BHyPreC: A Novel Bi-LSTM Based Hybrid Recurrent Neural Network Model to Predict the CPU Workload of Cloud Virtual Machine

Karim, Md. Ebtidaul; Maswood, Mirza Mohd Shahriar; Das, Sunanda; Alharbi, Abdullah G.

doi:10.1109/access.2021.3113714

Cited by 63 publications

(23 citation statements)

References 45 publications

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“…[14][15][16]. In addition, the existing literature notably focuses on models such as autoregressive integrated moving average (ARIMA) and seasonal ARIMA (SARIMA), which are classical estimation methods, and gated recurrent unit (GRU), recurrent neural network (RNN), long-short-term memory networks (LSTM), which are popular deep learning models of recent times [17][18][19]. Furthermore, hybrid studies on companies with gradually developing artificial intelligence technologies will enhance their prediction performance.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Kilinç¹,

Yurtsever

2022

Sustainability

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The effects of developing technology and rapid population growth on the environment have been expanding gradually. Particularly, the growth in water consumption has revealed the necessity of water management. In this sense, accurate flow estimation is important to water management. Therefore, in this study, a grey wolf algorithm (GWO)-based gated recurrent unit (GRU) hybrid model is proposed for streamflow forecasting. In the study, daily flow data of Üçtepe and Tuzla flow observation stations located in various water collection areas of the Seyhan basin were utilized. In the test and training analysis of the models, the first 75% of the data were used for training, and the remaining 25% for testing. The accuracy and success of the hybrid model were compared via the comparison model and linear regression, one of the most basic models of artificial neural networks. The estimation results of the models were analyzed using different statistical indexes. Better results were obtained for the GWO-GRU hybrid model compared to the benchmark models in all statistical metrics except SD at the Üçtepe station and the whole Tuzla station. At Üçtepe, the FMS, despite the RMSE and MAE of the hybrid model being 82.93 and 85.93 m3/s, was 124.57 m3/s, and it was 184.06 m3/s in the single GRU model. We achieved around 34% and 53% improvements, respectively. Additionally, the R2 values for Tuzla FMS were 0.9827 and 0.9558 from GWO-GRU and linear regression, respectively. It was observed that the hybrid GWO-GRU model could be used successfully in forecasting studies.

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Section: Introductionmentioning

confidence: 99%

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Kilinç¹,

Yurtsever

2022

Sustainability

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“…Table 3 shows an overview of adaptive threshold-based host overload detection techniques. A prediction model based on hybrid recurrent neural network has also been suggested by Karim et al [ 35 ].…”

Section: State Of Art For Energy-efficient Vm Consolidationmentioning

confidence: 99%

Adaptive Computational Solutions to Energy Efficiency in Cloud Computing Environment Using VM Consolidation

Magotra

Malhotra

Dogra

2022

Arch Computat Methods Eng

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Cloud Computing has emerged as a computing paradigm where services are provided through the internet in recent years. Offering on-demand services has transformed the IT companies' working environment, leading to a linearly increasing trend of its usage. The provisioning of the Computing infrastructure is achieved with the help of virtual machines. A great figure of physical devices is required to satisfy the users' resource requirements. To meet the requirements of the submitted workloads that are usually dynamic, the cloud data centers cause the over-provisioning of cloud resources. The result of this over-provisioning is the resource wastage with an increase in the levels of energy consumption, causing a raised operational cost. High CO 2 emissions result from this huge energy consumption by data centers, posing a threat to environmental stability. The environmental concern demands for the controlled energy consumption, which can be attained by optimal usage of resources to achieve in the server load, by minimizing the number of active nodes, and by minimizing the frequency of switching between active and de-active server mode in the data center. Motivated by these actualities, we discuss numerous statistical, deterministic, probabilistic, machine learning and optimization based computational solutions for the cloud computing environment. A comparative analysis of the computational methods, on the basis of architecture, consolidation step involved, objectives achieved, simulators involved and resources utilized, has also been presented. A taxonomy for virtual machine (VM) consolidation has also been derived in this research article followed by emerging challenges and research gaps in the field of VM consolidation in cloud computing environment.

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“…The performance is measured as predicted output is achieved for cloud resource management. 2D LSTM [30] Bi-LSTM [31] Crystal ILP [32] FEMT-LSTM [33] Encoder+ LSTM [34] CP Autoen -coder [35] LPAW Autoe -ncoder [36] GRUED [37] DBN+R BN [38] DBN+O ED [39] DP-CU PA [40] es-DNN [41] DNN+ MVM [42] DNN-PPE [43] SG-LSTM [44] ADRL [45] Bi-Hyp -rec [46] BG-LSTM [47] HPF-DNN [48] FAHP [28] ACPS [49] LSRU [50] KSE+WMC [51] FAST [52] SGW-S [19] ClIn [53] AMS [54] E-ELM [55] SF-Cluster [48] EQNN [56] Fig. 3: Classification and Taxonomy of Machine learning based Workload Prediction Models…”

Section: Workload Prediction Operational Flowmentioning

confidence: 99%

Performance Analysis of Machine Learning Centered Workload Prediction Models for Cloud

Saxena,

Kumar,

Singh

et al. 2023

Preprint

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The precise estimation of resource usage is a complex and challenging issue due to the high variability and dimensionality of heterogeneous service types and dynamic workloads. Over the last few years, the prediction of resource usage and traffic has received ample attention from the research community. Many machine learning-based workload forecasting models have been developed by exploiting their computational power and learning capabilities. This paper presents the first systematic survey cum performance analysis-based comparative study of diversified machine learning-driven cloud workload prediction models. The discussion initiates with the significance of predictive resource management followed by a schematic description, operational design, motivation, and challenges concerning these workload prediction models. Classification and taxonomy of different prediction approaches into five distinct categories are presented focusing on the theoretical concepts and mathematical functioning of the existing state-of-the-art workload prediction methods. The most prominent prediction approaches belonging to a distinct class of machine learning models are thoroughly surveyed and compared. All five classified machine learning-based workload prediction models are implemented on a common platform for systematic investigation and comparison using three distinct benchmark cloud workload traces via experimental analysis. The essential key performance indicators of state-of-the-art approaches are evaluated for comparison and the paper is concluded by discussing the trade-offs and notable remarks.

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BHyPreC: A Novel Bi-LSTM Based Hybrid Recurrent Neural Network Model to Predict the CPU Workload of Cloud Virtual Machine

Cited by 63 publications

References 45 publications

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Adaptive Computational Solutions to Energy Efficiency in Cloud Computing Environment Using VM Consolidation

Performance Analysis of Machine Learning Centered Workload Prediction Models for Cloud

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