Harnessing Data Movement in Virtual Clusters for In-Situ Execution

Document Type

Article

Publication Date

3-1-2019

Abstract

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 naïve 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.

Identifier

85052620422 (Scopus)

Publication Title

IEEE Transactions on Parallel and Distributed Systems

External Full Text Location

https://doi.org/10.1109/TPDS.2018.2867879

e-ISSN

15582183

ISSN

10459219

First Page

615

Last Page

629

Issue

3

Volume

30

Grant

CCF-1337244

Fund Ref

National Science Foundation

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