ProgME: Towards Programmable Network Measurement
ABSTRACT:
We present ProgME, a Programmable MEasurement architecture based on a novel concept of flowset – arbitrary set of flows defined according to application requirements and/or traffic conditions. Through a simple flowset composition language, ProgME can incorporate application requirements, adapt itself to circumvent the challenges on scalability posed by the large number of flows, and achieve a better application-perceived accuracy. ProgME can analyze and adapt to traffic statistics in real-time. Using sequential hypothesis test, ProgME can achieve fast and scalable heavy hitter identification.
Existing System:
Traffic measurements provide critical input for a wide range of network management applications, including traffic engineering, accounting, and security analysis. Existing measurement tools collect traffic statistics based on some predetermined, inflexible concept of “flows”. They do not have sufficient built-in intelligence to understand the application requirements or adapt to the traffic conditions. Consequently, they have limited scalability with respect to the number of flows and the heterogeneity of monitoring applications.
Proposed System:
We present a Programmable MEasurement architecture (ProgME) that can adapt to application requirements and traffic conditions in real time. Below figure shows the major components of ProgME. Our first proposal is to use a versatile definition of flowset – arbitrary set of flows – as the base of traffic statistics collection. In other words, ProgME keeps one counter per flowset. Compared to per-flow traffic statistics, per-flowset statistics enables one to achieve multiple resolutions within a traffic profile. Since flowsets can be defined arbitrarily, they do not necessarily map to the same number of unique flows or traffic volume. Therefore, one can track higher resolution statistics to maintain the desired accuracy for a sub-population of network traffic, while collecting coarse-grained aggregate statistics for the remaining traffic (e.g., through a flowset that catches uninteresting traffic) to reduce total number of counters required. Furthermore, since a flowset can contain arbitrary set of flows, one can construct flowsets that directly reflect the interest of management applications.
Modules:
1. Flow Set
2. Collect and Report Statistics
3. Scalable Flowset-Based Query Answering (FQAE)
4. Accuracy of FQAE
Modules Description:
1. Flow Set
Definition of flowset – arbitrary set of flows – as the base of traffic statistics collection. In other words, ProgME keeps one counter per flowset. Compared to per-flow traffic statistics, per-flowset statistics enables one to achieve multiple resolutions within a traffic profile. Since flowsets can be defined arbitrarily, they do not necessarily map to the same number of unique flows or traffic volume. Therefore, one can track higher resolution statistics to maintain the desired accuracy for a sub-population of network traffic, while collecting coarse-grained aggregate statistics for the remaining traffic (e.g., through a flowset that catches uninteresting traffic) to reduce total number of counters required. Furthermore, since a flowset can contain arbitrary set of flows, one can construct flowsets that directly reflect the interest of management applications.
2. Collect and Report Statistics:
During the measurement process, FQAE performs trafficaware optimization by sorting the order of candidates in the table of matching candidates based on the number of packets observed earlier (TrafficSort). Note that this seemingly simple optimization is possible only because FQAE make flowsets fully disjoint. If flowsets have non-empty intersections, finding the optimal order is NP-complete, and one will have to resolve to heuristics, as some have attempted in the context of packet Filtering .
3. Scalable Flowset-Based Query Answering (FQAE):
We propose a scalable Flowset-based Query Answering Engine (FQAE) to support arbitrary user queries. Used in conjunction with sampling,
FQAE can achieve the same accuracy for any given set of queries compared to an ideal flow-based measurement approach, while achieving orders of magnitude cost reduction in terms of memory requirements.
4. Accuracy of FQAE:
The accuracy of measurement results is of paramount concern for every management application. Existing flow-based measurement tools use average per-flow error as the primary measure of accuracy. If there were no memory limitation and statistics could be maintained for every flow, per-flow statistics could achieve high per-query accuracy under any traffic condition. FQAE achieves the same effect by counting for each query directly.
System Configuration:-
H/W System Configuration:-
Processor - Pentium –III
Speed - 1.1 Ghz
RAM - 256 MB(min)
Hard Disk - 20 GB
Floppy Drive - 1.44 MB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA
S/W System Configuration:-
v Operating System :Windows95/98/2000/XP
v Application Server : Tomcat5.0/6.X
v Front End : HTML, Java, Jsp
v Scripts : JavaScript.
v Server side Script : Java Server Pages.
v Database : Ms-Access
v Database Connectivity : JDBC.
REFERENCE:
Lihua Yuan, Chen-Nee Chuah, and Prasant Mohapatra, “ProgME: Towards Programmable Network Measurement”, IEEE/ACM Transactions on Networking, Vol. 19, No.1, February 2011.
