ResourceScheduler Tuning Mechanism: Optimizing Critical Image Loading in T7 Browser Kernel
Baidu's T7 browser kernel introduces a ResourceScheduler tuning mechanism that lets front‑end developers set image loading priority via an importance attribute or CSS property, enabling high‑priority above‑fold images to load faster while low‑priority resources are throttled under heavy domain request competition.
This article introduces Baidu's T7 browser kernel's ResourceScheduler tuning mechanism, which provides front-end developers with the ability to adjust sub-resource loading priority for critical resources.
Background: When browsing pages using Baidu App, faster page rendering and quicker loading of critical above-the-fold images provide better user experience. However, Chromium's native capabilities did not support front-end developers in modifying image resource loading priority. The T7 browser kernel addresses this gap by introducing the ResourceScheduler tuning mechanism.
How to Use:
For IMG tag images: Add importance=high attribute:
<img importance=high src="xxx.jpg">
For CSS background images: Configure background-image-importance:high in style attribute or style tag:
<style>
.bk-img {
background-image:url("xxx.jpg");
background-image-importance:high;
}
</style>
Or in style attribute:
<div style="background-image:url(xxx.jpg);background-image-importance:high">
Supported Values: The mechanism supports importance=high , low , and auto . When not configured, the kernel's default resource loading priority is used.
Implementation Principles: The article explains the page resource loading flow, resource loading priority initialization for different resource types (CSS, Font, JS, IMG, LinkPrefetch), and the sub-resource loading scheduling mechanism. The kernel uses priority-based scheduling where high-priority resources are allowed to initiate requests first while low-priority resources may be throttled when concurrent requests exceed limits (e.g., more than 6 concurrent requests per domain).
Effectiveness: The mechanism is most effective when a page has many resources from the same domain, as network request competition is higher. When resources from the same domain are few, the optimization space is limited.
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