Polaris: Faster Page Loads Using Fine-grained Dependency Tracking

Citation:

Netravali, Ravi, Ameesh Goyal, James Mickens, and Hari Balakrishnan, ed. 2016. “Polaris: Faster Page Loads Using Fine-grained Dependency Tracking.” NSDI 2016. Santa Clara, CA. Copy at http://www.tinyurl.com/mvqvm56

Abstract:

To load a web page, a browser must fetch and evaluate objects like HTML files and JavaScript source code. Evaluating an object can result in additional objects being fetched and evaluated. Thus, loading a web page requires a browser to resolve a dependency graph; this partial ordering constrains the sequence in which a browser can process individual objects. Unfortunately, many edges in a page’s dependency graph are unobservable by today’s browsers. To avoid violating these hidden dependencies, browsers make conservative assumptions about which objects to process next, leaving the network and CPU underutilized.

We provide two contributions. First, using a new measurement platform called Scout that tracks fine-grained data flows across the JavaScript heap and the DOM, we show that prior, coarse-grained dependency analyzers miss crucial edges: across a test corpus of 200 pages, prior approaches miss 30% of edges at the median, and 118% at the 95th percentile. Second, we quantify the benefits of exposing these new edges to web browsers. We introduce Polaris, a dynamic client-side scheduler that is written in JavaScript and runs on unmodified browsers; using a fully automatic compiler, servers can translate normal pages into ones that load themselves with Polaris. Polaris uses fine-grained dependency graphs to dynamically determine which objects to load, and when. Since Polaris’ graphs have no missing edges, Polaris can aggressively fetch objects in a way that minimizes network round trips. Experiments in a variety of network conditions show that Polaris decreases page load times by 34% at the median, and 59% at the 95th percentile.

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