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Abstract-The Simple Network Management Protocol (SNMP)is widely deployed to monitor, control, and configure network elements. Even though the SNMP technology is well documented and understood, it remains relatively unclear how SNMP is used in practice and what the typical SNMP usage patterns are. This paper discusses how to perform large-scale SNMP traffic measurements in order to develop a better understanding of how SNMP is used in production networks. The tools described in this paper have been applied to networks ranging from large national research networks to relatively small faculty networks. The goal of the research is to provide feedback to SNMP protocol developers within the IETF, researchers working within the context of the IRTF-NMRG, as well as other researchers interested in network management in general. We believe that the results are also valuable for operators and vendors who want to optimize their management interactions or understand the traffic generated by their management software.

Abstract. We propose an approach to monitoring IT systems offline, where system actions are logged in a distributed file system and subsequently checked for compliance against policies formulated in an expressive temporal logic. The novelty of our approach is that monitoring is parallelized so that it scales to large logs. Our technical contributions comprise a formal framework for slicing logs, an algorithmic realization based on MapReduce, and a high-performance implementation. We evaluate our approach analytically and experimentally, proving the soundness and completeness of our slicing techniques and demonstrating its practical feasibility and efficiency on real-world logs with 400 GB of relevant data.

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We have previously presented a monitoring algorithm for compliance checking of policies formalized in an expressive metric first-order temporal logic. We explain here the steps required to go from the original algorithm to a working infrastructure capable of monitoring an existing distributed application producing millions of log entries per day. The main challenge is to correctly and efficiently monitor the trace interleavings obtained by totally ordering actions that happen at the same time. We provide solutions based on formula transformations and monitoring representative traces. We also report, for the first time, on statistics on the performance of our monitor on real-world data, providing evidence of its suitability for nontrivial applications.

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