28,197 research outputs found
Performance scalability analysis of JavaScript applications with web workers
Web applications are getting closer to the performance of native applications taking advantage of new standard–based technologies. The recent HTML5 standard includes, among others, the Web Workers API that allows executing JavaScript applications on multiple threads, or workers. However, the internals of the browser’s JavaScript virtual
machine does not expose direct relation between workers and running threads in the browser and the utilization of logical cores in the processor. As a result, developers do not know how performance actually scales on different environments and therefore what is the optimal number of workers on parallel JavaScript codes. This paper presents the first performance scalability analysis of parallel web apps with multiple workers. We focus on two case studies representative of different worker execution models. Our analyses show performance scaling on different parallel processor microarchitectures and on three major web browsers in the market. Besides, we study the impact of co–running applications on the web app performance. The results provide insights for future approaches to automatically find out the optimal number of workers that provide the best tradeoff between performance and resource usage to preserve system responsiveness and user experience, especially on environments with unexpected changes on system workload.Peer ReviewedPostprint (author's final draft
adPerf: Characterizing the Performance of Third-party Ads
Monetizing websites and web apps through online advertising is widespread in
the web ecosystem. The online advertising ecosystem nowadays forces publishers
to integrate ads from these third-party domains. On the one hand, this raises
several privacy and security concerns that are actively studied in recent
years. On the other hand, given the ability of today's browsers to load dynamic
web pages with complex animations and Javascript, online advertising has also
transformed and can have a significant impact on webpage performance. The
performance cost of online ads is critical since it eventually impacts user
satisfaction as well as their Internet bill and device energy consumption.
In this paper, we apply an in-depth and first-of-a-kind performance
evaluation of web ads. Unlike prior efforts that rely primarily on adblockers,
we perform a fine-grained analysis on the web browser's page loading process to
demystify the performance cost of web ads. We aim to characterize the cost by
every component of an ad, so the publisher, ad syndicate, and advertiser can
improve the ad's performance with detailed guidance. For this purpose, we
develop an infrastructure, adPerf, for the Chrome browser that classifies page
loading workloads into ad-related and main-content at the granularity of
browser activities (such as Javascript and Layout). Our evaluations show that
online advertising entails more than 15% of browser page loading workload and
approximately 88% of that is spent on JavaScript. We also track the sources and
delivery chain of web ads and analyze performance considering the origin of the
ad contents. We observe that 2 of the well-known third-party ad domains
contribute to 35% of the ads performance cost and surprisingly, top news
websites implicitly include unknown third-party ads which in some cases build
up to more than 37% of the ads performance cost
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