  We consider the problem of separating error messages generated in large
distributed data center networks into error events. In such networks, each
error event leads to a stream of messages generated by hardware and software
components affected by the event. These messages are stored in a giant message
log. We consider the unsupervised learning problem of identifying the
signatures of events that generated these messages; here, the signature of an
error event refers to the mixture of messages generated by the event. One of
the main contributions of the paper is a novel mapping of our problem which
transforms it into a problem of topic discovery in documents. Events in our
problem correspond to topics and messages in our problem correspond to words in
the topic discovery problem. However, there is no direct analog of documents.
Therefore, we use a non-parametric change-point detection algorithm, which has
linear computational complexity in the number of messages, to divide the
message log into smaller subsets called episodes, which serve as the
equivalents of documents. After this mapping has been done, we use a well-known
algorithm for topic discovery, called LDA, to solve our problem. We
theoretically analyze the change-point detection algorithm, and show that it is
consistent and has low sample complexity. We also demonstrate the scalability
of our algorithm on a real data set consisting of $97$ million messages
collected over a period of $15$ days, from a distributed data center network
which supports the operations of a large wireless service provider.
