AnomalyDetect: Anomaly Detection for Preserving Availability of Virtualized Cloud Services
| Title | AnomalyDetect: Anomaly Detection for Preserving Availability of Virtualized Cloud Services |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | August, M. A., Diallo, M. H., Graves, C. T., Slayback, S. M., Glasser, D. |
| Conference Name | 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS*W) |
| Date Published | Sept. 2017 |
| Publisher | IEEE |
| ISBN Number | 978-1-5090-6558-5 |
| Keywords | anomaly detection, AnomalyDetect approach, AutoMigrate framework, autonomic cloud availability, Autonomic Cloud Security, Autonomic Security, cloud computing, cloud disruption, Cloud Security, cloud services, cloud services migration, Detectors, historical data, Intelligent systems, interconnected virtual machines, Kalman filter, Kalman filters, live migration, Live Migration of Virtual Machines, Metrics, Monitoring, potential anomalies forecasting, pubcrawl, Resiliency, Scalability, security of data, software fault tolerance, Time series analysis, virtual machines, Virtual machining, virtualized cloud services |
| Abstract | In this paper, we present AnomalyDetect, an approach for detecting anomalies in cloud services. A cloud service consists of a set of interacting applications/processes running on one or more interconnected virtual machines. AnomalyDetect uses the Kalman Filter as the basis for predicting the states of virtual machines running cloud services. It uses the cloud service's virtual machine historical data to forecast potential anomalies. AnomalyDetect has been integrated with the AutoMigrate framework and serves as the means for detecting anomalies to automatically trigger live migration of cloud services to preserve their availability. AutoMigrate is a framework for developing intelligent systems that can monitor and migrate cloud services to maximize their availability in case of cloud disruption. We conducted a number of experiments to analyze the performance of the proposed AnomalyDetect approach. The experimental results highlight the feasibility of AnomalyDetect as an approach to autonomic cloud availability. |
| URL | https://ieeexplore.ieee.org/document/8064145 |
| DOI | 10.1109/FAS-W.2017.169 |
| Citation Key | august_anomalydetect:_2017 |
- Kalman filter
- virtualized cloud services
- Virtual machining
- virtual machines
- Time series analysis
- software fault tolerance
- security of data
- Scalability
- Resiliency
- pubcrawl
- potential anomalies forecasting
- Monitoring
- Metrics
- Live Migration of Virtual Machines
- live migration
- Kalman filters
- Anomaly Detection
- interconnected virtual machines
- Intelligent systems
- historical data
- Detectors
- cloud services migration
- cloud services
- Cloud Security
- cloud disruption
- Cloud Computing
- Autonomic Security
- Autonomic Cloud Security
- autonomic cloud availability
- AutoMigrate framework
- AnomalyDetect approach