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Adaptive Service Performance Control using Cooperative Fuzzy Reinforcement Learning in Virtualized Environments
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Distributed Systems)ORCID iD: 0000-0002-3308-834X
Department of Computer Engineering, University of Kashan.
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Distributed Systems)
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Distributed Systems)
2017 (English)In: UCC '17 Proceedings of the10th International Conference on Utility and Cloud Computing, IEEE/ACM , 2017, p. 19-28Conference paper, Published paper (Refereed)
Abstract [en]

Designing efficient control mechanisms to meet strict performance requirements with respect tochanging workload demands without sacrificing resource efficiency remains a challenge in cloudinfrastructures. A popular approach is fine-grained resource provisioning via auto-scaling mechanisms that rely on either threshold-based adaptation rules or sophisticated queuing/control-theoretic models. While it is difficult at design time to specify optimal threshold rules, it is even more challenging inferring precise performance models for the multitude of services. Recently, reinforcement learning have been applied to address this challenge. However, such approaches require many learning trials to stabilize at the beginning and when operational conditions vary thereby limiting their application under dynamic workloads. To this end, we extend the standard reinforcement learning approach in two ways: a) we formulate the system state as a fuzzy space and b) exploit a set of cooperative agents to explore multiple fuzzy states in parallel to speed up learning. Through multiple experiments on a real virtualized testbed, we demonstrate that our approach converges quickly, meets performance targets at high efficiency without explicit service models.

Place, publisher, year, edition, pages
IEEE/ACM , 2017. p. 19-28
Keywords [en]
Performance control, Resource allocation, Quality of service, Reinforcement learning, Autoscaling, Autonomic computing
National Category
Computer Systems
Research subject
Computer Systems; business data processing
Identifiers
URN: urn:nbn:se:umu:diva-142032DOI: 10.1145/3147213.3147225ISBN: 978-1-4503-5149-2 (print)OAI: oai:DiVA.org:umu-142032DiVA, id: diva2:1157923
Conference
10th IEEE/ACM International Conference on Utility and Cloud Computing, Austin, Texas, USA, December 5-8, 2017
Projects
Cloud Control
Funder
Swedish Research Council, C0590801Available from: 2017-11-17 Created: 2017-11-17 Last updated: 2019-06-19Bibliographically approved
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; business data processing; 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, OlumuyiwaLakew, Ewnetu BayuhElmroth, Erik

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