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Workload Classification for Efficient Auto-Scaling of Cloud Resources
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Cloud and Grid Computing)
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Department of Electrical and Information Technology, Lund University, Lund, Sweden.
2013 (English)Manuscript (preprint) (Other academic)
Abstract [en]

Elasticity algorithms for cloud infrastructures dynamically change the amount of resources allocated to a running service according to the current and predicted future load. Since there is no perfect predictor, and since different applications’ workloads have different characteristics, no single elasticity algorithm is suitable for future predictions for all workloads. In this work, we introduceWAC, aWorkload Analysis and Classification tool that analyzes workloads and assigns them to the most suitable elasticity controllers based on the workloads’ characteristics and a set of business level objectives.

WAC has two main components, the analyzer and the classifier. The analyzer analyzes workloads to extract some of the features used by the classifier, namely, workloads’ autocorrelations and sample entropies which measure the periodicity and the burstiness of the workloads respectively. These two features are used with the business level objectives by the clas-sifier as the features used to assign workloads to elasticity controllers. We start by analyzing 14 real workloads available from different applications. In addition, a set of 55 workloads is generated to test WAC on more workload configurations. We implement four state of the art elasticity algorithms. The controllers are the classes to which the classifier assigns workloads. We use a K nearest neighbors classifier and experiment with different workload combinations as training and test sets. Our experi-ments show that, when the classifier is tuned carefully, WAC correctly classifies between 92% and 98.3% of the workloads to the most suitable elasticity controller.

Place, publisher, year, edition, pages
2013. , 36 p.
National Category
Computer Systems
Research subject
Computing Science
Identifiers
URN: urn:nbn:se:umu:diva-87231OAI: oai:DiVA.org:umu-87231DiVA: diva2:707711
Projects
Cloud Control
Funder
Swedish Research Council
Note

May 21, 2013.

Available from: 2014-03-25 Created: 2014-03-25 Last updated: 2014-04-03Bibliographically approved
In thesis
1. Capacity Scaling for Elastic Compute Clouds
Open this publication in new window or tab >>Capacity Scaling for Elastic Compute Clouds
2013 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

AbstractCloud computing is a computing model that allows better management, higher utiliza-tion and reduced operating costs for datacenters while providing on demand resourceprovisioning for different customers. Data centers are often enormous in size andcomplexity. In order to fully realize the cloud computing model, efficient cloud man-agement software systems that can deal with the datacenter size and complexity needto be designed and built.This thesis studies automated cloud elasticity management, one of the main andcrucial datacenter management capabilities. Elasticity can be defined as the abilityof cloud infrastructures to rapidly change the amount of resources allocated to anapplication in the cloud according to its demand. This work introduces algorithms,techniques and tools that a cloud provider can use to automate dynamic resource pro-visioning allowing the provider to better manage the datacenter resources. We designtwo automated elasticity algorithms for cloud infrastructures that predict the futureload for an application running on the cloud. It is assumed that a request is either ser-viced or dropped after one time unit, that all requests are homogeneous and that it takesone time unit to add or remove resources. We discuss the different design approachesfor elasticity controllers and evaluate our algorithms using real workload traces. Wecompare the performance of our algorithms with a state-of-the-art controller. We ex-tend on the design of the best performing controller out of our two controllers anddrop the assumptions made during the first design. The controller is evaluated with aset of different real workloads.All controllers are designed using certain assumptions on the underlying systemmodel and operating conditions. This limits a controller’s performance if the modelor operating conditions change. With this as a starting point, we design a workloadanalysis and classification tool that assigns a workload to its most suitable elasticitycontroller out of a set of implemented controllers. The tool has two main components,an analyzer and a classifier. The analyzer analyzes a workload and feeds the analysisresults to the classifier. The classifier assigns a workload to the most suitable elasticitycontroller based on the workload characteristics and a set of predefined business levelobjectives. The tool is evaluated with a set of collected real workloads and a set ofgenerated synthetic workloads. Our evaluation results shows that the tool can help acloud provider to improve the QoS provided to the customers.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2013. 22 p.
Series
Report / UMINF, ISSN 0348-0542 ; 2013:14
National Category
Computer Systems
Research subject
Computer Science; Computer Systems
Identifiers
urn:nbn:se:umu:diva-87238 (URN)978-91-7459-688-5 (ISBN)
Presentation
2013-06-10, Umeå universitet, Umeå, 11:00
Opponent
Supervisors
Funder
EU, FP7, Seventh Framework ProgrammeSwedish Research CouncileSSENCE - An eScience Collaboration
Note

Enligt Libris är författarnamnet: Ahmed Aleyeldin (Ali-Eldin) Hassan.

Available from: 2014-04-03 Created: 2014-03-25 Last updated: 2014-04-03Bibliographically approved

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Citation style
  • apa
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