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Workload characterization, controller design and performance evaluation for cloud capacity autoscaling
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Distributed Systems)
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 [en]
cloud computing, autoscaling, workloads, performance modeling, controller design
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:umu:diva-108398ISBN: 978-91-7601-330-4 (print)OAI: oai:DiVA.org:umu-108398DiVA: diva2:852794
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
List of papers
1. An adaptive hybrid elasticity controller for cloud infrastructures
Open this publication in new window or tab >>An adaptive hybrid elasticity controller for cloud infrastructures
2012 (English)In: 2012 IEEE Network operations and managent symposium (NOMS), IEEE Communications Society, 2012, 204-212 p.Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
IEEE Communications Society, 2012
Series
IEEE IFIP Network Operations and Management Symposium, ISSN 1542-1201
National Category
Computer and Information Science
Identifiers
urn:nbn:se:umu:diva-51044 (URN)10.1109/NOMS.2012.6211900 (DOI)000309517000025 ()978-1-4673-0268-5 (ISBN)
Conference
13th IEEE/IFIP Network Operations and Management Symposium, 16-20 April 2012, Maui, Hawaii, USA
Available from: 2012-01-09 Created: 2012-01-09 Last updated: 2015-09-11Bibliographically approved
2. Efficient provisioning of bursty scientific workloads on the cloud using adaptive elasticity control
Open this publication in new window or tab >>Efficient provisioning of bursty scientific workloads on the cloud using adaptive elasticity control
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
Keyword
Cloud Computing, Elasticity, Proportional Control
National Category
Computer Science
Research subject
Computer Science; Signal Processing
Identifiers
urn:nbn:se:umu:diva-54015 (URN)10.1145/2287036.2287044 (DOI)978-1-4503-1340-7 E-ISBN (ISBN)145031340X Print (ISBN)
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
3. How will your workload look like in 6 years?: Analyzing Wikimedia's workload
Open this publication in new window or tab >>How will your workload look like in 6 years?: Analyzing Wikimedia's workload
Show others...
2014 (English)In: Proceedings of the 2014 IEEE International Conference on Cloud Engineering (IC2E 2014) / [ed] Lisa O’Conner, IEEE Computer Society, 2014, 349-354 p.Conference paper, Published paper (Refereed)
Abstract [en]

Accurate understanding of workloads is key to efficient cloud resource management as well as to the design of large-scale applications. We analyze and model the workload of Wikipedia, one of the world's largest web sites. With descriptive statistics, time-series analysis, and polynomial splines, we study the trend and seasonality of the workload, its evolution over the years, and also investigate patterns in page popularity. Our results indicate that the workload is highly predictable with a strong seasonality. Our short term prediction algorithm is able to predict the workload with a Mean Absolute Percentage Error of around 2%.

Place, publisher, year, edition, pages
IEEE Computer Society, 2014
Series
IEEE, ISSN 2373-3845
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Computer Systems
Research subject
Computing Science
Identifiers
urn:nbn:se:umu:diva-87235 (URN)10.1109/IC2E.2014.50 (DOI)000361018600043 ()978-1-4799-3766-0 (ISBN)
Conference
IC2E 2014, IEEE International Conference on Cloud Engineering, Boston, Massachusetts, 11-14 March 2014
Funder
Swedish Research Council, C0590801eSSENCE - An eScience Collaboration
Available from: 2014-03-25 Created: 2014-03-25 Last updated: 2015-10-06Bibliographically approved
4. Measuring cloud workload burstiness
Open this publication in new window or tab >>Measuring cloud workload burstiness
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2014 (English)In: 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC), IEEE conference proceedings, 2014, 566-572 p.Conference paper, Published paper (Refereed)
Abstract [en]

Workload burstiness and spikes are among the main reasons for service disruptions and decrease in the Quality-of-Service (QoS) of online services. They are hurdles that complicate autonomic resource management of datacenters. In this paper, we review the state-of-the-art in online identification of workload spikes and quantifying burstiness. The applicability of some of the proposed techniques is examined for Cloud systems where various workloads are co-hosted on the same platform. We discuss Sample Entropy (SampEn), a measure used in biomedical signal analysis, as a potential measure for burstiness. A modification to the original measure is introduced to make it more suitable for Cloud workloads.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014
National Category
Computer Systems
Identifiers
urn:nbn:se:umu:diva-108397 (URN)10.1109/UCC.2014.87 (DOI)000380558700080 ()978-1-4799-7881-6 (ISBN)
Conference
7th International Conference on Utility and Cloud Computing (UCC), 8-11 December 2014, London, England, United Kingdom
Funder
EU, European Research CouncilSwedish Research Council
Available from: 2015-09-10 Created: 2015-09-10 Last updated: 2017-01-17Bibliographically approved
5. Analysis and characterization of a Video-on-Demand service workload
Open this publication in new window or tab >>Analysis and characterization of a Video-on-Demand service workload
2015 (English)In: Proceedings of the 6th ACM Multimedia Systems Conference, MMSys 2015, ACM Digital Library, 2015, 189-200 p.Conference paper, Published paper (Refereed)
Abstract [en]

Video-on-Demand (VoD) and video sharing services accountfor a large percentage of the total downstream Internet traf-fic. In order to provide a better understanding of the loadon these services, we analyze and model a workload tracefrom a VoD service provided by a major Swedish TV broad-caster. The trace contains over half a million requests gener-ated by more than 20000 unique users. Among other things,we study the request arrival rate, the inter-arrival time, thespikes in the workload, the video popularity distribution, thestreaming bit-rate distribution and the video duration distri-bution. Our results show that the user and the session ar-rival rates for the TV4 workload does not follow a Poissonprocess. The arrival rate distribution is modeled using a log-normal distribution while the inter-arrival time distributionis modeled using a stretched exponential distribution. Weobserve the “impatient user” behavior where users abandonstreaming sessions after minutes or even seconds of startingthem. Both very popular videos and non-popular videos areparticularly affected by impatient users. We investigate ifthis behavior is an invariant for VoD workloads.

Place, publisher, year, edition, pages
ACM Digital Library, 2015
National Category
Engineering and Technology Computer Systems
Research subject
Computing Science
Identifiers
urn:nbn:se:umu:diva-108393 (URN)10.1145/2713168.2713183 (DOI)2-s2.0-84942543893 (Scopus ID)978-1-4503-3351-1 (ISBN)
Conference
6th ACM Multimedia Systems Conference, MMSys 2015; Portland; United States; 18 March 2015 through 20 March 2015; Code 113421
Projects
CACTOSCloud Control
Funder
EU, European Research CouncilSwedish Research Council
Available from: 2015-09-10 Created: 2015-09-10 Last updated: 2016-06-07Bibliographically approved
6. PEAS: A Performance Evaluation framework for Auto-Scaling strategies in cloud applications
Open this publication in new window or tab >>PEAS: A Performance Evaluation framework for Auto-Scaling strategies in cloud applications
Show others...
2015 (English)Manuscript (preprint) (Other academic)
Abstract [en]

Numerous auto-scaling strategies have been proposed in the last few years for improving various Quality of Service (QoS)indicators of cloud applications, e.g., response time and throughput, by adapting the amount of resources assigned to theapplication to meet the workload demand. However, the evaluation of a proposed auto-scaler is usually achieved throughexperiments under specific conditions, and seldom includes extensive testing to account for uncertainties in the workloads, andunexpected behaviors of the system. These tests by no means can provide guarantees about the behavior of the system in generalconditions. In this paper, we present PEAS, a Performance Evaluation framework for Auto-Scaling strategies in the presenceof uncertainties. The evaluation is formulated as a chance constrained optimization problem, which is solved using scenariotheory. The adoption of such a technique allows one to give probabilistic guarantees of the obtainable performance. Six differentauto-scaling strategies have been selected from the literature for extensive test evaluation, and compared using the proposedframework. We build a discrete event simulator and parameterize it based on real experiments. Using the simulator, each auto-scaler’s performance is evaluated using 796 distinct real workload traces from projects hosted on the Wikimedia foundations’servers, and their performance is compared using PEAS. The evaluation is carried out using different performance metrics,highlighting the flexibility of the framework, while providing probabilistic bounds on the evaluation and the performance of thealgorithms. Our results highlight the problem of generalizing the conclusions of the original published studies and show thatbased on the evaluation criteria, a controller can be shown to be better than other controllers.

National Category
Computer Systems
Identifiers
urn:nbn:se:umu:diva-108394 (URN)
Funder
Swedish Research CouncilEU, European Research Council
Note

Submitted

Available from: 2015-09-10 Created: 2015-09-10 Last updated: 2015-09-24
7. WAC: A Workload analysis and classification tool for automatic selection of cloud auto-scaling methods
Open this publication in new window or tab >>WAC: A Workload analysis and classification tool for automatic selection of cloud auto-scaling methods
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Autoscaling algorithms for elastic cloud infrastructures dynami-cally change the amount of resources allocated to a service ac-cording to the current and predicted future load. Since there areno perfect predictors, no single elasticity algorithm is suitable foraccurate predictions of all workloads. To improve the quality ofworkload predictions and increase the Quality-of-Service (QoS)guarantees of a cloud service, multiple autoscalers suitable for dif-ferent workload classes need to be used. In this work, we intro-duce WAC, a Workload Analysis and Classification tool that as-signs workloads to the most suitable elasticity autoscaler out of aset of pre-deployed autoscalers. The workload assignment is basedon the workload characteristics and a set of user-defined Business-Level-Objectives (BLO). We describe the tool design and its maincomponents. We implement WAC and evaluate its precision us-ing various workloads, BLO combinations and state-of-the-art au-toscalers. Our experiments show that, when the classifier is tunedcarefully, WAC assigns between 87% and 98.3% of the workloadsto the most suitable elasticity autoscaler.

National Category
Computer Systems
Identifiers
urn:nbn:se:umu:diva-108396 (URN)
Funder
Swedish Research CouncilEU, European Research Council
Available from: 2015-09-10 Created: 2015-09-10 Last updated: 2015-09-24

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