2017
DOI: 10.1002/spe.2500
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PHOEBE: an automation framework for the effective usage of diagnosis tools in the performance testing of clustered systems

Abstract: The identification of performance issues and the diagnosis of their root causes are time-consuming and complex tasks, especially in clustered environments. To simplify these tasks, researchers have been developing tools with built-in expertise for practitioners. However, various limitations exist in these tools that prevent their efficient usage in the performance testing of clusters (e.g. the need of manually analysing huge volumes of distributed results). In a previous work, we introduced a policy-based adap… Show more

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Cited by 9 publications
(11 citation statements)
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“…Performance testing is an important type of testing which aims to assess whether or not an application (i.e., AUT) will be able to perform its business functionality under a given workload [13,19]. As shown in Fig.…”
Section: Background and Related Workmentioning
confidence: 99%
See 3 more Smart Citations
“…Performance testing is an important type of testing which aims to assess whether or not an application (i.e., AUT) will be able to perform its business functionality under a given workload [13,19]. As shown in Fig.…”
Section: Background and Related Workmentioning
confidence: 99%
“…(3) Error rate threshold: This optional parameter defines the upper bound value that indicates that the system has become saturated. If this parameter is not configured, a default value of 90% will be used (as this is a value commonly identified as saturation point [19]). (4) Functional dependencies: This is the set of dependency relationships that might exist between the tested transactions (e.g., a purchase cannot occur without first logging into the system).…”
Section: Diagnosismentioning
confidence: 99%
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“…Each of the neural network algorithms (LSTM, GRU and SimpleRNN) are trained for 100 epochs before making predictions, and all use the same seeded random values throughout the tests. The data is normalized to small values (between 0 and 10) and an error threshold of 1 (i.e., 10% of the range) is used to predict anomalies (as this threshold is typically used to delimit the behaviour of a steadystate process [15]). For instance, if the predicted value is 0.5 and the actual value is -0.3, it is not considered an anomaly.…”
Section: Comparative Analysismentioning
confidence: 99%