Optimizing data movement has always been one of the key ways to get a data processing system to perform efficiently. Appearing under different disguises as computers evolved over the years, the issue is today as relevant as ever. With the advent of the cloud, data movement has become the bottleneck to address in any data processing system. In the cloud, compute and storage are typically disaggregated, with a network in between. In addition, cloud systems are scale-out, i.e., performance is obtained by parallelizing across machines, which also involves network communication. And while it is possible to use machines with large amounts of memory, the pricing models and the virtualized nature of the cloud tends to favor clusters of smaller computing nodes. Nowadays, the problem of optimizing data movement has become the problem of using the network as efficiently as possible.

优化数据移动一直是使数据处理系统高效运行的关键方法之一。随着计算机多年来的发展，以不同的伪装出现，这个问题在今天与以往一样重要。随着云的出现，数据移动已成为任何数据处理系统中需要解决的瓶颈。在云中，计算和存储通常是分开的，中间有一个网络。此外，云系统是横向扩展的，即通过跨机器并行获得性能，这也涉及网络通信。虽然可以使用具有大量内存的机器，但定价模型和云的虚拟化性质往往有利于较小计算节点的集群。如今，