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Performance Anomaly Detection using Datacenter Landscape Graphs
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Distributed Systems)ORCID iD: 0000-0002-3308-834X
Intel Labs Europe, Collingstown Industrial Park, Leixlip, Ireland.
Intel Labs Europe, Collingstown Industrial Park, Leixlip, Ireland.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2017 (English)In: 2nd IEEE International Conference on Big Data, Cloud Computing, and Data Science (BCD 2017), Jul 9-13, Hamamatsu, Japan., IEEE Computer Society, 2017, p. 301-308Conference paper, Published paper (Refereed)
Abstract [en]

The migration of mission-critical workloads to the cloud and the automation of various aspects of datacenter management is contributing to the evolution of software-defined infrastructures. One implication of this evolution is that the composition (both physical and virtual) and logical topology of datacenters is becoming even more dynamic. Identification of performance problems (e.g.\ bottlenecks) in such environments needs to be done with awareness of this dynamic topology to understand the impact of dependencies among components. A technique is introduced that a) employs expert knowledge to identify bottleneck components using associated performance metrics, and b) utilizes dynamic dependencies to rank problem components in order to facilitate diagnosis efforts. The technique is demonstrated experimentally on an OpenStack testbed with realistic fault injection. Results of experiment case studies show that the technique is able to correctly detect and rank problem nodes. 

Place, publisher, year, edition, pages
IEEE Computer Society, 2017. p. 301-308
National Category
Computer Systems
Research subject
Computing Science
Identifiers
URN: urn:nbn:se:umu:diva-142023OAI: oai:DiVA.org:umu-142023DiVA, id: diva2:1157920
Conference
2nd IEEE International Conference on Big Data, Cloud Computing, and Data Science (BCD 2017)
Projects
Cloud Control (C0590801)
Funder
Swedish Research Council, C0590801Available from: 2017-11-17 Created: 2017-11-17 Last updated: 2018-06-09
In thesis
1. Performance anomaly detection and resolution for autonomous clouds
Open this publication in new window or tab >>Performance anomaly detection and resolution for autonomous clouds
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Fundamental properties of cloud computing such as resource sharing and on-demand self-servicing is driving a growing adoption of the cloud for hosting both legacy and new application services. A consequence of this growth is that the increasing scale and complexity of the underlying cloud infrastructure as well as the fluctuating service workloads is inducing performance incidents at a higher frequency than ever before with far-reaching impact on revenue, reliability, and reputation. Hence, effectively managing performance incidents with emphasis on timely detection, diagnosis and resolution has thus become a necessity rather than luxury. While other aspects of cloud management such as monitoring and resource management are experiencing greater automation, automated management of performance incidents remains a major concern.

Given the volume of operational data produced by cloud datacenters and services, this thesis focus on how data analytics techniques can be used in the aspect of cloud performance management. In particular, this work investigates techniques and models for automated performance anomaly detection and prevention in cloud environments. To familiarize with developments in the research area, we present the outcome of an extensive survey of existing research contributions addressing various aspects of performance problem management in diverse systems domains. We discuss the design and evaluation of analytics models and algorithms for detecting performance anomalies in real-time behaviour of cloud datacenter resources and hosted services at different resolutions. We also discuss the design of a semi-supervised machine learning approach for mitigating performance degradation by actively driving quality of service from undesirable states to a desired target state via incremental capacity optimization. The research methods used in this thesis include experiments on real virtualized testbeds to evaluate aspects of proposed techniques while other aspects are evaluated using performance traces from real-world datacenters.

Insights and outcomes from this thesis can be used by both cloud and service operators to enhance the automation of performance problem detection, diagnosis and resolution. They also have the potential to spur further research in the area while being applicable in related domains such as Internet of Things (IoT), industrial sensors as well as in edge and mobile clouds.

Abstract [sv]

Grundläggande egenskaper för datormoln såsom resursdelning och självbetjäning driver ett växande nyttjande av molnet för internettjänster. En följd av denna tillväxt är att den underliggande molninfrastrukturens ökande storlek och komplexitet samt fluktuerade arbetsbelastning orsakar prestandaincidenter med högre frekvens än någonsin tidigare. En konsekvens av detta blir omfattande inverkan på intäkter, tillförlitlighet och rykte för de som äger tjänsterna. Det har därför blivit viktigt att snabbt och effektivt hantera prestandaincidenter med avseende på upptäckt, diagnos och korrigering. Även om andra aspekter av resurshantering för datormoln, som övervakning och resursallokering, på senare tid automatiserats i allt högre grad så är automatiserad hantering av prestandaincidenter fortfarande ett stort problem.

Denna avhandling fokuserar på hur prestandahanteringen i molndatacenter kan förbättras genom användning av dataanalystekniker på de stora datamängder som produceras i de system som monitorerar prestanda hos datorresurser och tjänster. I synnerhet undersöks tekniker och modeller för automatisk upptäckt och förebyggande av prestandaanomalier i datormoln. För att kartlägga utvecklingen inom forskningsområdet presenterar vi resultatet av en omfattande undersökning av befintliga forskningsbidrag som behandlar olika aspekter av hantering av prestandaproblem inom i relevanta tillämpningsområden. Vi diskuterar design och utvärdering av analysmodeller och algoritmer för att upptäcka prestandaanomalier i realtid hos resurser och tjänster. Vi diskuterar också utformningen av ett maskininlärningsbaserat tillvägagångssätt för att mildra prestandaförluster genom att aktivt driva tjänsternas kvalitet från oönskade tillstånd till ett önskat målläge genom inkrementell kapacitetoptimering. Forskningsmetoderna som används i denna avhandling innefattar experiment på verkliga virtualiserade testmiljöer för att utvärdera aspekter av föreslagna tekniker medan andra aspekter utvärderas med hjälp av belastningsmönster från verkliga datacenter.

Insikter och resultat från denna avhandling kan användas av både moln- och tjänsteoperatörer för att bättre automatisera detekteringen av prestandaproblem, inklusive dess diagnos och korrigering. Resultaten har också potential att uppmuntra vidare forskning inom området samtidigt som de är användbara inom relaterade områden som internet-av-saker, industriella sensorer, och storskaligt distribuerade moln eller telekomnätverk.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2017. p. 60
Series
Report / UMINF, ISSN 0348-0542 ; 17.18
Keywords
Cloud Computing, Distributed Systems, Performance Management, Anomaly Detection, Quality of Service, Performance Analytics, Machine Learning
National Category
Computer Systems
Research subject
Computer Systems; Computing Science; Computer Science
Identifiers
urn:nbn:se:umu:diva-142033 (URN)978-91-7601-800-2 (ISBN)
Public defence
2017-12-14, MA121, MIT-huset, Umeå University, Umeå, 13:15 (English)
Opponent
Supervisors
Projects
Cloud ControleSSENCE
Funder
Swedish Research Council, C0590801
Available from: 2017-11-21 Created: 2017-11-17 Last updated: 2018-06-09Bibliographically approved

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Ibidunmoye, OlumuyiwaElmroth, Erik

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