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Infrastructure-as-a-Service (IaaS) clouds are prone to performance anomalies due to their complex nature. Although previous work has shown the effectiveness of using statistical learning to detect performance anomalies, existing schemes often assume labelled training data, which requires significant human effort and can only handle previously known(More)
Virtualized cloud systems are prone to performance anomalies due to various reasons such as resource contentions, software bugs, and hardware failures. In this paper, we present a novel Predictive Performance Anomaly Prevention (PREPARE) system that provides automatic performance anomaly prevention for virtualized cloud computing infrastructures. PREPARE(More)
Distributed applications running inside cloud are prone to performance anomalies due to various reasons such as insufficient resource allocations, unexpected workload increases, or software bugs. However, those applications often consist of multiple interacting components where one component anomaly may cause its dependent components to exhibit anomalous(More)
Distributed applications running inside cloud systems are prone to performance anomalies due to various reasons such as resource contentions, software bugs, and hardware failures. One big challenge for diagnosing an abnormal distributed application is to pinpoint the faulty components. In this paper, we present a black-box online fault localization system(More)
Infrastructure-as-a-service (IaaS) clouds are becoming widely adopted. However, as multiple tenants share the same physical resources, performance anomalies have become one of the top concerns for users. Unfortunately, performance anomaly diagnosis in the production IaaS cloud often takes a long time due to its inherent complexity and sharing nature. In(More)
BACKGROUND With increased use of robotic surgery in specialties including urology, development of training methods has also intensified. However, current approaches lack the ability to discriminate between operational and surgical skills. METHODS An automated recording system was used to longitudinally (monthly) acquire instrument motion/telemetry and(More)
Distributed applications running inside cloud are prone to performance anomalies due to various reasons such as insufficient resource allocations, unexpected workload increases, or software bugs. However, those applications often consist of multiple interacting components where one component anomaly may cause its dependent components to exhibit anomalous(More)
Performance bugs which manifest in a production cloud computing infrastructure are notoriously difficult to diagnose because of both the difficulty of reproducing those bugs and the lack of debugging information. In this paper, we present PerfScope, a practical online performance bug inference tool to help the developer understand how a performance bug(More)
Evolutionary computation algorithms are increasingly being used to solve optimization problems as they have many advantages over traditional optimization algorithms. In this paper we use evolutionary computation to study the trade-off between pleiotropy and redundancy in a client-server based network. Pleiotropy is a term used to describe components that(More)