Scaling topology pattern matching

Stein, Michael; Frömmgen, Alexander; Kluge, Roland; Wang, Lin; Wilberg, Augustin; Koldehofe, Boris; Mühlhäuser, Max

doi:10.1145/3167132.3167241

Cited by 4 publications

(5 citation statements)

References 28 publications

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“…In the following, we discuss our MACI experiences. We greatly benefited from MACI during the development and evaluation in recent research projects on Multipath TCP scheduling [11,12,10,33], DASH video streaming [31], topology graph pattern matching algorithms [28], and the supervision of student theses. We further reproduced the results of a notable Multipath TCP experimental design study [22].…”

Section: Experiences and Resultsmentioning

confidence: 99%

“…In this paper, we presented MACI, a framework for the management, the scalable execution, and the interactive analysis of a large number of network experiments. MACI significantly reduced repetitive tasks and increased the quality of the obtained results in various application scenarios [10,11,12,28,31,33]. MACI provided all evaluation process specific functionalities and allowed us to focus on research.…”

Section: Discussionmentioning

confidence: 99%

“…We discuss the benefits of MACI based on our experience with three research projects: i) an extensive DASH video streaming study [31], ii) the development of various Multipath TCP packet schedulers [12,10], and iii) the tuning of a distributed topology graph pattern matching protocol [28].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Don't Repeat Yourself: Seamless Execution and Analysis of Extensive Network Experiments

Frömmgen¹,

Stohr²,

Koldehofe³

et al. 2018

Preprint

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This paper presents MACI, the first bespoke framework for the management, the scalable execution, and the interactive analysis of a large number of network experiments. Driven by the desire to avoid repetitive implementation of just a few scripts for the execution and analysis of experiments, MACI emerged as a generic framework for network experiments that significantly increases efficiency and ensures reproducibility. To this end, MACI incorporates and integrates established simulators and analysis tools to foster rapid but systematic network experiments.We found MACI indispensable in all phases of the research and development process of various communication systems, such as i) an extensive DASH video streaming study, ii) the systematic development and improvement of Multipath TCP schedulers, and iii) research on a distributed topology graph pattern matching algorithm. With this work, we make MACI publicly available to the research community to advance efficient and reproducible network experiments.

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Section: Experiences and Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Don't Repeat Yourself: Seamless Execution and Analysis of Extensive Network Experiments

Frömmgen¹,

Stohr²,

Koldehofe³

et al. 2018

Preprint

Self Cite

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“…In [78], a vertex-centric approach was presented for performing topology pattern matching (subgraph isomorphism) in a distributed environment composed of a Wireless Sensor Network (WSN). Each sensor in this WSN is analogous to a data vertex having a partial view of its closest neighbors in the network topology.…”

Section: Synchronous Vertex-centricmentioning

confidence: 99%

“…Vertex-centric 68B nodes [67] 2018 Subgraph isomorphism Async. Vertex-centric 137B nodes [78] 2018 Subgraph isomorphism Async. Vertex-centric 400 nodes [24] 2018 ISO and GSIM BSP Master-slave 65M nodes [66] 2020 Inexact ISO Async.…”

Section: Work Year Modelmentioning

confidence: 99%

A Survey on Distributed Graph Pattern Matching in Massive Graphs

et al. 2021

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Besides its NP-completeness, the strict constraints of subgraph isomorphism are making it impractical for graph pattern matching (GPM) in the context of big data. As a result, relaxed GPM models have emerged as they yield interesting results in a polynomial time. However, massive graphs generated by mostly social networks require a distributed storing and processing of the data over multiple machines, thus, requiring GPM to be revised by adopting new paradigms of big graphs processing, e.g., Think-Like-A-Vertex and its derivatives. This article discusses and proposes a classification of distributed GPM approaches with a narrow focus on the relaxed models.

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