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Boosting Vertex-Cut Partitioning For Streaming Graphs
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
(SICS Swedish ICT)
(SICS Swedish ICT)
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
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2016 (English)In: Big Data (BigData Congress), 2016 IEEE International Congress on, IEEE conference proceedings, 2016, 1-8 p.Conference paper, Published paper (Refereed)
Abstract [en]

While the algorithms for streaming graph partitioning are proved promising, they fall short of creating timely partitions when applied on large graphs. For example, it takes 415 seconds for a state-of-the-art partitioner to work on a social network graph with 117 millions edges. We introduce an efficient platform for boosting streaming graph partitioning algorithms. Our solution, called HoVerCut, is Horizontally and Vertically scalable. That is, it can run as a multi-threaded process on a single machine, or as a distributed partitioner across multiple machines. Our evaluations, on both real-world and synthetic graphs, show that HoVerCut speeds up the process significantly without degrading the quality of partitioning. For example, HoVerCut partitions the aforementioned social network graph with 117 millions edges in 11 seconds that is about 37 times faster

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016. 1-8 p.
Keyword [en]
streaming graph, vertex-cut partitioning, graph partitioning, parallel scalability
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-189864DOI: 10.1109/BigDataCongress.2016.10ISI: 000390212200001Scopus ID: 2-s2.0-84994558691ISBN: 978-1-5090-2622-7 (print)OAI: oai:DiVA.org:kth-189864DiVA: diva2:949508
Conference
5th 2016 IEEE International Congress on Big Data (BigData Congress 2016)
Note

QC 20160923

Available from: 2016-07-20 Created: 2016-07-20 Last updated: 2017-02-23Bibliographically approved
In thesis
1. Towards Unifying Stream Processing over Central and Near-the-Edge Data Centers
Open this publication in new window or tab >>Towards Unifying Stream Processing over Central and Near-the-Edge Data Centers
2016 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

In this thesis, our goal is to enable and achieve effective and efficient real-time stream processing in a geo-distributed infrastructure, by combining the power of central data centers and micro data centers. Our research focus is to address the challenges of distributing the stream processing applications and placing them closer to data sources and sinks. We enable applications to run in a geo-distributed setting and provide solutions for the network-aware placement of distributed stream processing applications across geo-distributed infrastructures.

 First, we evaluate Apache Storm, a widely used open-source distributed stream processing system, in the community network Cloud, as an example of a geo-distributed infrastructure. Our evaluation exposes new requirements for stream processing systems to function in a geo-distributed infrastructure. Second, we propose a solution to facilitate the optimal placement of the stream processing components on geo-distributed infrastructures. We present a novel method for partitioning a geo-distributed infrastructure into a set of computing clusters, each called a micro data center. According to our results, we can increase the minimum available bandwidth in the network and likewise, reduce the average latency to less than 50%. Next, we propose a parallel and distributed graph partitioner, called HoVerCut, for fast partitioning of streaming graphs. Since a lot of data can be presented in the form of graph, graph partitioning can be used to assign the graph elements to different data centers to provide data locality for efficient processing. Last, we provide an approach, called SpanEdge that enables stream processing systems to work on a geo-distributed infrastructure. SpenEdge unifies stream processing over the central and near-the-edge data centers (micro data centers). As a proof of concept, we implement SpanEdge by extending Apache Storm that enables it to run across multiple data centers.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. 33 p.
Series
TRITA-ICT, 2016:27
Keyword
geo-distributed stream processing, geo-distributed infrastructure, edge computing, edge-based analytics
National Category
Computer and Information Science
Research subject
Information and Communication Technology
Identifiers
urn:nbn:se:kth:diva-193582 (URN)978-91-7729-118-3 (ISBN)
Presentation
2016-11-14, Sal 208, Electrum, Kungl Tekniska högskolan, Kistagången 16, Kista, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20161005

Available from: 2016-10-05 Created: 2016-10-04 Last updated: 2016-10-12Bibliographically approved

Open Access in DiVA

Boosting Vertex-Cut Partitioning For Streaming Graphs(745 kB)60 downloads
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File name FULLTEXT01.pdfFile size 745 kBChecksum SHA-512
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Type fulltextMimetype application/pdf

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Publisher's full textScopushttp://www.ieeebigdata.org/2016/cfp.html

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • asciidoc
  • rtf