Towards Resilient Method: An exhaustive survey of fault tolerance methods in the cloud computing environment

Shahid, Muhammad Asim; Islam, Noman; Alam, Muhammad Mansoor; Su’ud, Mazliham Mohd; Musa, Shahrulniza

doi:10.1016/j.cosrev.2021.100398

Cited by 41 publications

(14 citation statements)

References 66 publications

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“…Therefore, fault tolerance is one of the fundamental requirements of cloud computing. There are different ways of classifying fault tolerance approaches [5,8,9]. Here, we highlight two major tolerance approaches, namely the reactive and proactive approach.…”

Section: Fault Tolerance In Cloud Computingmentioning

confidence: 99%

Cloud failure prediction based on traditional machine learning and deep learning

2022

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Cloud failure is one of the critical issues since it can cost millions of dollars to cloud service providers, in addition to the loss of productivity suffered by industrial users. Fault tolerance management is the key approach to address this issue, and failure prediction is one of the techniques to prevent the occurrence of a failure. One of the main challenges in performing failure prediction is to produce a highly accurate predictive model. Although some work on failure prediction models has been proposed, there is still a lack of a comprehensive evaluation of models based on different types of machine learning algorithms. Therefore, in this paper, we propose a comprehensive comparison and model evaluation for predictive models for job and task failure. These models are built and trained using five traditional machine learning algorithms and three variants of deep learning algorithms. We use a benchmark dataset, called Google Cloud Traces, for training and testing the models. We evaluated the performance of models using multiple metrics and determined their important features, as well as measured their scalability. Our analysis resulted in the following findings. Firstly, in the case of job failure prediction, we found that Extreme Gradient Boosting produces the best model where the disk space request and CPU request are the most important features that influence the prediction. Second, for task failure prediction, we found that Decision Tree and Random Forest produce the best models where the priority of the task is the most important feature for both models. Our scalability analysis has determined that the Logistic Regression model is the most scalable as compared to others.

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Section: Fault Tolerance In Cloud Computingmentioning

confidence: 99%

Cloud failure prediction based on traditional machine learning and deep learning

2022

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“…There are two major tolerance approaches that are currently being implemented in the cloud computing [18]. The work by Shah et al [19] has studied and summarized the related research of fault tolerance in cloud computing from the year 2013 until 2020.…”

Section: Fault Tolerance In Cloud Computingmentioning

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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“…Several previous studies attempted to incorporate the various FT frameworks and cloud solutions proposed, but certain accounts proved to be limited. The ability of a system to perform properly in the presence of internal faults is referred to as FT [ 3 , 4 ]. In fault tolerance, there are four types of system faults: transient faults, intermittent faults, permanent faults, and Byzantine faults [ 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Achieving Reliability in Cloud Computing by a Novel Hybrid Approach

Shahid

Alam

Su’ud

2023

Sensors

Self Cite

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Cloud computing (CC) benefits and opportunities are among the fastest growing technologies in the computer industry. Cloud computing’s challenges include resource allocation, security, quality of service, availability, privacy, data management, performance compatibility, and fault tolerance. Fault tolerance (FT) refers to a system’s ability to continue performing its intended task in the presence of defects. Fault-tolerance challenges include heterogeneity and a lack of standards, the need for automation, cloud downtime reliability, consideration for recovery point objects, recovery time objects, and cloud workload. The proposed research includes machine learning (ML) algorithms such as naïve Bayes (NB), library support vector machine (LibSVM), multinomial logistic regression (MLR), sequential minimal optimization (SMO), K-nearest neighbor (KNN), and random forest (RF) as well as a fault-tolerance method known as delta-checkpointing to achieve higher accuracy, lesser fault prediction error, and reliability. Furthermore, the secondary data were collected from the homonymous, experimental high-performance computing (HPC) system at the Swiss Federal Institute of Technology (ETH), Zurich, and the primary data were generated using virtual machines (VMs) to select the best machine learning classifier. In this article, the secondary and primary data were divided into two split ratios of 80/20 and 70/30, respectively, and cross-validation (5-fold) was used to identify more accuracy and less prediction of faults in terms of true, false, repair, and failure of virtual machines. Secondary data results show that naïve Bayes performed exceptionally well on CPU-Mem mono and multi blocks, and sequential minimal optimization performed very well on HDD mono and multi blocks in terms of accuracy and fault prediction. In the case of greater accuracy and less fault prediction, primary data results revealed that random forest performed very well in terms of accuracy and fault prediction but not with good time complexity. Sequential minimal optimization has good time complexity with minor differences in random forest accuracy and fault prediction. We decided to modify sequential minimal optimization. Finally, the modified sequential minimal optimization (MSMO) algorithm with the fault-tolerance delta-checkpointing (D-CP) method is proposed to improve accuracy, fault prediction error, and reliability in cloud computing.

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Towards Resilient Method: An exhaustive survey of fault tolerance methods in the cloud computing environment

Cited by 41 publications

References 66 publications

Cloud failure prediction based on traditional machine learning and deep learning

Cloud failure prediction based on traditional machine learning and deep learning

Cloud Failure Prediction based on Traditional Machine Learning and Deep Learning

Achieving Reliability in Cloud Computing by a Novel Hybrid Approach

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