A Quantum Approach Towards the Adaptive Prediction of Cloud Workloads

Singh, Anurag; Saxena, Deepika; Kumar, Jitendra; Gupta, Vrinda

doi:10.1109/tpds.2021.3079341

Cited by 61 publications

(23 citation statements)

References 37 publications

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“…Results based on NASA have validated the efectiveness of the proposed approach. Singh et al [31] proposed an evolutionary quantum neural network-based approach for cloud workloads prediction, which leverages the computational eiciency of quantum computing to encode workloads, and utilizes the neural network to estimate resource demands. The experiments with traces from cloud data centers and traditional data centers have validated the efectiveness of the proposed approach.…”

Section: Learning-based Approaches For Cloud Workload Predictionmentioning

confidence: 99%

esDNN: Deep Neural Network Based Multivariate Workload Prediction in Cloud Computing Environments

Xu¹,

Song²,

Wu³

et al. 2022

ACM Trans. Internet Technol.

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Cloud computing has been regarded as a successful paradigm for IT industry by providing benefits for both service providers and customers. In spite of the advantages, cloud computing also suffers from distinct challenges, and one of them is the inefficient resource provisioning for dynamic workloads. Accurate workload predictions for cloud computing can support efficient resource provisioning and avoid resource wastage. However, due to the high-dimensional and high-variable features of cloud workloads, it is difficult to predict the workloads effectively and accurately. The current dominant work for cloud workload prediction is based on regression approaches or recurrent neural networks, which fail to capture the long-term variance of workloads. To address the challenges and overcome the limitations of existing works, we proposed an e fficient s upervised learning-based D eep N eural N etwork ( esDNN ) approach for cloud workload prediction. Firstly, we utilize a sliding window to convert the multivariate data into supervised learning time series that allow deep learning for processing. Then we apply a revised Gated Recurrent Unit (GRU) to achieve accurate prediction. To show the effectiveness of esDNN, we also conduct comprehensive experiments based on realistic traces derived from Alibaba and Google cloud data centers. The experimental results demonstrate that esDNN can accurately and efficiently predict cloud workloads. Compared with the state-of-the-art baselines, esDNN can reduce the mean square errors significantly, e.g. 15% than the approach using GRU only. We also apply esDNN for machines auto-scaling, which illustrates that esDNN can reduce the number of active hosts efficiently, thus the costs of service providers can be optimized.

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Section: Learning-based Approaches For Cloud Workload Predictionmentioning

confidence: 99%

esDNN: Deep Neural Network Based Multivariate Workload Prediction in Cloud Computing Environments

Xu¹,

Song²,

Wu³

et al. 2022

ACM Trans. Internet Technol.

View full text Add to dashboard Cite

show abstract

“…Results based on NASA have validated the effectiveness of the proposed approach. Singh et al [31] proposed an evolutionary quantum neural network-based approach for cloud workloads prediction, which leverages the computational efficiency of quantum computing to encode workloads, and utilizes the neural network to estimate resource demands. The experiments with traces from cloud data centers and traditional data centers have validated the effectiveness of the proposed approach.…”

Section: Learning-based Approaches For Cloud Workload Predictionmentioning

confidence: 99%

EsDNN: Deep Neural Network based Multivariate Workload Prediction Approach in Cloud Environment

Xu,

Song,

et al. 2022

Preprint

View full text Add to dashboard Cite

Cloud computing has been regarded as a successful paradigm for IT industry by providing benefits for both service providers and customers. In spite of the advantages, cloud computing also suffers from distinct challenges, and one of them is the inefficient resource provisioning for dynamic workloads. Accurate workload predictions for cloud computing can support efficient resource provisioning and avoid resource wastage. However, due to the high-dimensional and high-variable features of cloud workloads, it is difficult to predict the workloads effectively and accurately. The current dominant work for cloud workload prediction is based on regression approaches or recurrent neural networks, which fail to capture the long-term variance of workloads. To address the challenges and overcome the limitations of existing works, we proposed an efficient supervised learning-based Deep N eural N etwork (esDNN ) approach for cloud workload prediction. Firstly, we utilize a sliding window to convert the multivariate data into supervised learning time series that allow deep learning for processing. Then we apply a revised Gated Recurrent Unit (GRU) to achieve accurate prediction. To show the effectiveness of esDNN, we also conduct comprehensive experiments based on realistic traces derived from Alibaba and Google cloud data centers. The experimental results demonstrate that esDNN can accurately and efficiently predict cloud workloads. Compared with the state-of-the-art baselines, esDNN can reduce the mean square errors significantly, e.g. 15% than the approach using GRU only. We also apply esDNN for machines auto-scaling, which illustrates that esDNN can reduce the number of active hosts efficiently, thus the costs of service providers can be optimized. CCS Concepts: • General Literature; • Distributed Parallel and Cluster Computing; • System and Software; • Management of Cloud Computing Systems;

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“…Machine learning finds its usage across the applications such as prediction, optimization, detection, classification etc. [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55].…”

Section: Fraud Detection Approachesmentioning

confidence: 99%

Credit Card Fraud Detection using Machine Learning: A Study

Tiwari,

Mehta,

Sakhuja

et al. 2021

Preprint

Self Cite

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As the world is rapidly moving towards digitization and money transactions are becoming cashless, the use of credit cards has rapidly increased. The fraud activities associated with it have also been increasing which leads to a huge loss to the financial institutions. Therefore, we need to analyze and detect the fraudulent transaction from the non-fraudulent ones. In this paper, we present a comprehensive review of various methods used to detect credit card frauds. These methodologies include Hidden Markov Model, Decision Trees, Logistic Regression, Support Vector Machines (SVM), Genetic algorithm, Neural Networks, Random Forests, Bayesian Belief Network. A comprehensive analysis of various techniques is presented. We conclude the paper with the pros and cons of the same as stated in the respective papers.

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A Quantum Approach Towards the Adaptive Prediction of Cloud Workloads

Cited by 61 publications

References 37 publications

esDNN: Deep Neural Network Based Multivariate Workload Prediction in Cloud Computing Environments

esDNN: Deep Neural Network Based Multivariate Workload Prediction in Cloud Computing Environments

EsDNN: Deep Neural Network based Multivariate Workload Prediction Approach in Cloud Environment

Credit Card Fraud Detection using Machine Learning: A Study

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