Many scalable data center applications follow a partition-aggregate pattern where data and computations are distributed among many servers and their partial results are exchanged over the network and aggregated to produce the final output. For these workloads, the network communication costs are often one of the dominant scalability bottlenecks.
To improve performance, we present DAIET, a system that performs data aggregation along network paths by leveraging emerging programmable network devices. By offloading part of the aggregation task to the networking hardware, DAIET not only reduces the traffic at each network device, but also allows to reduce the computation done by the CPU, effectively decreasing job completion times. We describe a preliminary prototype of DAIET using P4 to perform in-network aggregation for MapReduce-based applications. Experimental results show that our approach achieves around 88% data reduction ratio and a similar decrease in the reducer running time, without requiring severe application-level modifications.
If you use DAIET in your research, please cite our paper:
@inproceedings{sapio2017daiet,
title={In-Network Computation is a Dumb Idea Whose Time Has Come},
author={Amedeo Sapio, Ibrahim Abdelaziz, Abdulla Aldilaijan, Marco Canini, and Panos Kalnis},
booktitle={Proceedings of the 16th ACM Workshop on Hot Topics in Networks (HotNets)},
year={2017},
organization={ACM}
}
- DAIET project website: sands.kaust.edu.sa/daiet/
The main changes from the original simpleMR are:
- Communication Layer (TCP to UDP)
- Message Serialization
- Sorting intermediate results
cd simplemr
mvn package
chmod +x dist/bin/*
cd ..See Detailed Steps: https://github.com/AmedeoSapio/DAIET