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A significant challenge in developing automated problem-diagnosis tools for distributed systems is the ability of these tools to differentiate between changes in system behavior due to workload changes from those due to faults. To address this challenge, current, typically white-box, techniques extract semantically-rich knowledge about the target application through fairly invasive, high-overhead instrumentation. The authors propose and explore two scalable, low-overhead, non-invasive techniques to infer semantics about target distributed systems, in a black-box manner, to facilitate problem diagnosis. RAMS applies statistical analysis on hardware performance counters to predict whether a given node in a distributed system is faulty, while BlackSheep corroborates multiple system metrics with application-level logs to determine whether a given node is faulty.
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