Programmable data plane hardware creates new opportunities for infusing intelligence into the network. DAIET (Data Aggregation In nETwork) proposes to leverage this new technology to offload part of the computation of distributed applications to execute in-network.

However, in-network computation tasks must be judiciously crafted to match the limitations of the network machine architecture of programmable devices. With the help of our experiments on machine learning and graph analytics workloads, we identify that aggregation functions raise opportunities to exploit the limited computation power of networking hardware to lessen network congestion and improve the overall application performance. Moreover, we propose a proof-of-concept system that performs in-network data aggregation. Experimental results with an initial system shows a large data reduction ratio (86.9%-89.3%) and a similar decrease in the workers’ computation time.

Chen-Yu Ho
Ph.D. Student

My research interests lie in distributed machine learning systems.