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SWIM is a peer-to-peer group membership protocol with attractive scaling androbustness properties. However, slow message processing can cause SWIM to markhealthy members as failed (so called false positive failure detection), despiteinclusion of a mechanism to avoid this.  We identify the properties of SWIM that lead to the problem, and proposeLifeguard, a set of extensions to SWIM which consider that the local failuredetector module may be at fault, via the concept of local health. We evaluatethis approach in a precisely controlled environment and validate it in areal-world scenario, showing that it drastically reduces the rate of falsepositives. The false positive rate and detection time for true failures can bereduced simultaneously, compared to the baseline levels of SWIM.

Lifeguard: Local Health Awareness for More Accurate Failure Detection