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Efficient provisioning of bursty scientific workloads on the cloud using adaptive elasticity control
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Dept. of Electrical and Information Technology, Lund University, Lund, Sweden.
Umeå University, Faculty of Science and Technology, Department of Computing Science. (UMIT Research Lab)
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2012 (English)In: Proceedings of the 3rd workshop on Scientific Cloud Computing Date, Association for Computing Machinery (ACM), 2012, 31-40 p.Conference paper, Published paper (Refereed)
Abstract [en]

Elasticity is the ability of a cloud infrastructure to dynamically change theamount of resources allocated to a running service as load changes. We build anautonomous elasticity controller that changes the number of virtual machinesallocated to a service based on both monitored load changes and predictions offuture load. The cloud infrastructure is modeled as a G/G/N queue. This modelis used to construct a hybrid reactive-adaptive controller that quickly reactsto sudden load changes, prevents premature release of resources, takes intoaccount the heterogeneity of the workload, and avoids oscillations. Using simulations with Web and cluster workload traces, we show that our proposed controller lowers the number of delayed requests by a factor of 70 for the Web traces and 3 for the cluster traces when compared to a reactive controller. Ourcontroller also decreases the average number of queued requests by a factor of 3 for both traces, and reduces oscillations by a factor of 7 for the Web traces and 3 for the cluster traces. This comes at the expense of between 20% and 30% over-provisioning, as compared to a few percent for the reactive controller.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2012. 31-40 p.
Keyword [en]
Cloud Computing, Elasticity, Proportional Control
National Category
Computer Science
Research subject
Computer Science; Signal Processing
Identifiers
URN: urn:nbn:se:umu:diva-54015DOI: 10.1145/2287036.2287044Libris ID: 13861173ISBN: 978-1-4503-1340-7 E-ISBN (print)ISBN: 145031340X Print (print)OAI: oai:DiVA.org:umu-54015DiVA: diva2:514975
Conference
Third workshop on scientific cloud computing, ScienceCloud 2012, June 18th 2012, Delft, The Netherlands
Projects
OPTIMIS
Available from: 2012-04-12 Created: 2012-04-11 Last updated: 2015-09-11Bibliographically 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
2. Workload characterization, controller design and performance evaluation for cloud capacity autoscaling
Open this publication in new window or tab >>Workload characterization, controller design and performance evaluation for cloud capacity autoscaling
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2015. 16 p.
Series
Report / UMINF, ISSN 0348-0542 ; 15.09
Keyword
cloud computing, autoscaling, workloads, performance modeling, controller design
National Category
Computer Systems
Identifiers
urn:nbn:se:umu:diva-108398 (URN)978-91-7601-330-4 (ISBN)
Public defence
2015-10-02, N360, Naturveterhuset Building, Umeå University, Umeå, 14:00 (English)
Opponent
Supervisors
Funder
EU, European Research CouncilSwedish Research Council
Available from: 2015-09-11 Created: 2015-09-10 Last updated: 2015-10-07Bibliographically approved

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Citation style
  • apa
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