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Harnessing Data Movement in Virtual Clusters for In-Situ Execution
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DOI：10.1109/TPDS.2018.2867879.png)
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Abstract
En 中文
As a result of increasing data volume and velocity, Big Data science at exascale has shifted towards the in-situ paradigm, where large scale simulations run concurrently alongside data analytics. With in-situ, data generated from simulations can be processed while still in memory, thereby avoiding the slow storage bottleneck. However, running simulations and analytics together on shared resources will likely result in substantial contention if left unmanaged, as demonstrated in this work, leading to much reduced efficiency of simulations and analytics. Recently, virtualization technologies such as Linux containers have been widely applied to data centers and physical clusters to provide highly efficient and elastic resource provisioning for consolidated workloads including scientific simulations and data analytics. In this paper, we investigate to facilitate network traffic manipulation and reduce mutual interference on the network for in-situ applications in virtual clusters. In order to dynamically allocate the network bandwidth when it is needed, we adopt SARIMA-based techniques to analyze and predict MPI traffic issued from simulations. Although this can be an effective technique, the naive usage of network virtualization can lead to performance degradation for bursty asynchronous transmissions within an MPI job. We analyze and resolve this performance degradation in virtual clusters.
Keywords:
In-situ applications
virtual network
virtual switch
ARIMA
collective communication
MPI
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