SCADS：面向社交计算应用的规模无关存储
SCADS: Scale-Independent Storage for Social Computing Applications
摘要：
Facebook、Flickr和Yelp等协作Web应用程序为存储和查询大量数据提出了新的挑战。由于用户和开发人员更关注性能，而不是单副本一致性或执行ad-hoc查询的能力，因此存在一个专门为宽松的一致性和预计算查询量身定制的高度可扩展系统的机会。Web 2.0开发模型要求能够快速部署新功能并随着用户数量自动扩展。已经有许多成功的分布式键值存储，但到目前为止，没有一个提供像SQL那样丰富的查询语言。我们提出了一个新的体系结构，SCADS，允许开发人员声明状态应用程序特定的一致性要求，利用效用计算，以提供成本效益的规模扩大和缩小，并将使用机器学习模型来预测性能问题，并预测执行前的新查询的资源需求。
英文摘要：
Collaborative web applications such as Facebook, Flickr and Yelp present new challenges for storing and querying large amounts of data. As users and developers are focused more on performance than single copy consistency or the ability to perform ad-hoc queries, there exists an opportunity for a highly-scalable system tailored specifically for relaxed consistency and pre-computed queries. The Web 2.0 development model demands the ability to both rapidly deploy new features and automatically scale with the number of users. There have been many successful distributed key-value stores, but so far none provide as rich a query language as SQL. We propose a new architecture, SCADS, that allows the developer to declaratively state application specific consistency requirements, takes advantage of utility computing to provide cost effective scale-up and scale-down, and will use machine learning models to introspectively anticipate performance problems and predict the resource requirements of new queries before execution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
