Proactive Scheduling in Cloud Computing

Kaur, Ripandeep; Kaur, Gurjot

doi:10.11591/eei.v6i2.649

Cited by 8 publications

(7 citation statements)

References 11 publications

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“…Fault-aware scheduling is significant for the cloud and identified with the reception of dynamic workload. R. Kaur et al [6] proposes a pattern similarity-based scheduling for the cloud. To approve the solution, they performed two investigations with conventional technique and with the fault aware technique.…”

Section: A Motivationmentioning

confidence: 99%

“…The measured different F is demonstrating the influence in prediction error; therefore, the system needs to adjust this for making the final prediction by restructuring the prediction equation using the following equation: (6) Now, the system utilizing the predicted resource demand and the available resources of the datacenter to decide to switch ON and OFF, the physical machine. In this context, we prepare an algorithm for making decisions regarding the same, steps are described as algorithm 2.…”

Section: Predictive Vm Schedulingmentioning

confidence: 99%

See 1 more Smart Citation

Proactive Virtual Machine Scheduling to Optimize the Energy Consumption of Computational Cloud

Saxena¹,

Khan²,

Singh³

et al. 2021

IJACSA

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Section: A Motivationmentioning

confidence: 99%

Section: Predictive Vm Schedulingmentioning

confidence: 99%

Proactive Virtual Machine Scheduling to Optimize the Energy Consumption of Computational Cloud

Saxena¹,

Khan²,

Singh³

et al. 2021

IJACSA

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“…This technique has been applied to different fields especially in information retrievals such as text mining, image segmentation, data mining, and pattern recognition [18]. Recently, clustering technique plays an important role for proactive scheduling in cloud computing [19]. There are various clustering algorithms can be used by researchers, but the performance and execution time is different according to which one is used.…”

Section: Related Workmentioning

confidence: 99%

Improving Classification Accuracy Using Clustering Technique

2018

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Product classification is the key issue in e-commerce domains. Many products are released to the market rapidly and to select the correct category in taxonomy for each product has become a challenging task. The application of classification model is useful to precisely classify the products. The study proposed a method to apply clustering prior to classification. This study has used a large-scale real-world data set to identify the efficiency of clustering technique to improve the classification model. The conventional text classification procedures are used in the study such as preprocessing, feature extraction and feature selection before applying the clustering technique. Results show that clustering technique improves the accuracy of the classification model. The best classification model for all three approaches which are classification model only, classification with hierarchical clustering and classification with K-means clustering is K-Nearest Neighbor (KNN) model. Even though the accuracy of the KNN models are the same across different approaches but the KNN model with K-means clustering had the shortest time of execution. Hence, applying K-means clustering prior to KNN model helps in reducing the computation time.

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“…Dynamic algorithm depends on present state, and there is no need for prior knowledge of the system. Dynamic algorithm can be classified into distributed and non-distributed algorithm [11]. In distributed algorithm, the work load is harmonized 82 between all the nodes collectively.…”

Section: Introductionmentioning

confidence: 99%