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Smart Partitioning of Geo-Distributed Resources to Improve Cloud Network Performance
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
SICS Swedish ICT.
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
2015 (English)Conference paper (Refereed)
Abstract [en]

Cloud Computing systems with geo-distributed re- sources are becoming more popular for enabling a new family of applications, which are latency sensitive or bandwidth intensive, e.g., Internet of Things and online video gaming services. The approach is to host the cloud services at the network edges to reduce the latency and bandwidth consumption. However, the topology of the existing networks is not necessarily optimal for hosting Cloud services. Moreover, how the services are placed on the nodes, can affect the performance of the applications and the whole network. Therefore, we propose a novel algorithm to partition a distributed infrastructure into a set of computing clusters, each called a Micro Data Center. Our proposed algorithm is a decentralized community detection algorithm that does not require any global knowledge of the network topology. We compare our solution with a geolocation based clustering solution and demonstrate our preliminary results based on a real world network data set. We show that micro data centers increase the minimum available bandwidth in the network to up to 62%. Likewise, the average latency can be reduced to 50%.

Place, publisher, year, edition, pages
Keyword [en]
geo-distributed cloud; community detection; cloud network performance; multiple data centers
National Category
Computer Systems
URN: urn:nbn:se:kth:diva-174381DOI: 10.1109/CloudNet.2015.7335292ISI: 000377207000022ScopusID: 2-s2.0-84960981479OAI: diva2:859384
The 2015 4th IEEE International Conference on Cloud Networking (IEEE CloudNet 2015)5-7 October 2015, Niagara Falls, Canada

QC 20160616

Available from: 2015-10-06 Created: 2015-10-06 Last updated: 2016-10-04Bibliographically 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.
TRITA-ICT, 2016:27
geo-distributed stream processing, geo-distributed infrastructure, edge computing, edge-based analytics
National Category
Computer and Information Science
Research subject
Information and Communication Technology
urn:nbn:se:kth:diva-193582 (URN)978-91-7729-118-3 (ISBN)
2016-11-14, Sal 208, Electrum, Kungl Tekniska högskolan, Kistagången 16, Kista, Stockholm, 13:00 (English)

QC 20161005

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

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