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An Elasticity Study of Distributed Graph Processing. / Au, Sietse; Uta, Alexandru; Ilyushkin, Alexey; Iosup, Alexandru.

2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). ed. / L. O'Conner. Piscataway, NJ : IEEE, 2018. p. 382-383.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Au, S, Uta, A, Ilyushkin, A & Iosup, A 2018, An Elasticity Study of Distributed Graph Processing. in L O'Conner (ed.), 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). IEEE, Piscataway, NJ, pp. 382-383, CCGRID 2018, Washington, DC, United States, 1/05/18. https://doi.org/10.1109/CCGRID.2018.00062

APA

Au, S., Uta, A., Ilyushkin, A., & Iosup, A. (2018). An Elasticity Study of Distributed Graph Processing. In L. O'Conner (Ed.), 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) (pp. 382-383). Piscataway, NJ: IEEE. https://doi.org/10.1109/CCGRID.2018.00062

Vancouver

Au S, Uta A, Ilyushkin A, Iosup A. An Elasticity Study of Distributed Graph Processing. In O'Conner L, editor, 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). Piscataway, NJ: IEEE. 2018. p. 382-383 https://doi.org/10.1109/CCGRID.2018.00062

Author

Au, Sietse ; Uta, Alexandru ; Ilyushkin, Alexey ; Iosup, Alexandru. / An Elasticity Study of Distributed Graph Processing. 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). editor / L. O'Conner. Piscataway, NJ : IEEE, 2018. pp. 382-383

BibTeX

@inproceedings{e97245d97a9c47d58ca8f86f798b3fac,
title = "An Elasticity Study of Distributed Graph Processing",
abstract = "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.",
author = "Sietse Au and Alexandru Uta and Alexey Ilyushkin and Alexandru Iosup",
note = "Accepted author manuscript",
year = "2018",
doi = "10.1109/CCGRID.2018.00062",
language = "English",
isbn = "978-1-5386-5816-1",
pages = "382--383",
editor = "L. O'Conner",
booktitle = "2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)",
publisher = "IEEE",
address = "United States",

}

RIS

TY - GEN

T1 - An Elasticity Study of Distributed Graph Processing

AU - Au, Sietse

AU - Uta, Alexandru

AU - Ilyushkin, Alexey

AU - Iosup, Alexandru

N1 - Accepted author manuscript

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

U2 - 10.1109/CCGRID.2018.00062

DO - 10.1109/CCGRID.2018.00062

M3 - Conference contribution

SN - 978-1-5386-5816-1

SP - 382

EP - 383

BT - 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)

A2 - O'Conner, L.

PB - IEEE

CY - Piscataway, NJ

ER -

ID: 46652034