Ubl

Dean, Daniel J.; Nguyen, Hiep; Gu, Xiaohui

doi:10.1145/2371536.2371572

Cited by 122 publications

(5 citation statements)

References 18 publications

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“…The ADS proposed in this paper relies on system monitoring data corresponding to software counters (such as CPU consumption, free memory...), collected through the hypervisors or the operating systems hosting the virtual machines on which the cloud services are deployed. As shown in previous works Nguyen et al (2013); Gong et al (2010a); Dean et al (2012); Guan and Fu (2013); Silvestre et al (2014); , such data are well suited to reflect the behavior of the monitored target system at runtime. In our context, relying on such data is also motivated by the requirement to develop generic and service-agnostic detection mechanisms that can be easily applied to different types of services.…”

Section: Introductionmentioning

confidence: 93%

“…As for any computing system, the performance of a cloud service can be observed and analyzed based on several means such as system or application logs, system monitoring data, or audit trails as shown in Simache et al (2002); Simache and Kaâniche (2001); Tan et al (2012); Dean et al (2012).…”

Section: Monitoringmentioning

confidence: 99%

“…Unsupervised algorithms. In Dean et al (2012), anomaly detection is tackled for cloud infrastructures using unsupervised neural networks, making the assumption that anomaly has preliminary symptoms expressed in system monitoring data. Regression models are used in Cherkasova et al (2009) for a workload change detection tool.…”

Section: Related Workmentioning

confidence: 99%

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Anomaly detection and diagnosis for cloud services: Practical experiments and lessons learned

Sauvanaud

Kaniche

Kanoun

et al. 2018

Journal of Systems and Software

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Introductionmentioning

confidence: 93%

Section: Monitoringmentioning

confidence: 99%

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Anomaly detection and diagnosis for cloud services: Practical experiments and lessons learned

Sauvanaud

Kaniche

Kanoun

et al. 2018

Journal of Systems and Software

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“…The literature thus applies self-adaptive systems to manage these threats. Beginning by setting protection scopes, the abnormal behavior can be monitored, analyzed, identified, anticipated, and isolated without much intervention of the centralized system administrator (Chen et al, 2014;Claudel et al, 2006;Dean et al, 2012;Qu et al, 2010). The distributed agents, if provided with proper decision capability, can monitor lowerlevel or front-line signals and react accordingly.…”

Section: Casementioning

confidence: 99%

Toward a Self-Adaptive Cyberdefense Framework in Organization

Huang

Chiang

2021

SAGE Open

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Organizations suffer more than ever from the inability to securely manage the information system, despite their myriad efforts. By introducing a real cyberattack of a bank, this research analyzes the characteristics of modern cyberattacks and simulates the dynamic propagation that makes them difficult to manage. It develops a self-adaptive framework that through simulation, distinctly improves cyberdefense efficiency. The results illustrate the discrepancies of the previous studies and validate the use of a time-based self-adaptive model for cybersecurity management. The results further show the significance of human and organizational learning effects and a coordination mechanism in obtaining a highly dependable cyberdefense setting. This study also provides an illuminating analysis for humans to position themselves in the collaborations with increasingly intelligent agents in the future.

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“…This happens as a result of exhaustion of resources of operating system, accumulation of errors, data corruption, memory leaks, unreleased file locks, data fragments, unreleased database locks and so on. Software aging has been reported in cloud environment [2], web servers [3]- [4], enterprise clusters [5], online transaction processing systems [6], spacecraft systems [7] and military systems [8]. The presented work here focuses on software aging in virtualized environment as it provides the platform for cloud computing environment.…”

Section: Introductionmentioning

confidence: 99%

Software aging prediction and rejuvenation in cloud computing environment: a new approach

Shruthi¹,

Cholli²

2021

IJEECS

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<span>Service availability is one of the major requirements for user satisfaction. Several researches were conducted in recent years to find suitable infrastructure to enhance the availability. Even though both hardware and software are to be in good condition, in recent years, software faults are the major concern for service availability. Software aging is a type of software fault. Software aging occurs as a result of errors accumulation in the internal environment of the system leading to performance degradation. To manage software aging, technique used is software rejuvenation. There exist two kinds of approaches for studying software aging and deriving optimal software rejuvenation schedules. The two approaches are measurement based and model based. In model based approach, analytic models are built for capturing system degradation and rejuvenation process. In measurement based approach, attributes are periodically monitored and that may indicate signs of software aging. In this work, a prototype of measurement based model has been developed. The model captures the aging indicator metrics from cloud environment and rejuvenates once the system reaches aged status. The proposed model uses platform independent, non-intrusive technique for capturing metrics. The rejuvenation carried out after analysing the captured metrics, increases the availability of the service.</span>

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Ubl

Cited by 122 publications

References 18 publications

Anomaly detection and diagnosis for cloud services: Practical experiments and lessons learned

Anomaly detection and diagnosis for cloud services: Practical experiments and lessons learned

Toward a Self-Adaptive Cyberdefense Framework in Organization

Software aging prediction and rejuvenation in cloud computing environment: a new approach

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