Elasticity in Graph Analytics? A Benchmarking Framework for Elastic Graph Processing

Alexandru Uta, Sietse Au, Alexey Ilyushkin, Alexandru Iosup

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Abstract

Graphs are a natural fit for modeling concepts used in solving diverse problems in science, commerce, engineering, and governance. Responding to the diversity of graph data and algorithms, many parallel and distributed graph-processing systems exist. However, until now these platforms use a static model of deployment: they only run on a pre-defined set of machines. This raises many conceptual and pragmatic issues, including misfit with the highly dynamic nature of graph processing, and could lead to resource waste and high operational costs. In contrast, in this work we explore the benefits and drawbacks of the dynamic model of deployment. Building a three-layer benchmarking framework for assessing elasticity in graph analytics, we conduct an in-depth elasticity study of distributed graph processing. Our framework is composed of state-of-the-art workloads, autoscalers, and metrics, derived from the LDBC Graphalytics benchmark and SPEC RG Cloud Group's elasticity metrics. We uncover the benefits and cost of elasticity in graph processing: while elasticity allows for fine-grained resource management, and does not degrade application performance, we find that graph workloads are sensitive to data migration while leasing or releasing resources. Moreover, we identify non-trivial interactions between scaling policies and graph workloads, which add an extra level of complexity to resource management and scheduling for graph processing.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Cluster Computing, CLUSTER 2018
PublisherInstitute of Electrical and Electronics Engineers, Inc.
Pages381-391
Number of pages11
ISBN (Electronic)9781538683194
DOIs
Publication statusPublished - 1 Nov 2018
Event2018 IEEE International Conference on Cluster Computing, CLUSTER 2018 - Belfast, United Kingdom
Duration: 10 Sep 201813 Sep 2018

Conference

Conference2018 IEEE International Conference on Cluster Computing, CLUSTER 2018
CountryUnited Kingdom
CityBelfast
Period10/09/1813/09/18

Keywords

  • Benchmark
  • Dynamic scaling
  • Elasticity
  • Elasticity metrics
  • Graph analytics
  • Graph processing

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    Uta, A., Au, S., Ilyushkin, A., & Iosup, A. (2018). Elasticity in Graph Analytics? A Benchmarking Framework for Elastic Graph Processing. In Proceedings - 2018 IEEE International Conference on Cluster Computing, CLUSTER 2018 (pp. 381-391). [8514898] Institute of Electrical and Electronics Engineers, Inc.. https://doi.org/10.1109/CLUSTER.2018.00056