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Not AvailableProgrammable networks, aside from carrying out their core network functions, can look deep into the data stream and perform application layer processing. But, expect for a few demonstrations, this capability remains largely under explored and under utilized. Currently, scientific computing leverages networks only for communication and not for computation. We propose Computing in Transit to unleash the potential of network computing for scientific workflows. Specifically, we investigate computing in transit in the context of light source experiments. Researchers using light sources are interested in rare events and we intend to leverage computing in transit to solve this problem. As the compute and memory resources available within the network are scarce, we must use these resources prudently without sacrificing on performance metrics. Computing within the network can support significantly higher throughput at low latency but it may be less accurate as there are limitations to how deep a network can inspect the payload. We propose a neutralized checksum that takes in TCP checksum as an input to avoid processing the entire payload. We evaluate this approach to identify rare events by introducing random perturbations to reference frames. We measure the effectiveness of neutralized checksum to identify changes. We see that neutralized checksum identifies all changes and is a very promising approach to rare event detection. 

Starting with a random initial seed, fuzzers search for inputs that trigger bugs or vulnerabilities. However, fuzzers often fail to generate inputs for program paths guarded by restrictive branch conditions. In this paper, we show that by first identifying rare-paths in programs (i.e., program paths with path constraints that are unlikely to be satisfied by random input generation), and then, generating inputs/seeds that trigger rare-paths, one can improve the coverage of fuzzing tools. In particular, we present techniques 1) that identify rare paths using quantitative symbolic analysis, and 2) generate inputs that can explore these rare paths using path-guided concolic execution. We provide these inputs as initial seed sets to three state of the art fuzzers. Our experimental evaluation on a set of programs shows that the fuzzers achieve better coverage with the rare-path based seed set compared to a random initial seed.