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An adaptive hybrid elasticity controller for cloud infrastructures
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (UMIT)
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (UMIT)
2012 (Engelska)Ingår i: 2012 IEEE Network operations and managent symposium (NOMS), IEEE Communications Society, 2012, s. 204-212Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Cloud elasticity is the ability of the cloud infrastructure to rapidly change the amount of resources allocated to a service in order to meet the actual varying demands on the service while enforcing SLAs. In this paper, we focus on horizontal elasticity, the ability of the infrastructure to add or remove virtual machines allocated to a service deployed in the cloud. We model a cloud service using queuing theory. Using that model we build two adaptive proactive controllers that estimate the future load on a service. We explore the different possible scenarios for deploying a proactive elasticity controller coupled with a reactive elasticity controller in the cloud. Using simulation with workload traces from the FIFA world-cup web servers, we show that a hybrid controller that incorporates a reactive controller for scale up coupled with our proactive controllers for scale down decisions reduces SLA violations by a factor of 2 to 10 compared to a regression based controller or a completely reactive controller.

Ort, förlag, år, upplaga, sidor
IEEE Communications Society, 2012. s. 204-212
Serie
IEEE IFIP Network Operations and Management Symposium, ISSN 1542-1201
Nationell ämneskategori
Data- och informationsvetenskap
Identifikatorer
URN: urn:nbn:se:umu:diva-51044DOI: 10.1109/NOMS.2012.6211900ISI: 000309517000025ISBN: 978-1-4673-0268-5 (tryckt)OAI: oai:DiVA.org:umu-51044DiVA, id: diva2:474360
Konferens
13th IEEE/IFIP Network Operations and Management Symposium, 16-20 April 2012, Maui, Hawaii, USA
Tillgänglig från: 2012-01-09 Skapad: 2012-01-09 Senast uppdaterad: 2018-06-08Bibliografiskt granskad
Ingår i avhandling
1. Capacity Scaling for Elastic Compute Clouds
Öppna denna publikation i ny flik eller fönster >>Capacity Scaling for Elastic Compute Clouds
2013 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Umeå: Umeå universitet, 2013. s. 22
Serie
Report / UMINF, ISSN 0348-0542 ; 2013:14
Nationell ämneskategori
Datorsystem
Forskningsämne
datalogi; datorteknik
Identifikatorer
urn:nbn:se:umu:diva-87238 (URN)978-91-7459-688-5 (ISBN)
Presentation
2013-06-10, Umeå universitet, Umeå, 11:00
Opponent
Handledare
Forskningsfinansiär
EU, FP7, Sjunde ramprogrammetVetenskapsrådeteSSENCE - An eScience Collaboration
Anmärkning

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

Tillgänglig från: 2014-04-03 Skapad: 2014-03-25 Senast uppdaterad: 2018-06-08Bibliografiskt granskad
2. Workload characterization, controller design and performance evaluation for cloud capacity autoscaling
Öppna denna publikation i ny flik eller fönster >>Workload characterization, controller design and performance evaluation for cloud capacity autoscaling
2015 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

This thesis studies cloud capacity auto-scaling, or how to provision and release re-sources to a service running in the cloud based on its actual demand using an auto-matic controller. As the performance of server systems depends on the system design,the system implementation, and the workloads the system is subjected to, we focuson these aspects with respect to designing auto-scaling algorithms. Towards this goal,we design and implement two auto-scaling algorithms for cloud infrastructures. Thealgorithms predict the future load for an application running in the cloud. We discussthe different approaches to designing an auto-scaler combining reactive and proactivecontrol methods, and to be able to handle long running requests, e.g., tasks runningfor longer than the actuation interval, in a cloud. We compare the performance ofour algorithms with state-of-the-art auto-scalers and evaluate the controllers’ perfor-mance with a set of workloads. As any controller is designed with an assumptionon the operating conditions and system dynamics, the performance of an auto-scalervaries with different workloads.In order to better understand the workload dynamics and evolution, we analyze a6-years long workload trace of the sixth most popular Internet website. In addition,we analyze a workload from one of the largest Video-on-Demand streaming servicesin Sweden. We discuss the popularity of objects served by the two services, the spikesin the two workloads, and the invariants in the workloads. We also introduce, a mea-sure for the disorder in a workload, i.e., the amount of burstiness. The measure isbased on Sample Entropy, an empirical statistic used in biomedical signal processingto characterize biomedical signals. The introduced measure can be used to charac-terize the workloads based on their burstiness profiles. We compare our introducedmeasure with the literature on quantifying burstiness in a server workload, and showthe advantages of our introduced measure.To better understand the tradeoffs between using different auto-scalers with differ-ent workloads, we design a framework to compare auto-scalers and give probabilisticguarantees on the performance in worst-case scenarios. Using different evaluation cri-teria and more than 700 workload traces, we compare six state-of-the-art auto-scalersthat we believe represent the development of the field in the past 8 years. Knowingthat the auto-scalers’ performance depends on the workloads, 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.

Ort, förlag, år, upplaga, sidor
Umeå: Umeå University, 2015. s. 16
Serie
Report / UMINF, ISSN 0348-0542 ; 15.09
Nyckelord
cloud computing, autoscaling, workloads, performance modeling, controller design
Nationell ämneskategori
Datorsystem
Identifikatorer
urn:nbn:se:umu:diva-108398 (URN)978-91-7601-330-4 (ISBN)
Disputation
2015-10-02, N360, Naturveterhuset Building, Umeå University, Umeå, 14:00 (Engelska)
Opponent
Handledare
Forskningsfinansiär
EU, Europeiska forskningsrådetVetenskapsrådet
Tillgänglig från: 2015-09-11 Skapad: 2015-09-10 Senast uppdaterad: 2018-06-07Bibliografiskt granskad

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Ali-Eldin, AhmedTordsson, JohanElmroth, Erik

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