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Performance anomaly detection and resolution for autonomous clouds
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Distributed Systems)ORCID iD: 0000-0002-3308-834X
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 [en]
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: urn:nbn:se:umu:diva-142033ISBN: 978-91-7601-800-2 (print)OAI: oai:DiVA.org:umu-142033DiVA, id: diva2:1157924
Public defence
2017-12-14, MA121, MIT-huset, Umeå University, Umeå, 13:15 (English)
Opponent
Supervisors
Projects
Cloud ControleSSENCE
Funder
Swedish Research Council, C0590801Available from: 2017-11-21 Created: 2017-11-17 Last updated: 2018-06-09Bibliographically approved
List of papers
1. Performance Anomaly Detection and Bottleneck Identification
Open this publication in new window or tab >>Performance Anomaly Detection and Bottleneck Identification
2015 (English)In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 48, no 1, article id 4Article in journal (Refereed) Published
Abstract [en]

In order to meet stringent performance requirements, system administrators must effectively detect undesirable performance behaviours, identify potential root causes and take adequate corrective measures. The problem of uncovering and understanding performance anomalies and their causes (bottlenecks) in different system and application domains is well studied. In order to assess progress, research trends and identify open challenges, we have reviewed major contributions in the area and present our findings in this survey. Our approach provides an overview of anomaly detection and bottleneck identification research as it relates to the performance of computing systems. By identifying fundamental elements of the problem, we are able to categorize existing solutions based on multiple factors such as the detection goals, nature of applications and systems, system observability, and detection methods.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2015
Keywords
Systems performance, performance anomaly detection, bottleneck detection, performance problem identification
National Category
Computer Systems
Research subject
Computer Systems
Identifiers
urn:nbn:se:umu:diva-105991 (URN)10.1145/2791120 (DOI)000363733200004 ()2-s2.0-84938363675 (Scopus ID)
Funder
Swedish Research Council, C0590801
Available from: 2015-07-03 Created: 2015-07-03 Last updated: 2018-06-07Bibliographically approved
2. Apex lake: a framework for enabling smart orchestration
Open this publication in new window or tab >>Apex lake: a framework for enabling smart orchestration
Show others...
2015 (English)In: Proceedings of the Industry Track of the 16th ACM/IFIP/USENIX Middleware Conference, New York, USA: Association for Computing Machinery (ACM), 2015, p. 1-7, article id 1Conference paper, Published paper (Refereed)
Abstract [en]

The introduction of a Software-defined infrastructures brings additional challenges to the management of cloud infrastructure. With the impending convergence of telecommunications and cloud infrastructures, datacenters become an essential part of an overall integrated environment. The potential scale of such environments has significant implications as traditional orchestration approaches cannot scale appropriately. However, the combination of infrastructure topology, fine-grained operational data and advanced analytics, has the potential to deliver a scalable approach to facilitate orchestration and resource management. In this paper we introduce Apex Lake, a framework designed to address the question of "how to efficiently define and maintain a physical and logical resource and service landscape enriched by operational data, to support orchestration for optimized service delivery?" We also demonstrate with a use-case illustrating how functionalities provided by Apex Lake can be used dealing with performance anomalies.

Place, publisher, year, edition, pages
New York, USA: Association for Computing Machinery (ACM), 2015
Keywords
Cloud monitoring and orchestration, Resource Management, Datacenter Management, Software-defined Infrastructure
National Category
Computer Systems
Research subject
Computer Systems
Identifiers
urn:nbn:se:umu:diva-114696 (URN)10.1145/2830013.2830016 (DOI)2-s2.0-84981340935 (Scopus ID)978-1-4503-3727-4 (ISBN)
Conference
16th ACM/IFIP/USENIX Middleware Conference, Middleware Industry 2015, Vancouver, Canada, 7 December 2015 through 11 December 2015
Available from: 2016-01-26 Created: 2016-01-26 Last updated: 2018-06-07Bibliographically approved
3. Performance Anomaly Detection using Datacenter Landscape Graphs
Open this publication in new window or tab >>Performance Anomaly Detection using Datacenter Landscape Graphs
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
National Category
Computer Systems
Research subject
Computing Science
Identifiers
urn:nbn:se:umu:diva-142023 (URN)
Conference
2nd IEEE International Conference on Big Data, Cloud Computing, and Data Science (BCD 2017)
Projects
Cloud Control (C0590801)
Funder
Swedish Research Council, C0590801
Available from: 2017-11-17 Created: 2017-11-17 Last updated: 2018-06-09
4. Adaptive Anomaly Detection in Performance Metric Streams
Open this publication in new window or tab >>Adaptive Anomaly Detection in Performance Metric Streams
2018 (English)In: IEEE Transactions on Network and Service Management, ISSN 1932-4537, E-ISSN 1932-4537, Vol. 15, no 1, p. 217-231Article in journal (Refereed) Published
Abstract [en]

Continuous detection of performance anomalies such as service degradations has become critical in cloud and Internet services due to impact on quality of service and end-user experience. However, the volume and fast changing behaviour of metric streams have rendered it a challenging task. Many diagnosis frameworks often rely on thresholding with stationarity or normality assumption, or on complex models requiring extensive offline training. Such techniques are known to be prone to spurious false-alarms in online settings as metric streams undergo rapid contextual changes from known baselines. Hence, we propose two unsupervised incremental techniques following a two-step strategy. First, we estimate an underlying temporal property of the stream via adaptive learning and, then we apply statistically robust control charts to recognize deviations. We evaluated our techniques by replaying over 40 time-series streams from the Yahoo! Webscope S5 datasets as well as 4 other traces of real web service QoS and ISP traffic measurements. Our methods achieve high detection accuracy and few false-alarms, and better performance in general compared to an open-source package for time-series anomaly detection.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Performance Monitoring and Measurement, Computer Network Management, Quality of Service, Time Series Analysis, Anomaly Detection, Unsupervised Learning
National Category
Computer Systems
Research subject
Computer Science; Computing Science; Computer Systems
Identifiers
urn:nbn:se:umu:diva-142030 (URN)10.1109/TNSM.2017.2750906 (DOI)000427420100016 ()
Projects
Cloud Control
Funder
Swedish Research Council, C0590801
Available from: 2017-11-17 Created: 2017-11-17 Last updated: 2018-08-07Bibliographically approved
5. A Black-box Approach for Detecting Systems Anomalies in Virtualized Environments
Open this publication in new window or tab >>A Black-box Approach for Detecting Systems Anomalies in Virtualized Environments
2017 (English)In: 2017 IEEE International Conference on Cloud and Autonomic Computing (ICCAC 2017), IEEE, 2017, p. 22-33Conference paper, Published paper (Refereed)
Abstract [en]

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. 

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
Anomaly Detection, Performance Anomaly Detection, Performance Diagnosis, Cloud Computing, Virtualized Services, Unsupervised Learning, Time Series Analysis, Quality of Service
National Category
Computer Systems
Research subject
Computer Systems; Computing Science
Identifiers
urn:nbn:se:umu:diva-142031 (URN)10.1109/ICCAC.2017.10 (DOI)978-1-5386-1939-1 (ISBN)
Conference
2017 IEEE International Conference on Cloud and Autonomic Computing (ICCAC 2017), Tucson, Arizona, USA, 18–22 September 2017
Projects
Cloud Control
Funder
Swedish Research Council, C0590801
Available from: 2017-11-17 Created: 2017-11-17 Last updated: 2018-06-09Bibliographically approved
6. Adaptive Service Performance Control using Cooperative Fuzzy Reinforcement Learning in Virtualized Environments
Open this publication in new window or tab >>Adaptive Service Performance Control using Cooperative Fuzzy Reinforcement Learning in Virtualized Environments
2017 (English)In: 10th IEEE/ACM International Conference on Utility and Cloud Computing, 2017, Dec 5-8, Austin TX, USA, IEEE/ACM , 2017Conference 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
Keywords
Performance control, Resource allocation, Quality of service, Reinforcement learning, Autoscaling, Autonomic computing
National Category
Computer Systems
Research subject
Computer Systems; Computing Science
Identifiers
urn:nbn:se:umu:diva-142032 (URN)10.1145/3147213.3147225 (DOI)978-1-4503-5149-2 (ISBN)
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, C0590801
Available from: 2017-11-17 Created: 2017-11-17 Last updated: 2018-06-09

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