Self Monitoring Analysis and Reporting Technology (SMART) Copyback

Rajashekarappa,; Soyjaudah, K. M. S.

doi:10.1007/978-3-642-22786-8_58

Cited by 5 publications

(2 citation statements)

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“…Authors in [19] used an ensemble classifier to achieve hard drive failure prediction on a cloud infrastructure. The data they conducted their work on was acquired through two sources, namely Windows performance counts and Self-Monitoring Analysis and Reporting Technology (S.M.A.R.T or SMART) [21]. This research closely resembles the intended work, but they only considered hard disk failure in the cloud architecture while real time business critical systems relies on other components and not only hard drive, but rather a host of Hardware (such as: CPU, Disk, DIMM, Cable .etc.…”

Section: Related Workmentioning

confidence: 99%

An Approach to Failure Prediction in a Cloud Based Environment

Hussaini

Mohammed

Maina

et al. 2017

2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)

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Failure in a cloud system is defined as an even that occurs when the delivered service deviates from the correct intended behavior. As the cloud computing systems continue to grow in scale and complexity, there is an urgent need for cloud service providers (CSP) to guarantee a reliable on-demand resource to their customers in the presence of faults thereby fulfilling their service level agreement (SLA). Component failures in cloud systems are very familiar phenomena. However, large cloud service providers' data centers should be designed to provide a certain level of availability to the business system. Infrastructure-as-a-service (Iaas) cloud delivery model presents computational resources (CPU and memory), storage resources and networking capacity that ensures high availability in the presence of such failures. The data in-production-faults recorded within a 2 years period has been studied and analyzed from the National Energy Research Scientific computing center (NERSC). Using the real-time data collected from the Computer Failure Data Repository (CFDR), this paper presents the performance of two machine learning (ML) algorithms, Linear Regression (LR) Model and Support Vector Machine (SVM) with a Linear Gaussian kernel for predicting hardware failures in a real-time cloud environment to improve system availability. The performance of the two algorithms have been rigorously evaluated using K-folds cross-validation technique. Furthermore, steps and procedure for future studies has been presented. This research will aid computer hardware companies and cloud service providers (CSP) in designing a reliable fault-tolerant system by providing a better device selection, thereby improving system availability and minimizing unscheduled system downtime.

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Section: Related Workmentioning

confidence: 99%

An Approach to Failure Prediction in a Cloud Based Environment

Hussaini

Mohammed

Maina

et al. 2017

2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)

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“…However, such environmental data is not available in SMART (Self-Monitoring, Analysis, and Reporting) ( (accessed on 13 June 2021)) attributes. SMART attributes are obtained from SMART technology [ 36 ], which enables monitoring of the hard disk’s status (see [ 7 ]) and reporting of various reliability indicators.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Graph Based Hard Drive Failure Prediction

Chhetri

Kurteva

Adigun

et al. 2022

Sensors

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The hard drive is one of the important components of a computing system, and its failure can lead to both system failure and data loss. Therefore, the reliability of a hard drive is very important. Realising this importance, a number of studies have been conducted and many are still ongoing to improve hard drive failure prediction. Most of those studies rely solely on machine learning, and a few others on semantic technology. The studies based on machine learning, despite promising results, lack context-awareness such as how failures are related or what other factors, such as humidity, influence the failure of hard drives. Semantic technology, on the other hand, by means of ontologies and knowledge graphs (KGs), is able to provide the context-awareness that machine learning-based studies lack. However, the studies based on semantic technology lack the advantages of machine learning, such as the ability to learn a pattern and make predictions based on learned patterns. Therefore, in this paper, leveraging the benefits of both machine learning (ML) and semantic technology, we present our study, knowledge graph-based hard drive failure prediction. The experimental results demonstrate that our proposed method achieves higher accuracy in comparison to the current state of the art.

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