Proactive Cloud Management for Highly Heterogeneous Multi-cloud Infrastructures

Pellegrini, Alessandro; Sanzo, Pierangelo Di; Avresky, Dimiter R.

doi:10.1109/ipdpsw.2016.124

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…Regions can also be heterogeneous in terms of computing resources and distributed at a geographical scale. 23,24 Each single region is managed according to the architecture described in Section 4. In this section, we discuss the inter-region application management capabilities offered by OCES.…”

Section: Management Of Virtual Resources For Increased Responsivenessmentioning

confidence: 99%

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Autonomic rejuvenation of cloud applications as a countermeasure to software anomalies

Sanzo

Avresky²,

Pellegrini

2020

Softw Pract Exp

Self Cite

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Failures in computer systems can be often tracked down to software anomalies of various kinds. In many scenarios, it might be difficult, unfeasible, or unprofitable to carry out extensive debugging activity to spot the cause of anomalies and remove them. In other cases, taking corrective actions may led to undesirable service downtime. In this article, we propose an alternative approach to cope with the problem of software anomalies in cloud-based applications, and we present the design of a distributed autonomic framework that implements our approach. It exploits the elastic capabilities of cloud infrastructures, and relies on machine learning models, proactive rejuvenation techniques, and a new load balancing approach. By putting together all these elements, we show that it is possible to improve both availability and performance of applications deployed to heterogeneous cloud regions and subject to frequent failures. Overall, our study demonstrates the viability of our approach, thus opening the way towards its adoption, and encouraging further studies and practical experiences to evaluate and improve it.

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Section: Management Of Virtual Resources For Increased Responsivenessmentioning

confidence: 99%

“…The OCES framework allows to manage any number of VMs across multiple cloud regions. Regions can also be heterogeneous in terms of computing resources and distributed at a geographical scale 23,24 . Each single region is managed according to the architecture described in Section 4.…”

Section: Management Of Virtual Resources For Increased Responsivenessmentioning

confidence: 99%

Autonomic rejuvenation of cloud applications as a countermeasure to software anomalies

Sanzo

Avresky²,

Pellegrini

2020

Softw Pract Exp

Self Cite

View full text Add to dashboard Cite

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“…Furthermore, in this work we are not limiting our experiments to a single system or application component failure, rather we attempt to predict several failures across the entire infrastructure. For more comprehensive review on other literatures the reader is referred to [28][29][30][31][32][33][34][35][36], [47][48][49][50][51][52][53].…”

Section: Related Workmentioning

confidence: 99%

Failure prediction using machine learning in a virtualised HPC system and application

et al. 2019

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Failure is an increasingly important issue in high performance computing and cloud systems. As large-scale systems continue to grow in scale and complexity, mitigating the impact of failure and providing accurate predictions with sufficient lead time remains a challenging research problem. Traditional existing fault-tolerance strategies such as regular checkpointing and replication are not adequate because of the emerging complexities of high performance computing systems. This necessitates the importance of having an effective as well as proactive failure management approach in place aimed at minimizing the effect of failure within the system. With the advent of machine learning techniques, the ability to learn from past information to predict future pattern of behaviours makes it possible to predict potential system failure more accurately. Thus, in this paper, we explore the predictive abilities of machine learning by applying a number of algorithms to improve the accuracy of failure prediction. We have developed a failure prediction model using time series and machine learning, and performed comparison based tests on the prediction accuracy. The primary algorithms we considered are the Support Vector Machine (SVM), Random Forest(RF), k-Nearest Neighbors (KNN), Classification and Regression Trees (CART) and Linear Discriminant Analysis (LDA). Experimental results indicates that the average prediction accuracy of our model using SVM when predicting fail

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“…Furthermore, in this work we are not limiting our experiments to a single hardware, rather we attempt to predict several hardware failures within a cloud infrastructure. For more comprehensive review on other literatures or works by other scholars, the reader is referred to [22], [23] , [24], [25], [26] , [27], [28], [29], [5], [6], [30] III. OVERVIEW OF THE NERSC DATA This NERSC data [42] was collected with the purpose of providing failure specifics for I/O related systems and components in as much detail as possible so that analysis might produce some useful findings.…”

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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Proactive Cloud Management for Highly Heterogeneous Multi-cloud Infrastructures

Cited by 7 publications

References 30 publications

Autonomic rejuvenation of cloud applications as a countermeasure to software anomalies

Autonomic rejuvenation of cloud applications as a countermeasure to software anomalies

Failure prediction using machine learning in a virtualised HPC system and application

An Approach to Failure Prediction in a Cloud Based Environment

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