Abstract
HTTP prioritization allows to signal the priority of web resources to aid and speed up the webpage loading process. However, setting optimal resource priorities is challenging. Typically, generalized priority strategies are used to achieve good performance for most websites, but the strategies struggle in certain scenarios reducing human-perceivable performance. Thus, we propose VGPrio, an approach that automatically optimizes resource priorities w.r.t. visual metrics / human-perceivable performance. VGPrio uses a Bayesian optimization-based method to learn prioritization strategies for websites that specifically improve the human-perceivable SpeedIndex. Through its sample-efficient method, VGPrio only requires few iterations while our evaluation on a public website corpus shows that it can improve the SpeedIndex by up to 50% compared to default strategies evading strong detriments and being more widely applicable than related work aiming at similar goals. As such, VGPrio represents a promising option to improve human-perceivable web performance beyond manual optimization.
Constantin Sander, M.Sc.
Researcher
Ike Kunze, M.Sc.
Researcher
Dario Veltri
