Software releases

Software to Generate IP Hitlists with Hadoop Now Available

We are happy to release the set of map/reduce processing scripts that run in Hadoop to consume our Internet address censuses and output hitlists, as described in the paper “Selecting Representative IP Addresses for Internet Topology Studies“.

These scripts depend on our internal Hadoop configuration and so will require some modification to work elsewhere, but we make them available and encourage feedback about their use.

Papers Publications

New conference paper “Selecting Representative IP Addresses for Internet Topology Studies” to appear at IMC

The paper “Selecting Representative IP Addresses for Internet Topology Studies” (available at was accepted to appear at the ACM Internet Measurement Conference 2010 in Melbourne, Australia.

From the abstract:

An Internet hitlist is a set of addresses that cover and can represent the the Internet as a whole. Hitlists have long been used in studies of Internet topology, reachability, and performance, serving as the destinations of traceroute or performance probes. Most early topology studies used manually generated lists of prominent addresses, but evolution and growth of the Internet make human maintenance untenable. Random selection scales to today’s address space, but most andom addresses fail to respond. In this paper we present what we believe is the first automatic generation of hitlists informed censuses of Internet addresses. We formalize the desirable characteristics of a hitlist: reachability, each representative responds to pings; completeness, they cover all the allocated IPv4 address space; and stability, list evolution is minimized when possible. We quantify the accuracy of our automatic hitlists, showing that only one-third of the Internet allows informed selection of representatives. Of informed representatives, 50–60% are likely to respond three months later, and we show that causes for non-responses are likely due to dynamic addressing (so no stable representative exists) or firewalls. In spite of these limitations, we show that the use of informed hitlists can add 1.7 million edge links (a 5% growth) to traceroute-based Internet topology studies. Our hitlists are available free-of-charge and are in use by several other research projects.

Citation: Xun Fan and John Heidemann. Selecting Representative IP Addresses for Internet Topology Studies. To appear in Proceedings of the ACM Internet Measurement Conference (IMC). Melbourne, Australia, ACM. November, 2010.

Announcements Collaborations Software releases

ANT extensions for bzip2-splitting to appear in Hadoop

The ANT project is happy to announce that our extensions to Hadoop to support splitting of bzip2-compressed files have been accepted to appear in the next Hadoop release (will be 0.21.0).

Support for compression is important in map/reduce because it reduces the amount of I/O, and because important input files (for us, our Internet address censuses) are provided in compressed format.

Splitting is important in map/reduce, because splitting allows many computers to process parts of a few big files.  Since the whole point of Hadoop and map/reduce is processing big files (for us, 4GB or more) with many computers (for us, dozens to hundreds), splitting is really essential.

Until now, Hadoop did not support splitting of compressed files.  Instead, if input data was compressed, you get at most one computer per file.  Some work-arounds were possible, but basically unpleasant, and often requiring that one rewrite all the input data is some other format.

Our extensions (see HADOOP-4012 and MAPREDUCE-830, plus HADOOP-3646 that went into 0.19.0) support Hadoop execution over bzip2 files with automatic splitting.  Getting this done was trickier than one might expect:  Hadoop really wants to decide where to split files, yet bzip2 can only support splits at specific locations that are different, and users don’t care about either of these but instead only about their record boundaries.  Fortunately, we were able to align all of these constraints, and deal with the corner cases that inevitably arise.  (What if the bzip2 marker appears in normal data?  What happens when markers exactly align, or are off-by-one?)

Abdul Qadeer did this work in 2008, working with Yuri Pradkin and me (John Heidemann), and continued to work with the patch through its getting committed.  We especially thank Chris Douglas at Yahoo for shepherding patch through the Hadoop bug tracking system, including helping clean it up and add test cases.  And we thank Doug Cutting for initially suggesting bzip2 as a splittable compression scheme.

This work was supported by NSF through the MR-Net research project (CNS-0823774).