Offloading Online MapReduce tasks with Stateful Programmable Data Planes

In-network computation based on programmable data plane hardware provides a tremendous opportunity to improve throughput, latency and reduce congestion in data center scenarios. However, a judicious use of these network devices must be done based on their limited resources and the specific features of the application to be offloaded. This paper promotes FlowBlaze, a stateful hardware programmable data plane, as a candidate for offloading online MapReduce tasks. Above all, tasks with strict time requirements can benefit from in-network computing since it can significantly lower their latency. Given that MapReduce is a generic programming paradigm, in this paper we first try to identify which subset of MapReduce operations can be transparently offloaded to a specific hardware architecture and which are the limitations of this offloading in terms of memory and computational resources. After, we show how the FlowBlaze architecture can match the partition/aggregation paradigm and we discuss a set of primitives exposed by the FlowBlaze abstraction to perform mapping and aggregation. Finally, we prove the feasibility of this approach applying it to a click-stream analysis use case.