An automatic approach for creating faster websites using HTTP/2, machine learning, and AI

Información del proyecto

AI for a faster web

Identificador del acuerdo de subvención: 868576

DOI

10.3030/868576

Proyecto cerrado

Fecha de la firma de la CE 11 Mayo 2019

Fecha de inicio 1 Junio 2019

Fecha de finalización 30 Noviembre 2019

Financiado con arreglo a

• INDUSTRIAL LEADERSHIP - Innovation In SMEs

Coste total

€ 71 429,00

Aportación de la UE

€ 50 000,00

Coordinado por

SHIMMERCAT AB
Sweden

Periodic Reporting for period 1 - AI for a faster web (An automatic approach for creating faster websites using HTTP/2, machine learning, and AI)

Período documentado: 2019-06-01 hasta 2019-11-30

Resumen del contexto y de los objetivos generales del proyecto

Website speed is the key to success for e-commerce websites. A fast website creates a better user experience, improves Google ranking, and increases conversion. Optimizing slow websites is therefore a pressing problem on a growing market that calls for efficient solutions.

As web usage and online commerce has exploded, we have moved from simple static websites to fully interactive and complex websites, introducing new challenges for creating fast websites. To address these new challenges, the web protocol HTTP/2 was released in 2015. The new protocol introduces new features that software can use to substantially increase website speed.

However, most of the HTTP/2 features are not straightforward to apply and need to be actively configured. Since the features can lead to substantial speed improvements, the overall objective of this project was to develop a software product that can utilize the features of HTTP/2 to help online businesses create faster websites.

The project outcome shows that the automatic approach with ShimmerCat for creating faster loading times, that uses machine learning and HTTP/2, has the potential to significantly improve the performance of e-commerce websites.

Trabajo realizado desde el comienzo del proyecto hasta el final del período abarcado por el informe y los principales resultados hasta la fecha

The work performed during the project has focused on exploring and assessing the technical feasibility and commercial viability of the software product ShimmerCat, a web server that handles requests for things which are part of a website. During the project ShimmerCat has been developed to send we traffic data to a cloud infrastructure that analyzes the data, and sends back directives about how to manage the traffic to improve website speed.

During the project the technical viability of the product has been validated in an online operational e-commerce environment that takes live web traffic. The project has also resulted in a validation of the products commercial viability, thanks to the help of an ecommerce platform that has implemented the product in order to resell it.

Avances que van más allá del estado de la técnica e impacto potencial esperado (incluida la repercusión socioeconómica y las implicaciones sociales más amplias del proyecto hasta la fecha)

ShimmerCat goes beyond the state of the art as it can collect, analyze, and optimize performance with data from HTTP headers and other metadata (timestamp, TCP details, TLS and protocol fingerprints, etc). This data is analyzed to automatically build rules for HTTP/2 Push and resource preloading, rules for redirecting automatic traffic, above-the-fold image prioritization data, and next-gen optimized image formats. The potential impact of the project is to be able to create websites that load 30-60% faster.

Data collection overview