A New Approach for VM Failure Prediction using Stochastic Model in Cloud

Rawat, Ajay Kumar Singh; Sushil, Rama; Agarwal, Amit; Sikander, Afzal

doi:10.1080/03772063.2018.1537814

Cited by 12 publications

(4 citation statements)

References 24 publications

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“…A study on VM failure prediction was carried out by Meenakumari et al [28], Alkasem et al [29], Qasem et al [30], Liu et al [31] and Rawat et al [32]. The study by Meenakumari et al [28] employed a dynamic thresholding approach to predict failure based on system metrics such as CPU utilisation, CPU usage, bandwidth, temperature, and memory.…”

Section: Vm-level Failure Predictionmentioning

confidence: 99%

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A Combined System Metrics Approach to Cloud Service Reliability Using Artificial Intelligence

Chhetri

Dehury

Lind

et al. 2022

BDCC

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Identifying and anticipating potential failures in the cloud is an effective method for increasing cloud reliability and proactive failure management. Many studies have been conducted to predict potential failure, but none have combined SMART (self-monitoring, analysis, and reporting technology) hard drive metrics with other system metrics, such as central processing unit (CPU) utilisation. Therefore, we propose a combined system metrics approach for failure prediction based on artificial intelligence to improve reliability. We tested over 100 cloud servers’ data and four artificial intelligence algorithms: random forest, gradient boosting, long short-term memory, and gated recurrent unit, and also performed correlation analysis. Our correlation analysis sheds light on the relationships that exist between system metrics and failure, and the experimental results demonstrate the advantages of combining system metrics, outperforming the state-of-the-art.

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Section: Vm-level Failure Predictionmentioning

confidence: 99%

“…Similar to Qasem et al [30], Rawat et al conducted a VM failure prediction study using simulated data. However, unlike Qasem et al [30], Rawat et al [32] focused on using an autoregressive integrated moving average and the Box-Jenkin method. Saxena et al [11] proposed an online model for VM failure prediction and tolerance.…”

Section: Vm-level Failure Predictionmentioning

confidence: 99%

A Combined System Metrics Approach to Cloud Service Reliability Using Artificial Intelligence

Chhetri

Dehury

Lind

et al. 2022

BDCC

View full text Add to dashboard Cite

show abstract

“…El Fazziki et al [27] examined user-based CF on two datasets, film trust and MovieLens, and found that it works well and enhances prediction accuracy. Rawat et al [28] proposed a method for predicting virtual machine failure based on a time series stochastic model that accurately predicts failure. Various evaluation techniques like precision, recall, F-measure, accuracy for machine learning based algorithm are discussed by Yadla and Rao [29].…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting movie rating using k-nearest neighbor based collaborative filtering

Rokade

Rao

Kumari

2022

IJECE

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<p><span lang="EN-US">Expressing reviews in the form of sentiments or ratings for item used or movie seen is the part of human habit. These reviews are easily available on different social websites. Based on interest pattern of a user, it is important to recommend him the items. Recommendation system is playing a vital role in everyone’s life as demand of recommendation for user’s interest increasing day by day. Movie recommendation system based on available ratings for a movie has become interesting part for new users. Till today, a lot many recommendation systems are designed using several machine learning algorithms. Still, sparsity problems, cold start problem, scalability, grey sheep problem are the hurdles for the recommendation systems that must be resolved using hybrid algorithms. We proposed in this paper, a movie rating system using a k-nearest neighbor (KNN-based) collaborative filtering (CF) approach. We compared user’s ratings for different movies to get top K users. Then we have used this top K set to find missing ratings by user for a movie using CF. Our proposed system when evaluated for various criteria shows promising results for movie recommendations compared with existing systems.</span></p>

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“…There are three main works identified under this approach. Firstly, the work by Rawat et al [46] For failure prediction, HORA utilizes RF as the base machine learning algorithm.…”

Section: Statistical Machine Learning Approachmentioning

confidence: 99%

Cloud Failure Prediction based on Traditional Machine Learning and Deep Learning

Asmawi

Ismail

Shen

2022

Preprint

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Cloud failure is one of the critical issues since it can cost millions to the cloud service providers in addition to the loss of productivity being suffered by the industrial users using different cloud-based applications and services. As the cloud system grows larger, the process of failure prediction is still a challenge for both practitioners and academic researchers. In this study, we tackle the challenge of predicting job and task failure using the benchmarking Google Cluster Traces Dataset. We first propose a conceptual model to prepare, construct and evaluate five traditional machine learning algorithms and three variants of the latest deep learning algorithms for predicting job and task failures. We then perform a series of experiments on two datasets; (1) Dataset A for the job failure prediction models and (2) Dataset B for the task failure prediction models. In the case of job failure prediction experiments, we observe that Extreme Gradient Boosting is the best performance with 94.35% accuracy with the F-Score score of 0.9310, 91.92% sensitivity, and 96.07% specificity. For Extreme Gradient Boosting, the disk space request and CPU request are the most important features in determining the outcome of the job prediction. In the case of task failure prediction, we observe that Decision Tree and Random Forest have achieved the highest 89.75% accuracy and 0.9145 for F-Score with 98% sensitivity and 78% specificity. The priority of the task is the most important feature for determining the task prediction outcome for both, the Decision Tree and Random Forest.

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A New Approach for VM Failure Prediction using Stochastic Model in Cloud

Cited by 12 publications

References 24 publications

A Combined System Metrics Approach to Cloud Service Reliability Using Artificial Intelligence

A Combined System Metrics Approach to Cloud Service Reliability Using Artificial Intelligence

Forecasting movie rating using k-nearest neighbor based collaborative filtering

Cloud Failure Prediction based on Traditional Machine Learning and Deep Learning

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