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Misconfiguration is a major cause of system failures. Prior solutions focus on detecting invalid settings that are introduced by user mistakes. But another type of misconfiguration that continues to haunt production services is specious configuration---settings that are valid but lead to unexpectedly poor performance in production. Such misconfigurations are subtle, so even careful administrators may fail to foresee them. We propose a tool called Violet to detect specious configuration. We realize the crux of specious configuration is that it causes some slow code path to be executed, but the bad performance effect cannot always be triggered. Violet thus takes a novel approach that uses selective symbolic execution to systematically reason about the performance effect of configuration parameters, their combination effect, and the relationship with input. Violet outputs a performance impact model for the automatic detection of poor configuration settings. We applied Violet on four large systems. To evaluate the effectiveness of Violet, we collect 17 real-world specious configuration cases. Violet detects 15 of them. Violet also identifies 11 unknown specious configurations. 

Partial failures occur frequently in cloud systems and can cause serious damage including inconsistency and data loss. Unfortunately, these failures are not well understood. Nor can they be effectively detected. In this paper, we first study 100 real-world partial failures from five mature systems to understand their characteristics. We find that these failures are caused by a variety of defects that require the unique conditions of the production environment to be triggered. Manually writing effective detectors to systematically detect such failures is both time-consuming and error-prone. We thus propose OmegaGen, a static analysis tool that automatically generates customized watchdogs for a given program by using a novel program reduction technique. We have successfully applied OmegaGen to six large distributed systems. In evaluating 22 real-world partial failure cases in these systems, the generated watchdogs can detect 20 cases with a median detection time of 4.2 seconds, and pinpoint the failure scope for 18 cases. The generated watchdogs also expose an unknown, confirmed partial failure bug in the latest version of ZooKeeper. 

Systems software today is composed of numerous modules and exhibits complex failure modes. Existing failure detectors focus on catching simple, complete failures and treat programs uniformly at the process level. In this paper, we argue that modern software needs intrinsic failure detectors that are tailored to individual systems and can detect anomalies within a process at finer granularity. We particularly advocate a notion of intrinsic software watchdogs and propose an abstraction for it. Among the different styles of watchdogs, we believe watchdogs that imitate the main program can provide the best combination of completeness, accuracy and localization for detecting gray failures. But, manually constructing such mimic-type watchdogs is challenging and time-consuming. To close this gap, we present an early exploration for automatically generating mimic-type watchdogs