A Black-box Approach for Detecting Systems Anomalies in Virtualized Environments

Ibidunmoye, OlumuyiwaUmeå universitet,Institutionen för datavetenskap,Distributed Systems (författare)

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.

TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Datorsystem hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Computer Systems hsv//eng
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

Lakew, Ewnetu BayuhUmeå universitet,Institutionen för datavetenskap(Swepub:umu)ewba0001 (författare)
Elmroth, ErikUmeå universitet,Institutionen för datavetenskap(Swepub:umu)erel0001 (författare)
Umeå universitetInstitutionen för datavetenskap (creator_code:org_t)

Ingår i:2017 IEEE International Conference on Cloud and Autonomic Computing (ICCAC 2017): IEEE, s. 22-339781538619391

Av författaren/redakt...: Ibidunmoye, Olum ...; Lakew, Ewnetu Ba ...; Elmroth, Erik

Om ämnet

TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Elektroteknik oc ...; och Datorsystem

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.