Accelerating the Mobile Web with Selective Offloading

Motivation

    Nowadays, mobile devices become more and more popular as a convenient access to Internet. However, it's still very slow to load a web page on these devices. The reason is that there are a lot of limitations with mobile devices. Thus, offloading is now adopted in some browsers to solve the problem, such as Opera Mini, Android Chrome Beta and Amazon Silk. But only compressing and fixed part offloading are applied in them. The authors come up with a more flexible way, offloading portions of the page load process, and describe the opportunities, challenges and a measurement-based framework in this paper.

Main Points

• Web application is different, its network activities and computation are inter-dependent, and computation of different kinds of objects incurs different patterns.
• If close to a CDN provider, a cloud can efficiently reduce the page load time. And a computer is obviously faster than a smartphone, while a computer does not have power and 3G/4G data limitation. 
• Lack of standardization and documentation makes the page load process hard to understand. Decreasing computation may cause increasing network time, and it's hard to measure. Position of cloud server can be guaranteed to be close to CDN providers, and security issues & cache issues may change due to the cloud between CDN providers and mobile devices.
• The size of intermediate representations generated by HTML Parser, Object Loader, Evaluator and Rendering Engine can vary even larger than the raw. And blocking due to dependencies among these controllers leads to the bottlenecks of a page load, so identifying the bottlenecks and spending less time on it are necessary for a good decision.
• Divisions are subject to constraints, and two portions division will not increase the interactions.
• Cloud servers should be close to CDNs, and decision depend on different situations and web pages.
• A single TCP connection between cloud and device can provide more bandwidth and better control with specified priority policies.
• The model only depend on measurements, so it can accommodate with future technologies.

Trade-off

• Since the Web servers is dynamic due to CDNs, to choose a cloud server may cause overhead, or it will cause a bad decision.
• Some issues are described without good solutions, such as security and cache.
• This framework need more concrete examples and experiments to be convincing.