A self‐learning fuzzy approach for proactive resource provisioning in cloud environment

Khorsand, Reihaneh; Ghobaei‐Arani, Mostafa; Ramezanpour, Mohammadreza

doi:10.1002/spe.2737

Cited by 46 publications

(16 citation statements)

References 35 publications

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“…Some researchers are working to scale the multi-player online game applications resources hosted on the cloud. For example, Khorsand et al [17] proposed a self-learning fuzzy approach for the proactive provision of resources for multi-player online game applications in a cloud environment. The authors applied Maximum Likelihood Estimation and Local Linear Regression for parameter prediction and fuzzy decision-maker to determine appropriate autoscaling decisions.…”

Section: Related Workmentioning

confidence: 99%

Online Workload Burst Detection for Efficient Predictive Autoscaling of Applications

et al. 2020

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Autoscaling methods are employed to ensure the scalability of cloud-hosted applications. The public-facing applications are prone to receive sudden workload bursts, and the existing autoscaling methods do not handle the bursty workloads gracefully. It is challenging to detect the burst online from the incoming dynamic workload traffic, and then identifying appropriate resources to address the burst without overprovisioning is even harder. In this paper, we address this challenge by investigating the appropriate method for online burst detection and then proposed a novel predictive autoscaling method to use burst detection for satisfying specific response time requirements. We compared the proposed method with multiple state-of-the-art baseline autoscaling methods under multiple realistic and synthetic bursty workloads for a benchmark application. Our experimental results show a 60.8% average decrease in response time violations as compared to the baseline method. INDEX TERMS Autoscaling, predictive, SLO violations, response time, workload, burstiness, online burst detection.

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Section: Related Workmentioning

confidence: 99%

Online Workload Burst Detection for Efficient Predictive Autoscaling of Applications

et al. 2020

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“…Multiobjective evolutionary algorithms may not find the exact outcome but in many cases, we need to find an optimal solution, which serves our purpose. For a given multiobjective problem, Figure 1 Multi-objective evolutionary algorithms are the widely researched area know a day and the research presented in [12] is based on a self-learning fuzzy method that is aimed at dynamic resource allocation in a cloud environment for Multiplayer Online Games (MMOG). The development of an improved poison distribution attempts to predict future requests from a historical repository.…”

Section: B Multi-objective Evolutionary Algorithmsmentioning

confidence: 99%

Disease Classification in Health Care Systems With Game Theory Approach

Raja

Asghar

2020

IEEE Access

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There are numerous cases in real life when we come across problems involving the optimization of multiple objectives simultaneously. One of the complexities of solving such problems is that often one or more objectives are usually conflicting under given conditions. In this study, the benefits of relying on a deployed Clinical Decision Support System (CDSS) concerning the overall reputation of a health facility has been studied. The analysis is performed in terms of a cooperative Bayesian game-theoretic model. The game is played between two players of which the first player is a patient who needs quick and accurate medical attention and the second player is the hospital administration that relies on medical experts as well as integrated multi-objective clinical data classification systems for decision-making. The proposed model ''MEAF''-Multi-objective Evolutionary Algorithm using Fuzzy Genetics attempts to address accuracy and interpretability simultaneously using Evolutionary Algorithms (EAs). This model enables a H CDSS to detect a disease accurately by using the available resources efficiently. The results of our simulation show that H CDSS produces better and accurate results in detecting disease with efficient resource utilization along with the reduced computational cost. This approach has also produced a better response for both players based on Bayesian Nash Equilibrium. Finally, the proposed model has been tested for accuracy, efficient resource utilization, and computationally cost-effective solution.

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“…The cancer data analysis can be achieved by data mining and machine learning techniques [4][5][6]. The main purpose of data mining and machine learning techniques is to create a model with capable of classifying and predicting the outcome of unknown input data according to previous observations [4].…”

Section: Introductionmentioning

confidence: 99%

“…In general, classification and prediction problems have a fundamental role in medical decision making. In this paper, an intelligent ensemble classification [5] method based on multi-layer perceptron neural network (IEC-MLP) is proposed for breast cancer diagnosis. The principal restriction of MLP-NN is related to its parameter settings.…”

Section: Introductionmentioning

confidence: 99%

Design of Ensemble Classifier Model Based on MLP Neural Network For Breast Cancer Diagnosis

Rezaeipanah

Syah

Wulandari

et al. 2021

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Nowadays, breast cancer is one of the leading causes of death women in the worldwide. If breast cancer is detected at the beginning stage, it can ensure long-term survival. Numerous methods have been proposed for the early prediction of this cancer, however, efforts are still ongoing given the importance of the problem. Artificial Neural Networks (ANN) have been established as some of the most dominant machine learning algorithms, where they are very popular for prediction and classification work. In this paper, an Intelligent Ensemble Classification method based on Multi-Layer Perceptron neural network (IEC-MLP) is proposed for breast cancer diagnosis. The proposed method is split into two stages, parameters optimization and ensemble classification. In the first stage, the MLP Neural Network (MLP-NN) parameters, including optimal features, hidden layers, hidden nodes and weights, are optimized with an Evolutionary Algorithm (EA) for maximize the classification accuracy. In the second stage, an ensemble classification algorithm of MLP-NN is applied to classify the patient with optimized parameters. Our proposed IEC-MLP method which can not only help to reduce the complexity of MLP-NN and effectively selection the optimal feature subset, but it can also obtain the minimum misclassification cost. The classification results were evaluated using the IEC-MLP for different breast cancer datasets and the prediction results obtained were very promising (98.74% accuracy on the WBCD dataset). Meanwhile, the proposed method outperforms the GAANN and CAFS algorithms and other state-of-the-art classifiers. In addition, IEC-MLP could also be applied to other cancer diagnosis.

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A self‐learning fuzzy approach for proactive resource provisioning in cloud environment

Cited by 46 publications

References 35 publications

Online Workload Burst Detection for Efficient Predictive Autoscaling of Applications

Online Workload Burst Detection for Efficient Predictive Autoscaling of Applications

Disease Classification in Health Care Systems With Game Theory Approach

Design of Ensemble Classifier Model Based on MLP Neural Network For Breast Cancer Diagnosis

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