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Performance problem diagnosis in cloud infrastructures
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (Distributed Systems)
2016 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Cloud datacenters comprise hundreds or thousands of disparate application services, each having stringent performance and availability requirements, sharing a finite set of heterogeneous hardware and software resources. The implication of such complex environment is that the occurrence of performance problems, such as slow application response and unplanned downtimes, has become a norm rather than exception resulting in decreased revenue, damaged reputation, and huge human-effort in diagnosis. Though causes can be as varied as application issues (e.g. bugs), machine-level failures (e.g. faulty server), and operator errors (e.g. mis-configurations), recent studies have attributed capacity-related issues, such as resource shortage and contention, as the cause of most performance problems on the Internet today. As cloud datacenters become increasingly autonomous there is need for automated performance diagnosis systems that can adapt their operation to reflect the changing workload and topology in the infrastructure. In particular, such systems should be able to detect anomalous performance events, uncover manifestations of capacity bottlenecks, localize actual root-cause(s), and possibly suggest or actuate corrections.

This thesis investigates approaches for diagnosing performance problems in cloud infrastructures. We present the outcome of an extensive survey of existing research contributions addressing performance diagnosis in diverse systems domains. We also present models and algorithms for detecting anomalies in real-time application performance and identification of anomalous datacenter resources based on operational metrics and spatial dependency across datacenter components. Empirical evaluations of our approaches shows how they can be used to improve end-user experience, service assurance and support root-cause analysis. 

Ort, förlag, år, upplaga, sidor
Umeå: Department of Computing Science, Umeå University , 2016. , s. 28
Serie
Report / UMINF, ISSN 0348-0542 ; 16.14
Nyckelord [en]
Systems Performance, Performance anomalies, Performance bottlenecks, Cloud infrastructures, Cloud Computing, Cloud Services, Cloud Computing Performance, Performance problems, Performance anomaly detection, Performance bottleneck identification, Performance Root-cause Analysis
Nationell ämneskategori
Datorsystem
Forskningsämne
datorteknik; datalogi
Identifikatorer
URN: urn:nbn:se:umu:diva-120287ISBN: 978-91-7601-500-1 (tryckt)OAI: oai:DiVA.org:umu-120287DiVA, id: diva2:928037
Presentation
2016-05-24, N430, Naturvetarhuset, Umeå University, Umeå, 10:00 (Engelska)
Opponent
Handledare
Projekt
Cloud Control (C0590801)
Forskningsfinansiär
Vetenskapsrådet, C0590801Tillgänglig från: 2016-05-23 Skapad: 2016-05-13 Senast uppdaterad: 2018-06-07Bibliografiskt granskad
Delarbeten
1. Performance Anomaly Detection and Bottleneck Identification
Öppna denna publikation i ny flik eller fönster >>Performance Anomaly Detection and Bottleneck Identification
2015 (Engelska)Ingår i: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 48, nr 1, artikel-id 4Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
Association for Computing Machinery (ACM), 2015
Nyckelord
Systems performance, performance anomaly detection, bottleneck detection, performance problem identification
Nationell ämneskategori
Datorsystem
Forskningsämne
datorteknik
Identifikatorer
urn:nbn:se:umu:diva-105991 (URN)10.1145/2791120 (DOI)000363733200004 ()2-s2.0-84938363675 (Scopus ID)
Forskningsfinansiär
Vetenskapsrådet, C0590801
Tillgänglig från: 2015-07-03 Skapad: 2015-07-03 Senast uppdaterad: 2018-06-07Bibliografiskt granskad
2. Apex lake: a framework for enabling smart orchestration
Öppna denna publikation i ny flik eller fönster >>Apex lake: a framework for enabling smart orchestration
Visa övriga...
2015 (Engelska)Ingår i: Proceedings of the Industry Track of the 16th ACM/IFIP/USENIX Middleware Conference, New York, USA: Association for Computing Machinery (ACM), 2015, s. 1-7, artikel-id 1Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
New York, USA: Association for Computing Machinery (ACM), 2015
Nyckelord
Cloud monitoring and orchestration, Resource Management, Datacenter Management, Software-defined Infrastructure
Nationell ämneskategori
Datorsystem
Forskningsämne
datorteknik
Identifikatorer
urn:nbn:se:umu:diva-114696 (URN)10.1145/2830013.2830016 (DOI)2-s2.0-84981340935 (Scopus ID)978-1-4503-3727-4 (ISBN)
Konferens
16th ACM/IFIP/USENIX Middleware Conference, Middleware Industry 2015, Vancouver, Canada, 7 December 2015 through 11 December 2015
Tillgänglig från: 2016-01-26 Skapad: 2016-01-26 Senast uppdaterad: 2018-06-07Bibliografiskt granskad
3. Performance Anomaly Detection using Datacenter Landscape Graphs
Öppna denna publikation i ny flik eller fönster >>Performance Anomaly Detection using Datacenter Landscape Graphs
(Engelska)Manuskript (preprint) (Övrigt vetenskapligt)
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:umu:diva-124577 (URN)
Tillgänglig från: 2016-08-16 Skapad: 2016-08-16 Senast uppdaterad: 2018-06-07

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