High-availability clusters: A taxonomy, survey, and future directions

Somasekaram, Premathas; Călinescu, Radu; Buyya, Rajkumar

doi:10.1016/j.jss.2021.111208

Cited by 10 publications

(9 citation statements)

References 81 publications

(132 reference statements)

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“…Classification [7][8][9], regression [10], and clustering [4] models have been used to improve the performance and increase the detection rate of intrusion detection systems (IDS). Intrusion data is knowns as stream data with large amounts of data generated at high speed in networking [7].…”

Section: Motivationmentioning

confidence: 99%

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Dynamic Evolving Cauchy Possibilistic Clustering Based on the Self-Similarity Principle (DECS) for Enhancing Intrusion Detection System

2022

IJIES

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Unsupervised machine learning plays a critical role in improving the security level of applications and systems. The cyberattack floods the network with data streams to deny services or destroy the network infrastructure. In this paper, a new development strategy (dynamic evolving cauchy possibilistic clustering based on the self-similarity principle (DECS)) is proposed to optimize the data stream clustering model based on the self-similarity principles (inter-cluster and intra-cluster). It is based on computing the self-similarity principles for data between and within clusters. The proposed system mitigates the dependence on the centre of the cluster to produce clusters with highly correlated data and minimal errors within the same cluster. The DECS consists of two phases: In the first phase, the data are clustered based on density points to generate clusters, and in the second phase, the clusters are evolved according to the inter-and intra-distance points to/in the clusters. The proposed model is tested on well-known intrusion datasets such as UNSW-NB15, KDD99 and NSL-KDD. In the experiments, DECS efficiently clustered the data with fewer errors and an optimal number of clusters compared to other models, it has achieved minimum error, high silhouette coefficient and an optimal number of clusters. The implementation results show that the average error of DECS over eight datasets is (0.45) with an average silhouette coefficient (0.63). At the same time, the pure cauchy possibilistic clustering obtains on average error (0.69) and silhouette coefficient (0.44).

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Section: Motivationmentioning

confidence: 99%

“…The stream clustering models significantly suffer from producing a high number of clustering data that might degrade the performance of the clustering performance [10].…”

Section: Huge Sparse Clustersmentioning

confidence: 99%

Dynamic Evolving Cauchy Possibilistic Clustering Based on the Self-Similarity Principle (DECS) for Enhancing Intrusion Detection System

2022

IJIES

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“…To summarise, many studies have explored BDN to support the complex decision-making process under uncertainty. However, no HAC solutions use any form of BDNs 10 . Moreover, we could not find any HAC-related research initiatives that employ BDNs or other stochastic decision models.…”

Section: Related Workmentioning

confidence: 99%

“…We first analyzed and identified characteristics using research studies, technical manuals, and HAC and EA documentation, HAC logs, as well as the taxonomy and survey that we developed in our research project. 10 We organized the identified characteristics into two categories. The first category consists of key HAC characteristics already known to affect resource failure analysis (established characteristics).…”

Section: Hac Characteristics For Predicting Locally Manageable Resour...mentioning

confidence: 99%

“…The ClusterLabs stack HAC (Pacemaker/Corosync) we selected is a comprehensive solution that supports a broad range of features. 10 Although the ClusterLab is an open-source HAC, it has been commercialized and included in commercial HACs (SUSE Linux Enterprise High Availability Extension, Red Hat High Availability Add-On, etc.). We selected this HAC for three reasons: (1) It is based on an open-source license and is free of charge.…”

Section: Testbedmentioning

confidence: 99%

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Predicting locally manageable resource failures of high availability clusters

Somasekaram

Călinescu

2022

Softw Pract Exp

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Critical services from domains as diverse as finance, manufacturing and healthcare are often delivered by complex enterprise applications (EAs). High-availability clusters (HACs) are software-managed IT infrastructures that enable these EAs to operate with minimum downtime. This paper presents a novel Bayesian decision network model to improve the failure detection capabilities of the HACs components using a comprehensive set of characteristics for the analysed component. The model then combines these characteristics to predict whether the failure of this component can be managed locally at the failed component level without propagating the failure to upper-level components and causing a complete system failure. By improving the detection capabilities and predicting locally manageable failures, the model improves the decision-making process of HACs, and has the potential to reduce the downtime and improve availability for the applications protected by HACs. The model uses the capabilities of the Bayesian decision networks, which combines Bayesian networks with the utility theory, to assign weights to different characteristics and consolidate the related variables to output the result. The model evaluation in a realistic testbed environment with three servers, an established HAC and a well-known EA shows that the model can improve the area under the Receiver Operating Characteristic (ROC) curve for prediction of locally manageable failures by up to 9.05% compared to the baseline HAC results.

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