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Friday, 23 February 2018

Efficient and Dynamic Routing Topology Inference From End-to-End Measurements(2009)


Efficient and Dynamic Routing 

Topology Inference From 

End-to-End Measurements(2009)

ABSTRACT:
Inferring the routing topology and link performance from a node to a set of other nodes is an important component in network monitoring and application design. In this paper we propose a general framework for designing topology inference algorithms based on additive metrics. The framework can flexibly fuse information from multiple measurements to achieve better estimation accuracy. We develop computationally efficient (polynomial-time) topology inference algorithms based on the framework. We prove that the probability of correct topology inference of our algorithms converges to one exponentially fast in the number of probing packets. In particular, for applications where nodes may join or leave frequently such as overlay network construction, application-layer multicast, peer-to-peer file sharing/streaming, we propose a novel sequential topology inference algorithm which significantly reduces the probing overhead and can efficiently handle node dynamics. We demonstrate the effectiveness of the proposed inference algorithms via Internet experiments. this tool can help a network operator obtain routing information and network internal characteristics (e.g., loss rate, delay, utilization) from its network to a set of other collaborating networks that are separated by non-participating autonomous networks. In application design, this tool can be particularly useful for peer-to-peer (P2P) style applications where a node communicates with a set of other nodes (called peers) for file sharing and multimedia streaming. a streaming node using multi-path may want to know both the routing topology and link loss rates so the selected paths have low loss correlation. there are two primary approaches to infer the routing topology and link performance in a communication network. 
SYSTEM ANALYSIS
EXISTING SYSTEM
Traceroute
It relies on internal routers responding to traceroute requests and returning ICMP (Internet Control Message Protocol) messages. However, an increasing number of routers in the Internet today will block traceroute requests due to privacy and security concerns. These routers are known as anonymous routers and their existence makes the routing topology inferred by traceroutelike tools inaccurate.
Network tomography
This utilizes end-to-end packet probing measurements (such as packet loss and delay measurements) conducted by the end hosts and does not require extra cooperation from the internal nodes (except the basic packet forwarding functionality). Under a network tomography approach, a source node will send probes to a set of destination nodes. The basic idea is to utilize the correlations among the observed losses and delays of the probes at the destination nodes to infer the network structure and internal characteristics.
Disadvantages

Ø  Use tools based on measurements or feedback messages of the internal nodes (e.g., routers). Such an approach is limited as today’s communication networks are evolving towards more decentralized and private administration.
Ø Unable to handle node dynamics efficiently, it needs fast and scalable inference procedures/algorithms which have low computational complexity, fast convergence rate, and small probing overhead.
PROPOSED SYSTEM
We present a network routing topology inference algorithms based on additive metrics. We show how to construct additive metrics and estimate the (shared) path lengths using end-to-end multicast and unicast packet probing measurements as well as traceroute type measurements.
We propose a sequential topology inference algorithm which significantly reduces the probing overhead under unicast probing. In addition, it can efficiently handle dynamic node joining and leaving, and thus is particularly desirable for applications and networks where node dynamics are prevalent.
Advantages
The framework can flexibly fuse information available from multiple measurements to achieve better estimation accuracy and faster convergence rate.
System Requirements:
Hardware requirements:
Processor                     : Any Processor above 500 MHz.
Ram                              :  128Mb.
Hard Disk                    :  10 Gb.
Compact Disk             :  650 Mb.
Input device                :  Standard Keyboard and Mouse.
Output device             :  VGA and High Resolution Monitor.
Software requirements:
Operating System       : Windows Family.
Language                    : JDK 1.5
Front End                   : Java Swing
MODULES:
FTP Receiver and Response:
FTPServer is running invisible/visible for all accessing computers. FTPServer getting permission to take packets and memory information’s. FTPServer getting these info’s are compressed format.
FTP Client:
FTPClient is running invisible/visible for take an connection from FTPServer running systems using IP. This is taken information about that IP system for compressed format then will be extracted automatically.
Memory Usage Analyses:
To get remote system and local system memory information using JVM. Memory management is included maximum memory, free memory, allocated memory and total free memory.
Packets Analyses:
To getting remote systems and local system packets information using JVM and Jacob. Jacob is taken permission and stored temporary file of information. The packets analyses included bytes level send, receive and total packets. Sending and receiving packets included/calculated unicast, non-unicast, error, unknown and discarded.

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