KAUST DepartmentComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Extreme Computing Research Center
Online Publication Date2017-11-27
Print Publication Date2017
Permanent link to this recordhttp://hdl.handle.net/10754/627118
MetadataShow full item record
AbstractProgrammable data plane hardware creates new opportunities for infusing intelligence into the network. This raises a fundamental question: what kinds of computation should be delegated to the network? In this paper, we discuss the opportunities and challenges for co-designing data center distributed systems with their network layer. We believe that the time has finally come for offloading part of their computation 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, as a proof-of-concept, we propose DAIET, a system that performs in-network data aggregation. Experimental results with an initial prototype show a large data reduction ratio (86.9%-89.3%) and a similar decrease in the workers' computation time.
CitationSapio A, Abdelaziz I, Aldilaijan A, Canini M, Kalnis P (2017) In-Network Computation is a Dumb Idea Whose Time Has Come. Proceedings of the 16th ACM Workshop on Hot Topics in Networks - HotNets-XVI. Available: http://dx.doi.org/10.1145/3152434.3152461.
SponsorsWe thank the anonymous reviewers for their feedback. We are grateful to Colin Dixon, Changhoon Kim, Jeongkeun Lee, Jeff Mogul, KyoungSoo Park and Amin Vahdat for their valuable comments and suggestions. We further thank Jeff for inspiring the title of this paper.
Conference/Event name16th ACM Workshop on Hot Topics in Networks, HotNets 2017