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  1. Proceedings of the 2013 SIGMOD/PODS Ph.D. symposium (SIGMOD'13 PhD Symposium)
  2. Exploiting in-network processing for big data management
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Exploiting in-network processing for big data management
Learning queries for relational, semi-structured, and graph databases
Exploratory mining of collaborative social content
DeepSea: self-adaptive data partitioning and replication in scalable distributed data systems
Turning scientists into data explorers
Discovering and disambiguating named entities in text
Designing a database system for modern processing architectures
Information diffusion in online social networks
The tantalizing new prospect of index-based diversified retrieval
Effective hashing for large-scale multimedia search
Efficient and scalable monitoring and summarization of large probabilistic data
RDF-4G: algorithmic building blocks for large-scale graph analytics

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Exploiting in-network processing for big data management

Content Provider ACM Digital Library
Author Rupprecht, Lukas
Abstract Data processing systems face the task of efficiently storing and processing data at petabyte scale, with the amount set to increase in the future. To meet such a requirement, highly scalable, shared-nothing systems, e.g. Google's BigTable [6] or Facebook's Cassandra [14], are built to partition data and process it in parallel on distributed nodes in a cluster. This allows the handling of data at scale but introduces new challenges due to the distribution of data. Running queries involves a high network overhead because data has to be exchanged between cluster nodes and hence, the network becomes a critical part of the system. To avoid the network bottleneck, it is essential for distributed data processing systems (DDPS) to be aware of the network rather than treating it as a black box. We propose in-network processing as a way of achieving network-awareness to decrease bandwidth usage by custom routing, redundancy elimination, and on-path data reduction. Thereby, we can increase the query throughput of a DDPS. The challenges of an in-network processing system range from design issues, such as performance and transparency, to the integration with query optimisation and deployment in data centres. We formulate these challenges as possible research directions and provide a prototype implementation. Our preliminary results suggest that we can significantly improve query throughput in a DDPS by performing partial data reduction within the network.
Starting Page 1
Ending Page 6
Page Count 6
File Format PDF
ISBN 9781450321556
DOI 10.1145/2483574.2483579
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-06-23
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Nosql Network-awareness Scale-out Date centres
Content Type Text
Resource Type Article
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