Network Topology refers to layout of a network and how different nodes in a network are connected to each other and how they communicate. Topologies are either physical (the physical layout of devices on a network) or logical (the way that the signals act on the network media, or the way that the data passes through the network from one device to the next). The knowledge of network topology is to improve the quality of service (QoS) offered to users and provide a high availability of the shared data, it is common to store the data in replicated servers distributed across the internet. The replication of data over different machines makes the choice of its location a challenging problem that can be addressed with knowledge of the internet topology. In this paper, introduced a novel framework to improve the efficiency of the network topology discovery. To improve the efficiency the parameters such as communication cost should be reduced, coverage should be increased, and probing redundancy should be reduced. The efficiency is improved by introducing a clustering technique in the network topology discovery. First, the topology data collected can form the basis for a formal graph of the internet. Depending on the level considered, a vertex in the graph can be an IP interface, a router a PoP or an AS. Once the graph is built, one can study its characteristics, such as the average degree, the degree distribution and the clustering coefficient. Then the network topology information is useful in deciding where to add new routers and to figure out whether current hardware is correctly configured. It also allows network managers to find bottlenecks and failures in the network. Key Terms: Network Topology, Clustering Technique, Routing I. INTRODUCTIONNetwork topology is a representation of the interconnection between directly connected peers in a network. In a physical network topology, peers are ports on devices connected by a physical transmission link. A physical topology corresponds to many logical topologies, each at a different level of abstraction. For example, at the IP level, peers are hosts or routers one IP hop from each other, and at the workgroup level, the peers are workgroups connected by a logical link. Network topology refers exclusively to the logical IP topology, ignoring hubs and bridges, and link-level details such as FDDI token rotation times, ATM or Frame-relay links, and Ethernet segment lengths. At this level, a peer corresponds to one or more IP addresses, and a link corresponds to a channel with specific delay, capacity, and loss characteristics. Network topology constantly changes as nodes and links join a network, personnel move offices, and network capacity is increased to deal with added traffic. Keeping track of network topology manually is a difficult task and often impossible job. Yet, accurate topology information is necessary for the following reasons:Simulation: In order to simulate real networks, the topology of the network must be first obtained. Network Management: Network ...

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-Maximum spatial-frequency diversity can be achieve by combining bit-interleaved coded modulation (BICM), orthogonal space-time block coding (OSTBC) and orthogonal frequency division multiplexing (OFDM) in frequency selective multi-path fading channels, provided that perfect channel state information (CSI) is available to the receiver. In view of the fact that perfect CSI can be obtained only if a sufficient amount of resource is allocated for training or pilot data, this paper investigates DF relay based technique for the distributed BICM-OSTBC-OFDM system. Our focus is mainly on noncoherent diversity analysis. We study a class of carefully designed transmission schemes, called perfect channel identifiability (PCI) achieving schemes, and show that they can exhibit good diversity performance. Specifically, we present a worst-case diversity analysis framework to show that PCI-achieving schemes can achieve the maximum noncoherent spatial-frequency diversity of distributed BICM-OSTBC-OFDM. Simulation results are presented to confirm our theoretical claims and show that the proposed noncoherent schemes can exhibit near-coherent performance.