Large companies like Facebook, Google, and Microsoft as well as a number of small and medium enterprises daily process massive amounts of data in batch jobs and in real time applications. This generates high network traffic, which is hard to support using traditional, oversubscribed, network infrastructures. To address this issue, several alternative network topologies have been proposed, aiming to increase the bandwidth available in enterprise clusters. We observe that in many of the commonly used workloads, data is aggregated during the process and the output size is a fraction of the input size. This motivated us to explore a different point in the design space. Instead of increasing the bandwidth, we focus on decreasing the traffic by pushing aggregation from the edge into the network.We built Camdoop, a MapReduce-like system running on CamCube, a cluster design that uses a direct-connect network topology with servers directly linked to other
servers. Camdoop exploits the property that CamCube servers forward traffic, to perform in-network aggregation of data during the shuffle phase. Camdoop supports the same functions used in MapReduce and is compatible with existing MapReduce applications. We demonstrate that, in common cases, Camdoop significantly reduces the network traffic and provides high performance
increase over a version of Camdoop running over a switch and against two production systems, Hadoop and Dryad/DryadLINQ.