An elasticity study of distributed graph processing

Sietse Au, Alexandru Uta, Alexey Ilyushkin, Alexandru Iosup

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Graphs are a natural fit for modeling concepts used in solving diverse problems in science, commerce, engineering, and governance. Responding to the variety 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 a dynamic model of deployment. We first characterize workload dynamicity, beyond mere active-vertex variability. Then, to conduct an in-depth elasticity study of distributed graph processing, we build a prototype, JoyGraph, which is the first such system that implements complex, policy-based, and fine-grained elasticity. Using the state-of-the-art LDBC Graphalytics benchmark and the SPEC Cloud Group's elasticity metrics, we show the benefits of elasticity in graph processing: (i) improved resource utilization, (ii) reduced operational costs, and (iii) aligned operation-workload dynamicity. Furthermore, we explore the cost of elasticity in graph processing. We identify a key drawback: although elasticity does not degrade application throughput, graph-processing workloads are sensitive to data movement while leasing or releasing resources.

Original languageEnglish
Title of host publicationProceedings - 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages2
ISBN (Electronic)9781538658154
Publication statusPublished - 13 Jul 2018
Event18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2018 - Washington, United States
Duration: 1 May 20184 May 2018


Conference18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2018
Country/TerritoryUnited States


Supported by Vidi MagnaData (NL) and Oracle Labs (USA).

FundersFunder number
Oracle Labs
Vidi MagnaData


    • Auto sclaing
    • Autoscaling
    • Cost
    • Distributed graph processing
    • Elasticity
    • Graph
    • Graph processing
    • Graphalytics
    • Joygraph
    • SPEC
    • Utilization


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