Large-Scale Computing Systems Workload Prediction Using Parallel Improved LSTM Neural Network

Tang, Xiaoyong

doi:10.1109/access.2019.2905634

Cited by 33 publications

(10 citation statements)

References 27 publications

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“…This model fails to focus on choosing the precise destination based on optimal resource rich server. In [35], a prediction technique is developed that examines the dependency in a large-scale system and builds two separate time series models depending on day and time. Using two-dimensional time series information, an improved LSTM model is suggested for forecasting future workload.…”

Section: Deep Learning Techniquesmentioning

confidence: 99%

Load-Aware VM Migration Using Hypergraph Based CDB-LSTM

Subramanian¹,

Sriram²

2023

Intelligent Automation &Amp; Soft Computing

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Live Virtual Machine (VM) migration is one of the foremost techniques for progressing Cloud Data Centers' (CDC) proficiency as it leads to better resource usage. The workload of CDC is often dynamic in nature, it is better to envisage the upcoming workload for early detection of overload status, underload status and to trigger the migration at an appropriate point wherein enough number of resources are available. Though various statistical and machine learning approaches are widely applied for resource usage prediction, they often failed to handle the increase of non-linear CDC data. To overcome this issue, a novel Hypergrah based Convolutional Deep Bi-Directional-Long Short Term Memory (CDB-LSTM) model is proposed. The CDB-LSTM adopts Helly property of Hypergraph and Savitzky-Golay (SG) filter to select informative samples and exclude noisy inference & outliers. The proposed approach optimizes resource usage prediction and reduces the number of migrations with minimal computational complexity during live VM migration. Further, the proposed prediction approach implements the correlation co-efficient measure to select the appropriate destination server for VM migration. A Hypergraph based CDB-LSTM was validated using Google cluster dataset and compared with state-of-the-art approaches in terms of various evaluation metrics.

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Section: Deep Learning Techniquesmentioning

confidence: 99%

Load-Aware VM Migration Using Hypergraph Based CDB-LSTM

Subramanian¹,

Sriram²

2023

Intelligent Automation &Amp; Soft Computing

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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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“…Recurrent Neural Network (RNN) is an ideal network to implement the inference module of our prediction model. RNNs (including mutations such as Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU)) are usually applied as end-to-end models (e.g., [26] [27]). However, a major limitation of them is the difficulty in learning complex seasonal patterns in multi-seasonal time series.…”

Section: A Prediction Modelmentioning

confidence: 99%