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Cloud computing has emerged as a popular computing paradigm allowing workloads to automatically scale in response to changes in demand. Clouds use virtualization to enable elasticity by continually reconfiguring the allocation of physical and virtual resources to workloads. Continual changes in clouds may lead to unexpected performance anomalies and traditional problem determination techniques are unable to deal with such cloud-induced anomalies. In this paper, the authors study problem determination in virtualized clouds. They show that sharing of non-virtualized resources, frequent reconfiguration, higher propensity of faults, and automated steady state management in cloud pose new challenges for problem determination systems.
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