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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.

XTRA (XFSM for Transport) aims at providing a first attempt towards a “ code-once-port-everywhere ” platform-agnostic programming abstraction tailored to the deployment of transport layer functions. XTRA’s programming abstraction not only fits SW platforms, but is specifically designed to harness, with no re-coding effort, the offloading opportunities offered by CPU-less HW boards or smart NICs. We demonstrate the viability of XTRA with three completely different implementations of the underlying execution engine (HW proof-of-concept on a NetFPGA board, User-space SW over Linux’ Open Data Plane, and NS3 emulator). Flexibility is shown via a number of example applications, ranging from a variety of congestion control algorithms, to a middlebox-type TCP proxy functionality, up to a customized “Timer-Based” (TB) TCP which leverages the native reliance of XTRA on timers, so as to produce a loss recovery operation which, despite being formalized only via a handful of code lines, performs almost comparable with the highly optimized Linux and FreeBSD implementations.