Software Aging Prediction Based on Extreme Learning Machine

Du, Xiaoyong; Lu, Huimin; Liu, Gang

doi:10.11591/telkomnika.v11i11.3495

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…Johansen [7] studied resource utilisation by using state‐of‐the‐art algorithms in a resource‐limited embedded computing platform. Du et al [8] investigated software ageing phenomena in a real VOD system and proposed a resource consumption prediction model based on an extreme learning machine. In the experiment, root mean square error was adopted as an indicator to evaluate the performance among support vector machine, ANN, and the proposed method.…”

Section: Related Workmentioning

confidence: 99%

Variance analysis of software ageing problems

Yan

2018

IET softw.

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Software ageing problems are mainly caused by resource consumption exhaustion, so many researchers focused on predicting software resource consumption. However, the loss analysis using variance has not been done. In this study, the authors propose a framework to analyse variance change in the resource consumption prediction problems. This framework is made up of three steps. First, an original variance decomposition is proposed in view of data sampling and partitioning process. Second, in order to study the influence of data sampling and partitioning process to the variance, the enhanced Friedman test plus Nemenyi post-hoc test is introduced. Lastly, they propose a corrected t-test to analyse the performances of two regression algorithms: auto-regressive integrated moving average and artificial neuron network. In the experiments, they analyse the variance in two levels: operating system level and application level. They find the result that k is equal to ten for k-fold crossvalidation is proper for resource consumption prediction, although the contribution to variance is almost same for the sensitivity of forecasted estimation loss in consideration of data partitioning process and the sensitivity of forecasted estimation loss in consideration of data sampling procedure.

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Section: Related Workmentioning

confidence: 99%

Variance analysis of software ageing problems

Yan

2018

IET softw.

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“…Previous studies also have discussed the predictive measurement approach that can be used for aging detection [9,10]. Xiaozhi Du, et al, proposed software aging prediction model based on extreme learning machine (ELM) for a real VOD system [17].…”

Section: Related Workmentioning

confidence: 99%

“…(We have defined 100 generations as Stopping Criteria) Step- 17 From population, obtain the host having higher candidate fitness value for Placement Step- 18 Allocate VMs to the host using Step-17…”

Section: Genetic Algorithm Based Vm Migrationmentioning

confidence: 99%

Optimum Software Aging Prediction and Rejuvenation Model for Virtualized Environment

Umesh

Srinivasan²

2016

IJEECS

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“…They concluded that Bayesian classifiers work well for this problem [4]. Proposed Online version of Imbalanced SVM (OISVM) for binary email classification to improve processing speed and save storage space [5]. Proved ELM is efficient algorithm for classification problem [6].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Adaptive Data Structure Based Oversampling Algorithm for Ordinal Classification

kasiraja¹,

Vijendran²

2018

IJEECS

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<p>The main objective of this research is to improve the predictive accuracy of classification in ordinal multiclass imbalanced scenario. The methodology attempts to uplift the classifier performance through synthesizing sophisticated objects of immature classes. A novel Adaptive Data Structure based oversampling algorithm is proposed to create synthetic objects and Extreme Learning Machine for Ordinal Regression (ELMOP) classifier is adopted to validate our work. The proposed method generating new objects by analyzing the characteristics and intricacy of immature class objects. On the whole, the data set is divided into training and test data. Training data set is updated with new synthetic objects. The experimental analysis is performed on testing data set to check the efficiency of the proposed methodology by comparing it with the existing work. The performance evaluation is conducted in terms of the parameters called Mean Absolute Error, Maximum Mean Absolute Error, Geometric Mean, Kappa and Average Accuracy. The measures prove that the proposed methodology can produce authentic synthetic objects than the existing techniques. The Proposed technique can synthesize the new effective objects through evaluating the structure of immature class. It boosts the global precision and class wise precision especially preserves rank order of the classes.</p>

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Software Aging Prediction Based on Extreme Learning Machine

Cited by 6 publications

References 18 publications

Variance analysis of software ageing problems

Variance analysis of software ageing problems

Optimum Software Aging Prediction and Rejuvenation Model for Virtualized Environment

Adaptive Data Structure Based Oversampling Algorithm for Ordinal Classification

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