Integrating software performance curves with the palladio component model

2012 ◽  
Author(s):  
Alexander Wert ◽  
Jens Happe ◽  
Dennis Westermann
Keyword(s):  
Component Model ◽  
Software Performance ◽  
Performance Curves

scholarly journals Cloud Function Performance: a component modeling approach

2021 ◽  
Vol 24 (2) ◽  
Author(s):  
Ignacio Trejos-Zelaya ◽  
Martín Flores-González
Keyword(s):  
Cloud Computing ◽  
Modeling And Simulation ◽  
Performance Model ◽  
Component Model ◽  
Software Performance ◽  
Automatic Scaling ◽  
Performance Engineering ◽  
Software Performance Engineering ◽  
Modeling Approach ◽  
Execution Monitoring

Cloud Functions are a trend in cloud computing in which developers are allowed to install code in a Function-as-a-Service (FaaS) platform able to manage provisioning, execution, monitoring and automatic scaling. The underlying infrastructure in FaaS platforms is hidden from the developers and designers and, since the inuence of the infrastructure is unknown, this makes it di_cult to apply software performance engineering approaches on cloud functions, which could lead to wrong or inaccurate performance estimations. In this study, we explore the use of component-based modeling and simulation in order to generate performance estimations of an exemplar cloud function which was exercised using a variety of workloads. A cloud function was both implemented and instrumented to record performance datain a log _le, associated with its invocations; using the log _le as an input, we extracted a performance model in a Palladio Component Model format suitable for running simulations to validate whether the generated model could explain the runtime behavior of the function. Using this approach and further tunings in the model, we were able to validate that the simulations could explain more than 95% of the function's behavior and that component-based modeling and simulation can be considered a serious option when trying to explain the behavior of a cloud function.

Comparison of Principal Component Solutions in Two Populations

Methodology ◽  
2016 ◽  
Vol 12 (1) ◽  
pp. 11-20 ◽  
Author(s):  
Gregor Sočan
Keyword(s):  
Simulation Study ◽  
Principal Components ◽  
Common Factor ◽  
Principal Component ◽  
Component Model ◽  
Bootstrap Procedure ◽  
Factor Loadings ◽  
Component Loadings ◽  
Principal Component Model ◽  
Two Populations

Abstract. When principal component solutions are compared across two groups, a question arises whether the extracted components have the same interpretation in both populations. The problem can be approached by testing null hypotheses stating that the congruence coefficients between pairs of vectors of component loadings are equal to 1. Chan, Leung, Chan, Ho, and Yung (1999) proposed a bootstrap procedure for testing the hypothesis of perfect congruence between vectors of common factor loadings. We demonstrate that the procedure by Chan et al. is both theoretically and empirically inadequate for the application on principal components. We propose a modification of their procedure, which constructs the resampling space according to the characteristics of the principal component model. The results of a simulation study show satisfactory empirical properties of the modified procedure.

Using Rest's Four Component Model to Predict Applicant Cheating

2011 ◽  
Author(s):  
Lynn R. Hartmann ◽  
Kristie Lynn Campana ◽  
Lance Andrews
Keyword(s):  
Component Model ◽  
Four Component Model

Radiometric Chronology of Neh–nar Glacier, Kashmir

1982 ◽  
Vol 28 (98) ◽  
pp. 91-105 ◽  
Author(s):  
V. N. Nijampurkar ◽  
N. Bhandari ◽  
C. P. Vohra ◽  
V. Krishnan
Keyword(s):  
Average Rate ◽  
Component Model ◽  
Vertical Profiles ◽  
Kashmir Valley ◽  
Movement Rate ◽  
Accumulation Zone ◽  
Core Samples ◽  
Surface Samples ◽  
Two Component

AbstractSurface and core samples of Neh–nar Glacier in the Kashmir Valley have been analysed for the radionuclides 32Si. 210Pb, 40K, and 137Cs. The lateral and vertical profiles (at an altitude of about 4 140 m) reveal:(1)32Si activity decreasing slowly from the accumulation zone to 4 050 m altitude and then abruptly towards the snout.(2)Five zones of alternating high and low 210Pb activity in the surface samples.(3)An horizon at between 2 and 3 m depth containing 210Pb activity above natural levels. This horizon is also associated with 137Cs and a maximum in total ß activity.The ice samples have been dated on the basis of a simplified two–component model, the “fresh“contribution determined by 2l0Pb and the old component by 32Si. The following conclusions can be drawn from these observations:(1)The model age of the snout ice is c. 850 years.(2)The average rate of ice movement in the lower glacier is about 2 m/year, which compares well with the annual movement rate of 2.65 m/year observed since 1974.

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