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Apex Lake: A Framework for Enabling Smart Orchestration
Intel Labs Europe, Intel Ireland. (Cloud Services Labs)
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Distributed Systems)
Intel Labs Europe, Intel Ireland. (Cloud Services Labs)
Intel Labs Europe, Intel Ireland. (Cloud Services Labs)
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2015 (English)In: Proceedings of the Industrial Track of the 16th International Middleware Conference, New York, USA: Association for Computing Machinery (ACM), 2015Conference 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.
Keyword [en]
Cloud monitoring and orchestration, Resource Management, Datacenter Management, Software-defined Infrastructure
National Category
Computer Systems
Research subject
Computer Systems
Identifiers
URN: urn:nbn:se:umu:diva-114696DOI: 10.1145/2830013.2830016ISBN: 978-1-4503-3727-4OAI: oai:DiVA.org:umu-114696DiVA: diva2:897671
Conference
16th International ACM/IFIP/USENIX Middleware Conference
Available from: 2016-01-26 Created: 2016-01-26 Last updated: 2016-08-14
In thesis
1. Performance problem diagnosis in cloud infrastructures
Open this publication in new window or tab >>Performance problem diagnosis in cloud infrastructures
2016 (English)Licentiate thesis, comprehensive summary (Other academic)
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. 

Place, publisher, year, edition, pages
Umeå: Department of Computing Science, Umeå University, 2016. 28 p.
Series
Report / UMINF, ISSN 0348-0542 ; 16.14
Keyword
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
National Category
Computer Systems
Research subject
Computer Systems; Computer Science
Identifiers
urn:nbn:se:umu:diva-120287 (URN)978-91-7601-500-1 (ISBN)
Presentation
2016-05-24, N430, Naturvetarhuset, Umeå University, Umeå, 10:00 (English)
Opponent
Supervisors
Projects
Cloud Control (C0590801)
Funder
Swedish Research Council, C0590801
Available from: 2016-05-23 Created: 2016-05-13 Last updated: 2016-08-23Bibliographically approved

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Ibidunmoye, OlumuyiwaHernández-Rodriguez, FranciscoElmroth, Erik
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