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A Black-box Approac...
A Black-box Approach for Detecting Systems Anomalies in Virtualized Environments
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- Ibidunmoye, Olumuyiwa (author)
- Umeå universitet,Institutionen för datavetenskap,Distributed Systems
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- Lakew, Ewnetu Bayuh (author)
- Umeå universitet,Institutionen för datavetenskap
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- Elmroth, Erik (author)
- Umeå universitet,Institutionen för datavetenskap
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(creator_code:org_t)
- IEEE, 2017
- 2017
- English.
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In: 2017 IEEE International Conference on Cloud and Autonomic Computing (ICCAC 2017). - : IEEE. - 9781538619391 ; , s. 22-33
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Abstract
Subject headings
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- Virtualization technologies allow cloud providers to optimize server utilization and cost by co-locating services in as few servers as possible. Studies have shown how applications in multi-tenant environments are susceptible to systems anomalies such as abnormal resource usage due to performance interference. Effective detection of such anomalies requires techniques that can adapt autonomously with dynamic service workloads, require limited instrumentation to cope with diverse applications services, and infer relationship between anomalies non-intrusively to avoid "alarm fatigue" due to scale. We propose a black-box framework that includes an unsupervised prediction-based mechanism for automated anomaly detection in multi-dimensional resource behaviour of datacenter nodes and a graph-theoretic technique for ranking anomalous nodes across the datacenter. The proposed framework is evaluated using resource traces of over 100 virtual machines obtained from a production cluster as well as traces obtained from an experimental testbed under realistic service composition. The technique achieve average normalized root mean squared forecast error and R^2 of (0.92, 0.07) across hosts servers and (0.70, 0.39) across virtual machines. Also, the average detection rate is 88% while explaining 62% of SLA violations with an average lead-time of 6 time-points when the testbed is actively perturbed under three contention scenarios.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Keyword
- Anomaly Detection
- Performance Anomaly Detection
- Performance Diagnosis
- Cloud Computing
- Virtualized Services
- Unsupervised Learning
- Time Series Analysis
- Quality of Service
- Computer Systems
- datorteknik
- business data processing
- administrativ databehandling
Publication and Content Type
- ref (subject category)
- kon (subject category)
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