NDLI: Predicting Network Futures with Plankton

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Predicting Network Futures with Plankton

Content Provider	ACM Digital Library
Author	Prabhu, Santhosh Caesar, Matthew Godfrey, Brighten Kheradmand, Ali
Abstract	Recent years have seen significant advancement in the field of formal network verification. Tools have been proposed for offline data plane verification, real-time data plane verification and configuration verification under arbitrary, but static sets of failures. However, due to the fundamental limitation of not treating the network as an evolving system, current verification platforms have significant constraints in terms of scope. In real-world networks, correctness policies may be violated only through a particular combination of environment events and protocol actions, possibly in a non-deterministic sequence. Moreover, correctness specifications themselves may often correlate multiple data plane states, particularly when dynamic data plane elements are present. Tools in existence today are not capable of reasoning about all the possible network events, and all the subsequent execution paths that are enabled by those events. We propose Plankton, a verification platform for identifying undesirable evolutions of networks. By combining symbolic modeling of data plane and control plane with explicit state exploration, Plankton performs a goal-directed search on a finite-state transition system that captures the behavior of the network as well as the various events that can influence it. In this way, Plankton can automatically find policy violations that can occur due to a sequence of network events, starting from the current state. Initial experiments have successfully predicted scenarios like BGP Wedgies.
Starting Page	92
Ending Page	98
Page Count	7
File Format	PDF
ISBN	9781450352444
DOI	10.1145/3106989.3106991
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Network troubleshooting Correctness
Content Type	Text
Resource Type	Article

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