Availability and Network-Aware MapReduce Task Scheduling over the Internet

Bing Tang (1) , Qi Xie (2) , Haiwu He (3) , Gilles Fedak (4, 5)

1 School of Computer Science and Engineering [ Changsha]
2 Department of Plant and Environmental Sciences
3 CNIC - Computer Network Information Center [Beijing]
4 LIP - Laboratoire de l'Informatique du Parallélisme
5 AVALON - Algorithms and Software Architectures for Distributed and HPC Platforms

MapReduce offers an ease-of-use programming paradigm for processing large datasets. In our previous work, we have designed a MapReduce framework called BitDew-MapReduce for desktop grid and volunteer computing environment, that allows nonexpert users to run data-intensive MapReduce jobs on top of volunteer resources over the Internet. However, network distance and resource availability have great impact on MapReduce applications running over the Internet. To address this, an availability and network-aware MapReduce framework over the Internet is proposed. Simulation results show that the MapReduce job response time could be decreased by 27.15%, thanks to Naive Bayes Classifier-based availability prediction and landmark-based network estimation .

Mots clés: MapReduce; Volunteer Computing; Availability Prediction; Network Distance Prediction; Naive Bayes Classifier

Domaines: Calcul parallèle, distribué et partagé [cs.DC]

Bing Tang, Qi Xie, Haiwu He, Gilles Fedak. Availability and Network-Aware MapReduce Task Scheduling over the Internet. Algorithms and Architectures for Parallel Processing, Dec 2015, Zhangjiajie, China. ⟨10.1007/978-3-319-27119-4_15⟩. ⟨hal-01256183⟩