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Whetstone: Reliable monitoring of cloud services

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Whetstone : Reliable monitoring of cloud services. / Zhang, H.; Luna, J.; Trapero, R.; Suri, Neeraj.

2018 IEEE International Conference on Smart Computing (SMARTCOMP). IEEE, 2018. p. 115-122.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Zhang, H, Luna, J, Trapero, R & Suri, N 2018, Whetstone: Reliable monitoring of cloud services. in 2018 IEEE International Conference on Smart Computing (SMARTCOMP). IEEE, pp. 115-122. https://doi.org/10.1109/SMARTCOMP.2018.00081

APA

Zhang, H., Luna, J., Trapero, R., & Suri, N. (2018). Whetstone: Reliable monitoring of cloud services. In 2018 IEEE International Conference on Smart Computing (SMARTCOMP) (pp. 115-122). IEEE. https://doi.org/10.1109/SMARTCOMP.2018.00081

Vancouver

Zhang H, Luna J, Trapero R, Suri N. Whetstone: Reliable monitoring of cloud services. In 2018 IEEE International Conference on Smart Computing (SMARTCOMP). IEEE. 2018. p. 115-122 https://doi.org/10.1109/SMARTCOMP.2018.00081

Author

Zhang, H. ; Luna, J. ; Trapero, R. ; Suri, Neeraj. / Whetstone : Reliable monitoring of cloud services. 2018 IEEE International Conference on Smart Computing (SMARTCOMP). IEEE, 2018. pp. 115-122

Bibtex

@inproceedings{148fb601461c4a9c9d94634f84601a49,
title = "Whetstone: Reliable monitoring of cloud services",
abstract = "Cloud services have become powerful enablers for a variety of smart computing solutions supporting multimedia, social networking, e-commerce and critical infrastructures among others. Consequently, as we increasingly depend on the cloud, the need exists to ensure its effective role as a trustworthy services platform. Towards this objective, a plethora of cloud monitoring mechanisms have been proposed which typically assume that the collected monitoring information is reliably correct. In reality, the information collected by cloud monitors is often susceptible to reliability issues (e.g., monitor malfunctions, data corruptions, or data tampering), and obtaining reliable cloud monitoring information is still an open issue. We propose Whetstone as a novel approach to address the gap where an efficient approach of ascertaining reliable values from a set of collected monitoring data is required. To this end, Whetstone first introduces a statistical approach to filter defective data from the collected data set. Next, Whetstone develops an optimization approach to quantify the reliability of the collected data by leveraging the value deviation of the collected data. Finally, Whetstone devises a weighted aggregation approach for generating the reliable value based on the obtained information. We evaluate the proposed approach with different experimental configurations. The experimental results demonstrate the efficacy of our approach for successfully generating the maximum likelihood reliable value for raw data sets. {\textcopyright} 2018 IEEE.",
keywords = "cloud, Reliability, Security, Service monitoring, Clouds, Distributed computer systems, Distributed database systems, Maximum likelihood, Multimedia services, Social sciences computing, Web services, Cloud monitoring, Data corruption, Monitoring information, Optimization approach, Services platforms, Statistical approach, Monitoring",
author = "H. Zhang and J. Luna and R. Trapero and Neeraj Suri",
year = "2018",
month = jun
day = "18",
doi = "10.1109/SMARTCOMP.2018.00081",
language = "English",
isbn = "9781538647066",
pages = "115--122",
booktitle = "2018 IEEE International Conference on Smart Computing (SMARTCOMP)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Whetstone

T2 - Reliable monitoring of cloud services

AU - Zhang, H.

AU - Luna, J.

AU - Trapero, R.

AU - Suri, Neeraj

PY - 2018/6/18

Y1 - 2018/6/18

N2 - Cloud services have become powerful enablers for a variety of smart computing solutions supporting multimedia, social networking, e-commerce and critical infrastructures among others. Consequently, as we increasingly depend on the cloud, the need exists to ensure its effective role as a trustworthy services platform. Towards this objective, a plethora of cloud monitoring mechanisms have been proposed which typically assume that the collected monitoring information is reliably correct. In reality, the information collected by cloud monitors is often susceptible to reliability issues (e.g., monitor malfunctions, data corruptions, or data tampering), and obtaining reliable cloud monitoring information is still an open issue. We propose Whetstone as a novel approach to address the gap where an efficient approach of ascertaining reliable values from a set of collected monitoring data is required. To this end, Whetstone first introduces a statistical approach to filter defective data from the collected data set. Next, Whetstone develops an optimization approach to quantify the reliability of the collected data by leveraging the value deviation of the collected data. Finally, Whetstone devises a weighted aggregation approach for generating the reliable value based on the obtained information. We evaluate the proposed approach with different experimental configurations. The experimental results demonstrate the efficacy of our approach for successfully generating the maximum likelihood reliable value for raw data sets. © 2018 IEEE.

AB - Cloud services have become powerful enablers for a variety of smart computing solutions supporting multimedia, social networking, e-commerce and critical infrastructures among others. Consequently, as we increasingly depend on the cloud, the need exists to ensure its effective role as a trustworthy services platform. Towards this objective, a plethora of cloud monitoring mechanisms have been proposed which typically assume that the collected monitoring information is reliably correct. In reality, the information collected by cloud monitors is often susceptible to reliability issues (e.g., monitor malfunctions, data corruptions, or data tampering), and obtaining reliable cloud monitoring information is still an open issue. We propose Whetstone as a novel approach to address the gap where an efficient approach of ascertaining reliable values from a set of collected monitoring data is required. To this end, Whetstone first introduces a statistical approach to filter defective data from the collected data set. Next, Whetstone develops an optimization approach to quantify the reliability of the collected data by leveraging the value deviation of the collected data. Finally, Whetstone devises a weighted aggregation approach for generating the reliable value based on the obtained information. We evaluate the proposed approach with different experimental configurations. The experimental results demonstrate the efficacy of our approach for successfully generating the maximum likelihood reliable value for raw data sets. © 2018 IEEE.

KW - cloud

KW - Reliability

KW - Security

KW - Service monitoring

KW - Clouds

KW - Distributed computer systems

KW - Distributed database systems

KW - Maximum likelihood

KW - Multimedia services

KW - Social sciences computing

KW - Web services

KW - Cloud monitoring

KW - Data corruption

KW - Monitoring information

KW - Optimization approach

KW - Services platforms

KW - Statistical approach

KW - Monitoring

U2 - 10.1109/SMARTCOMP.2018.00081

DO - 10.1109/SMARTCOMP.2018.00081

M3 - Conference contribution/Paper

SN - 9781538647066

SP - 115

EP - 122

BT - 2018 IEEE International Conference on Smart Computing (SMARTCOMP)

PB - IEEE

ER -