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  1. ACM SIGCOMM Computer Communication Review (CCRV)
  2. Volume 45
  3. Volume 45, Issue 5, October 2015
  4. Datix
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Volume 47
Volume 46
Volume 45
Volume 45, Issue 5, October 2015
CrowdSurf
Edge-centric Computing
Making the Case for Elliptic Curves in DNSSEC
ACM SIGCOMM Student Mentoring Column
Datix
Examples of Research Affecting the Practice of Networking
ReWiFlow
On Technology Transfer
Volume 45, Issue 4, October 2015
Volume 45, Issue 3, July 2015
Volume 45, Issue 2, April 2015
Volume 45, Issue 1, January 2015
Volume 44
Volume 43
Volume 42
Volume 41
Volume 40
Volume 39
Volume 38
Volume 37
Volume 36
Volume 35
Volume 34
Volume 33
Volume 32
Volume 31
Volume 30
Volume 29
Volume 28
Volume 27
Volume 26
Volume 25
Volume 24
Volume 23
Volume 22
Volume 21
Volume 20
Volume 19
Volume 18
Volume 17
Volume 16
Volume 15
Volume 13-14
Volume 14
Volume 13
Volume 12
Volume 11
Volume 10
Volume 9
Volume 8
Volume 7
Volume 6
Volume 5
Volume 4
Volume 3
Volume 2
Volume 1

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Datix:A System for Scalable Network Analytics

Content Provider ACM Digital Library
Author Papailiou, Nikolaos Smaragdakis, Georgios Konstantinou, Ioannis Sarlis, Dimitrios Koziris, Nectarios
Abstract The ever-increasing Internet traffic poses challenges to network operators and administrators that have to analyze large network datasets in a timely manner to make decisions regarding network routing, dimensioning, accountability and security. Network datasets collected at large networks such as Internet Service Providers (ISPs) or Internet Exchange Points (IXPs) can be in the order of Terabytes per hour. Unfortunately, most of the current network analysis approaches are ad-hoc and centralized, and thus not scalable. In this paper, we present Datix, a fully decentralized, open-source analytics system for network traffic data that relies on smart partitioning storage schemes to support fast join algorithms and efficient execution of filtering queries. We outline the architecture and design of Datix and we present the evaluation of Datix using real traces from an operational IXP. Datix is a system that deals with an important problem in the intersection of data management and network monitoring while utilizing state-of-the-art distributed processing engines. In brief, Datix manages to efficiently answer queries within minutes compared to more than 24 hours processing when executing existing Python based code in single node setups. Datix also achieves nearly 70% speedup compared to baseline query implementations of popular big data analytics engines such as Hive and Shark.
Starting Page 21
Ending Page 28
Page Count 8
File Format PDF
ISSN 01464833
DOI 10.1145/2831347.2831351
Journal ACM SIGCOMM Computer Communication Review (CCRV)
Volume Number 45
Issue Number 5
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 1993-07-01
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Sflow K-d tree Hadoop Map-join Mapreduce Hbase
Content Type Text
Resource Type Article
Subject Computer Networks and Communications Software
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