With the rapid development of IPv6 network, constructing a router-level topology of IPv6 is helpful for network management, network simulation, protocol design, etc. As same as IPv4, the well-known IPv6 topology discovery tool, traceroute6, also brings an important problem named alias resolution. In this paper, we present one novel heuristic method, called Route Positional Method (RPM), for IPv6 alias resolution. Given some interface addresses, this method introduces minimum traffic into the network and can effectively group these addresses into routers. Our experimental evaluation on the IPv6 network shows that, compared to the address-based method, our method is also accurate and can increase the success rate of finding aliases by nearly half. In addition, both methods can be used together to improve the completeness of alias resolution. And we believe that our method can be more effective in practice by using multiple vantage points.

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Recently, the characteristics of traceroute probe have been widely studied in the field of Internet topology discovery, which helps researchers to understand the role of Traceroute tool better, such as sampling bias, marginal utility and so on. In this paper, we study three raw datasets of traceroute probe of different platforms and observe an interesting phenomenon: in the process of one probe, every two of three parameters (i.e. the number of discovered nodes, the number of discovered links and the number of vantage points) have a very strong powerlaw relationship without considering the distribution of sources and destinations. We call this Trace Power Law and validate its existence in both IP and AS level. As far as we know, it is the first time to discover this characteristic, which, we believe, should be of value for better understanding and use of traceroute.The main contribution of our work lies in two aspects: first, we find a new feature of traceroute sampling and validate it in real datasets and simulated experiments; second, based on what we find, an application is given to predict the numbers of nodes and links detected in one probe.