Distributed Network Traffic Monitoring and Analysis using Load Balancing Technology

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1 Distributed Network Traffic Monitoring and Analysis using Load Balancing Technology Soon-Hwa Hong, Jae-Young Kim, Bum-Rae Cho and James W. Hong Dept. of Computer Science and Engineering, Pohang Korea {padosori, jay, brcho,

2 Introduction Network traffic (text, image, software, audio, video) is increasing continuously both on the Internet and Intranet. A simple, accurate and efficient network traffic monitoring and analysis is required to understand the current usage as well as to plan for future. Many shortcomings exist in currently available monitoring systems. cannot analyze long-term traffic. do not have monitoring capability from multiple network points. capture, analysis and presentation all in one machine. cannot prevent packet drops from the system overload. attempts to overcome these shortcomings using distributed architecture and load balancing technique. (2/16)

3 Related Work Capture Method Analysis Method Analysis Interval Analysis Scope Support for Multiple network points Distributed Architecture User Interface tcpdump libpcap real time current layer 7 no no Text ntop libpcap batch 5 second, hourly layer 7 no no Web ethereal libpcap real time, batch current, user-specified time layer 7 no no X- Windows MRTG snmp agent batch 5 minute, hourly, daily, weekly, monthly layer 2 yes yes Web WebTrafMon libpcap real time, batch current, hourly layer 7 no no Web libpcap real time, batch current, hourly, daily, monthly, yearly layer 7 yes yes Web (3/16)

4 Requirements analyze various types of information: host information, network, transport, and application layer protocols. analyze present real-time, hourly, daily, monthly and yearly network traffic data automatically. analyze multiple network points traffic. no packet drops. web-based graphical user interface. (4/16)

5 Centralized vs. Distributed Centralized Traffic Analysis Architecture Distributed Traffic Analysis Architecture analysis distributed environment analysis packet header information network traffic data packet header information network traffic data capture presentation capture presentation network point user analyzed information network point user analyzed information system overload occurs frequently and many packets are dropped cannot support for multiple network points presentation time is slow capture, analysis and presentation modules execute on separate machines to minimize system overload can support for multiple network points presentation time is fast (5/16)

6 1. multiple network point packet capture and analyze packet header distributed environment promiscuous mode packet capture network point : Design packet header information 2. analyze packet header and save into DB and make short term and long term traffic data analyze packet header and save into DB make real-time, short term, long term traffic data 3. query to database from user request and give information to user statistics network traffic data request response promiscuous mode packet capture user network point (real-time, hourly, daily, monthly, yearly) (6/16)

7 Packet Capture Module (Probe) Probe captures packet with promiscuous mode, analyzes packet header and saves into log file No packet drops from system overload using an independent packet capture module. save packet header information into log file Log File log format log format promiscuous mode packet capture network point log format log format Log format : time, length, frame_type, source ip, destination ip, protocol, source port and destination port information (7/16)

8 Ethernet Dst addr IP Log Format bytes data TCP Src addr Type Log format 2 2 TCP data time length frame_type src_ip dst_ip protocol src_port dst_port frame capture time Src addr Dst addr Protocol IP data total Ethernet frame size(ethernet header + data + CRC) src port dst port 1 (bytes) CRC (8/16)

9 Packet Analysis Module (Analyzer) log data Log Transformer IP-based, non IP-based data IP-based, non IP-based data DB Analyzer statistical real-time, hourly, daily, monthly and yearly data. An Analyzer is divided into a Log Transformer module and DB Analyzer module. Log Transformer assorts log files into IP-based data, and non IP-based data (e.g., ARP, RARP, IPX). Log Transformer saves these assorted data to database. DB Analyzer analyzes assorted data in database and makes statistical real-time,hourly, daily, monthly and yearly data. (9/16)

10 Data Translation by Analyzer for Long-Term Traffic hourly daily monthly yearly (10/16)

11 DB Schema Name time length frame_type src_ip dst_ip protocol src_port dst_port Name time length ether_type Type INT SMALLINT SMALLINT INT INT TINYINT SMALLINT SMALLINT Type INT SMALLINT SMALLINT hourly Name hour frame_type frame_name bytes count Name day frame_type frame_name bytes count daily Type TINYINT VARCHAR(20) VARCHAR(100) BIGINT BIGINT Type TINYINT VARCHAR(20) VARCHAR(100) BIGINT BIGINT monthly Name year frame_type frame_name bytes count Name month frame_type frame_name bytes count Type TINYINT VARCHAR(20) VARCHAR(100) BIGINT BIGINT yearly Type TINYINT VARCHAR(20) VARCHAR(100) BIGINT BIGINT This is a *_network_table example. network_table has IP information from raw_ip_table and ARP and so on information from raw_non_ip_table. (11/16)

12 Web Viewer Module Design Web Viewer Database raw_ip, non_ip table data_sent table data_received table data_exchanged table network table transport table application table query reply Database Client CGI Web Server request response (12/16)

13 probe packet capture using libpcap Binary Log File analyzer save packet header information into log file MySQL Client Implementation NFS distributed environment save packet header information into database raw ip, non-ip, table web viewer C-CGI, MySQL Client query make real-time, hourly, daily, monthly and yearly host, protocol and application information table reply C-CGI, Apache web-server MySQL Server http Database raw_ip, non_ip_table data sent table data received table data exchanged table network table transport table application table Internet user (13/16)

14 Web-based User Interface: Main View menu time interval menu analysis time hourly total traffic detailed views (14/16)

15 1 Detailed Views (15/16)

16 Summary & Future Work overcomes many shortcomings of existing monitoring and analysis systems. can analyze real-time and hourly, daily, monthly and yearly network traffic data. With load balancing, independent packet capture prevents packet drops from monitoring system overload. can analyze multiple network points traffic. Future work on More analysis on host and application relationships. Adapt to monitor and analyze other types of IP networks (IPoA, IPoWDM, etc.) Traffic analysis based on contents (video, audio, etc.) (16/16)

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