#NETWORK TOPOLOGY MAPPER ONLINE FREE#
Subsequent research confirmed that the difference between scale-free and random networks are too significant to be ignored: protocols designed for random networks fare poorly on a scale-free topology ¶ a scale-free Internet displays high tolerance to random node failures but is fragile against attacks ( 9–11) computer viruses spread threshold free on scale-free networks ( 12) with obvious consequences on network security. ( 7) that the Internet is a scale-free network with a power-law degree distribution ( 8) invalidated all previous modeling efforts.
#NETWORK TOPOLOGY MAPPER ONLINE SOFTWARE#
Indeed, until recently all Internet topology generators ( 3, 4), ∥ which are software designed to generate realistic network topologies with several input parameters for research and development purposes, provided versions of random graphs ( 5, 6). Our ability to design good topology generators is limited by our poor understanding of the basic mechanisms that shape the Internet's large-scale topology. § Thus to efficiently control and route traffic on an exponentially expanding Internet ( 2), it is important that topology generators not only capture the structure of the current Internet, but allow for efficient planning and long-term network design as well. ¶ Protocols that work seamlessly on prototypes fail to scale up, being inefficient on the larger real network. Indeed, security and communication protocols perform poorly on topologies provided by generators different from which they are optimized for, and are often ineffective when released. In light of extensive evidence that Internet protocol performance is greatly influenced by the network topology ( 1), §¶ network generators are a crucial prerequisite for understanding and modeling the Internet. The universal parameters that we extract significantly restrict the class of potentially correct Internet models and indicate that the networks created by all available topology generators are fundamentally different from the current Internet. The placement of links is driven by competition between preferential attachment and linear distance dependence, a marked departure from the currently used exponential laws. We find that the physical layout of nodes form a fractal set, determined by population density patterns around the globe. By combining several independent databases capturing the time evolution, topology, and physical layout of the Internet, we identify the universal mechanisms that shape the Internet's router and autonomous system level topology. Our ability to design realistic generators is limited by the incomplete understanding of the fundamental driving forces that affect the Internet's evolution. Network generators that capture the Internet's large-scale topology are crucial for the development of efficient routing protocols and modeling Internet traffic.
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