The Cap Theorem’s Broad Reach

THE CAP THEOREM’S BROAD REACH The CAP theorem has been highly influential within the cloud computing community, and is widely cited as a justification for building cloud services with weak consistency or assurance properties. CAP’s impact has been especially important in the first-tier settings on which we focus in this article. Many of today’s developers believe that CAP precludes consistency in first-tier services. For example, eBay has proposed BASE (Basically Available replicated Soft state with Eventual consistency), a development methodology in which services that run in a single datacenter on a reliable network are deliberately engineered to use potentially stale or incorrect data, rejecting synchronization in favor of faster response, but running the risk of inconsistencies.3 Researchers at Amazon.com have also adopted BASE. They point to the self-repair mechanisms in the Dynamo keyvalue store as an example of how eventual consistency behaves in practice.4 Inconsistencies that occur in eBay and Amazon cloud applications can often be masked so that users will not notice them. The same can be said for many of today’s most popular cloud computing uses: how much consistency is really needed by YouTube or to support Web searches? However, as applications with stronger assurance needs T he CAP theorem explores tradeoffs between consistency, availability, and partition tolerance, and concludes that a replicated service can have just two of these three properties.1,2 To prove CAP, researchers construct a scenario in which a replicated service is forced to respond to conflicting requests during a wide-area network outage, as might occur if two different datacenters hosted replicas of some single service, and received updates at a time when the network link between them was down. The replicas respond without discovering the conflict, resulting in inconsistency that might confuse an end user. However, there are important situations in which cloud computing developers depend upon data or service replication, and for which this particular proof does not seem to apply. Here, we consider such a case: a scalable service running in the first tier of a single datacenter. Today’s datacenters employ redundant networks that almost never experience partitioning failures: the “P” in CAP does not occur. Nonetheless, many cloud computing application developers believe in a generalized CAP “folk theorem,” holding that scalability and elasticity are incompatible with strong forms of consistency. New data-consistency models make it possible for cloud computing developers to replicate soft state without encountering the limitations associated with the CAP theorem. Kenneth P. Birman, Daniel A. Freedman, Qi Huang, and Patrick Dowell, Cornell University Overcoming CAP with Consistent Soft-State Replication