Paper
Abstract: When designing distributed web services, there are three properties that are commonly desired: consistency, availability, and partition tolerance. It is impossible to achieve all three. In this note, we prove this conjecture in the asynchronous network model, and then discuss solutions to this dilemma in the partially synchronous model.
Paper
A very detailed (339 pages) paper on how to use HP products to create a highly available cluster. It's somewhat dated and obviously concentrates on HP products, but it is still good information. Table of contents: 1. Disaster Tolerance and Recovery in a Serviceguard Cluster 2. Building an
Strategy
This paper is a great overview of different lightweight web servers. A lot of websites use lightweight web servers to serve images and static content. YouTube is one example: YouTube Architecture. So if you need to improve performance consider changing over a different web server for some types of content.
MySQL
Guide to Cost-effective Database Scale-Out using MySQL by MySQL This paper is behind a registration-wall, you can't do anything on the MySQL site without filling out a form of some kind, but it's a short, decent introduction to using MySQL for a good sized website. A
MySQL
MySQL Scale-Out by application partitioning by Oli Sennhauser Eventually every database system hit its limits. Especially on the Internet, where you have millions of users which theoretically access your database simultaneously, eventually your IO system will be a bottleneck. [A] promising but more complex solution with nearly no scale-out limits
Clustered Storage System
The Clustered Storage Revolution If the clustered file system, clustered storage system, storage virtualization movement is new to you then this is a good intro paper. It's a both vendor puff piece and informative, so it might be worth your time. A Quick Hit of What's
Paper
Replication Under Scalable Hashing: A Family of Algorithms for Scalable Decentralized Data Distribution From the abstract: Typical algorithms for decentralized data distribution work best in a system that is fully built before it first used; adding or removing components results in either extensive reorganization of data or load imbalance in