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Optimizing Windowed Aggregation over Geo-Distributed Data Streams
KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
Stockholm University.
KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
2018 (English)In: 2018 IEEE International Conference on Edge Computing (EDGE), IEEE Computer Society Digital Library, 2018, p. 33-41Conference paper, Published paper (Refereed)
Abstract [en]

Real-time data analytics is essential since more and more applications require online decision making in a timely manner. However, efficient analysis of geo-distributed data streams is challenging. This is because data needs to be collected from all edge data centers, which aggregate data from local sources, in order to process most of the analytic tasks. Thus, most of the time edge data centers need to transfer data to a central data center over a wide area network, which is expensive. In this paper, we advocate for a coordinated approach of edge data centers in order to handle these analytic tasks efficiently and hence, reducing the communication cost among data centers. We focus on the windowed aggregation of data streams, which has been widely used in stream analytics. In general, aggregation of data streams among edge data centers in the same region reduces the amount of data that needs to be sent over cross-region communication links. Based on state-of-the-art research, we leverage intra-region links and design a low-overhead coordination algorithm that optimizes communication cost for data aggregation. Our algorithm has been evaluated using synthetic and Big Data Benchmark datasets. The evaluation results show that our algorithm reduces the bandwidth cost up to ~6x, as compared to the state-of-the-art solution.

Place, publisher, year, edition, pages
IEEE Computer Society Digital Library, 2018. p. 33-41
Keywords [en]
data analytics, stream processing, aggregation, WAN analytics
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-236051DOI: 10.1109/EDGE.2018.00012ISI: 000447289500005Scopus ID: 2-s2.0-85055625599ISBN: 978-1-5386-7238-9 (electronic)OAI: oai:DiVA.org:kth-236051DiVA, id: diva2:1255658
Conference
2018 IEEE International Conference on Edge Computing (EDGE)San Francisco, CA, USA
Note

QC 20181022

Available from: 2018-10-14 Created: 2018-10-14 Last updated: 2019-02-08Bibliographically approved
In thesis
1. Methods and Algorithms for Data-Intensive Computing: Streams, Graphs, and Geo-Distribution
Open this publication in new window or tab >>Methods and Algorithms for Data-Intensive Computing: Streams, Graphs, and Geo-Distribution
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Struggling with the volume and velocity of Big Data has attracted lots of interest towards stream processing paradigm, a paradigm in the area of data-intensive computing that provides methods and solutions to process data in motion. Today's Big Data includes geo-distributed data sources.In addition, a major part of today's Big Data requires exploring complex and evolving relationships among data, which complicates any reasoning on the data. This thesis aims at challenges raised by geo-distributed streaming data, and the data with complex and evolving relationships.

Many organizations provide global scale applications and services that are hosted on servers and data centers that are located in different parts of the world. Therefore, the data that needs to be processed are generated in different geographical locations. This thesis advocates for distributed stream processing in geo-distributed settings to improve the performance including better response time and lower network cost compared to centralized solutions. In this thesis, we conduct an experimental study of Apache Storm, a widely used open-source stream processing system, on a geo-distributed infrastructure made of near-the-edge resources. The resources that host the system's components are connected by heterogeneous network links. Our study exposes a set of issues and bottlenecks of deploying a stream processing system on the geo-distributed infrastructure. Inspired by the results, we propose a novel method for grouping of geo-distributed resources into computing clusters, called micro data centers, in order to mitigate the effect of network heterogeneity for distributed stream processing applications. Next, we focus on the windowed aggregation of geo-distributed data streams, which has been widely used in stream analytics. We propose to reduce the bandwidth cost by coordinating windowed aggregations among near-the-edge data centers. We leverage intra-region links and design a novel low-overhead coordination algorithm that optimizes communication cost for data aggregation. Then, we propose a system, called SpanEdge, that provides an expressive programming model to unify programming stream processing applications on a geo-distributed infrastructure and provides a run-time system to manage (schedule and execute) stream processing applications across data centers. Our results show that SpanEdge can optimally deploy stream processing applications in a geo-distributed infrastructure, which significantly reduces the bandwidth consumption and response latency.

With respect to data with complex and evolving relationships, this thesis aims at effective and efficient processing of inter-connected data. There exist several domains such as social network analysis, machine learning, and web search in which data streams are modeled as linked entities of nodes and edges, namely a graph. Because of the inter-connection among the entities in graph data, processing of graph data is challenging. The inter-connection among the graph entities makes it difficult to distribute the graph among multiple machines to process the graph at scale. Furthermore, in a streaming setting, the graph structure and the graph elements can continuously change as the graph elements are streamed. Such a dynamic graph requires incremental computing methods that can avoid redundant computations on the whole graph. This thesis proposes incremental computing methods of streaming graph processing that can boost the processing time while still obtaining high quality results. In this thesis, we introduce HoVerCut, an efficient framework for boosting streaming graph partitioning algorithms. HoVerCut is Horizontally and Vertically scalable. Our evaluations show that HoVerCut speeds up the partitioning process significantly without degrading the quality of partitioning. Finally, we study unsupervised representation learning in dynamic graphs. Graph representation learning seeks to learn low dimensional vector representations for the graph elements, i.e. edges and vertices, and the whole graph.We propose novel and computationally efficient incremental algorithms. The computation complexity of our algorithms depends on the extent and rate of changes in a graph and on the graph density. The evaluation results show that our proposed algorithms can achieve competitive results to the state-of-the-art static methods while being computationally efficient.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2019. p. 58
Series
TRITA-EECS-AVL ; 2019:13
Keywords
stream processing, geo-distributed infrastructure, edge computing, streaming graph, dynamic graph
National Category
Computer and Information Sciences
Research subject
Information and Communication Technology; Computer Science
Identifiers
urn:nbn:se:kth:diva-243883 (URN)978-91-7873-094-0 (ISBN)
Public defence
2019-03-15, Ka-Sal C (Sal Sven-Olof Öhrvik), Electrum, Kista, Stockholm, 13:30 (English)
Opponent
Supervisors
Note

QC 20190208

Available from: 2019-02-08 Created: 2019-02-08 Last updated: 2019-02-08Bibliographically approved

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