Tool for Web Page Loads

ECON: Modeling the network to improve application performance