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A virtual machine re-packing approach to the horizontal vs. vertical elasticity trade-off for cloud autoscaling
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2013 (English)In: Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference, ACM Press, 2013, Article no. 6- p.Conference paper, Published paper (Refereed)
Abstract [en]

An automated solution to horizontal vs. vertical elasticity problem is central to make cloud autoscalers truly autonomous. Today's cloud autoscalers are typically varying the capacity allocated by increasing and decreasing the number of virtual machines (VMs) of a predefined size (horizontal elasticity), not taking into account that as load varies it may be advantageous not only to vary the number but also the size of VMs (vertical elasticity). We analyze the price/performance effects achieved by different strategies for selecting VM-sizes for handling increasing load and we propose a cost-benefit based approach to determine when to (partly) replace a current set of VMs with a different set. We evaluate our repacking approach in combination with different auto-scaling strategies. Our results show a range of 7% up to 60% cost saving in total resource utilization cost of our sample applications and workloads.

Place, publisher, year, edition, pages
ACM Press, 2013. Article no. 6- p.
Keyword [en]
Cloud computing, Autoscaling, Autonomous computing, Vertical elasticity, Horizontal elasticity
National Category
Computer Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-79781DOI: 10.1145/2494621.2494628ISBN: 978-1-4503-2172-3 (print)OAI: oai:DiVA.org:umu-79781DiVA: diva2:644784
Conference
2013 ACM International Conference on Cloud and Autonomic Computing, CAC 2013, Miami, FL, United States, 5 August 2013 through 9 August 2013
Available from: 2013-09-02 Created: 2013-09-02 Last updated: 2015-12-15Bibliographically approved
In thesis
1. Capacity Management Approaches for Compute Clouds
Open this publication in new window or tab >>Capacity Management Approaches for Compute Clouds
2013 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Cloud computing provides the illusion of a seamless, infinite resource pool with flexibleon-demand accessibility. However, behind this illusion there are thousands ofservers and peta-bytes of storage, running tens of thousands of applications accessedby millions of users. The management of such systems is non-trivial because theyface elastic demand, have heterogeneous resources, must fulfill diverse managementobjectives, and are vast in scale.Autonomic computing techniques can be used to tackle the complex problem ofresource management in cloud data centers by introducing self-managing elementsknown as autonomic managers. Each autonomic manager should be capable of managingitself while simultaneously contributing to the fulfillment of high level systemwideobjectives. A wide range of approaches and mechanisms can be used to defineand design these autonomic managers as well as to organize them and coordinate theiractions in order to achieve specific goals.This thesis investigates autonomic approaches for cloud resource management thataim to optimize the cloud infrastructure layer with respect to various high level objectives.The resource management problem is formulated as a problem of optimizationwith respect to one or more management objectives such as cost, profitability, or datacenter utilization, as well as performance concerns such as response time, quality ofservice, and rejection rates. The aim of the reported investigations is to address theproblems of cost-efficient elastic resource provisioning, unified management of cloudresources, and scalability in cloud resource management. This is achieved by introducingthree new concepts in capacity management: the Repacking, Holistic, and Peerto Peer approaches.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2013. 66 p.
Series
Report / UMINF, ISSN 0348-0542 ; 2013:24
Keyword
Cloud computing, Capacity Management
National Category
Computer Science
Research subject
Computing Science
Identifiers
urn:nbn:se:umu:diva-87242 (URN)978-91-7459-788-2 (ISBN)
Presentation
2013-12-19, Umeå universitet, Umeå, 10:00
Opponent
Supervisors
Available from: 2014-04-03 Created: 2014-03-25 Last updated: 2014-04-03Bibliographically approved
2. Cluster Scheduling and Management for Large-Scale Compute Clouds
Open this publication in new window or tab >>Cluster Scheduling and Management for Large-Scale Compute Clouds
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Cloud computing has become a powerful enabler for many IT services and new technolo-gies. It provides access to an unprecedented amount of resources in a fine-grained andon-demand manner. To deliver such a service, cloud providers should be able to efficientlyand reliably manage their available resources. This becomes a challenge for the manage-ment system as it should handle a large number of heterogeneous resources under diverseworkloads with fluctuations. In addition, it should also satisfy multiple operational require-ments and management objectives in large scale data centers.Autonomic computing techniques can be used to tackle cloud resource managementproblems. An autonomic system comprises of a number of autonomic elements, which arecapable of automatically organizing and managing themselves rather than being managedby external controllers. Therefore, they are well suited for decentralized control, as theydo not rely on a centrally managed state. A decentralized autonomic system benefits fromparallelization of control, faster decisions and better scalability. They are also more reliableas a failure of one will not affect the operation of the others, while there is also a lower riskof having faulty behaviors on all the elements, all at once. All these features are essentialrequirements of an effective cloud resource management.This thesis investigates algorithms, models, and techniques to autonomously managejobs, services, and virtual resources in a cloud data center. We introduce a decentralizedresource management framework, that automates resource allocation optimization and ser-vice consolidation, reliably schedules jobs considering probabilistic failures, and dynam-icly scales and repacks services to achieve cost efficiency.As part of the framework, we introduce a decentralized scheduler that provides andmaintains durable allocations with low maintenance costs for data centers with dynamicworkloads. The scheduler assigns resources in response to virtual machine requests andmaintains the packing efficiency while taking into account migration costs, topologicalconstraints, and the risk of resource contention, as well as fluctuations of the backgroundload.We also introduce a scheduling algorithm that considers probabilistic failures as part ofthe planning for scheduling. The aim of the algorithm is to achieve an overall job reliabil-ity, in presence of correlated failures in a data center. To do so, we study the impacts ofstochastic and correlated failures on job reliability in a virtual data center. We specificallyfocus on correlated failures caused by power outages or failure of network components onjobs running large number of replicas of identical tasks.Additionally, we investigate the trade-offs between vertical and horizontal scaling. Theresult of the investigations is used to introduce a repacking technique to automatically man-age the capacity required by an elastic service. The repacking technique combines thebenefits of both scaling strategies to improve its cost-efficiency.

Abstract [sv]

Datormoln har kommit att bli kraftfulla möjliggörare för många nya IT-tjänster. De ger tillgång till mycket storskaliga datorresurser på ett finkornigt och omedelbart sätt. För att tillhandahålla sådana resurser krävs att de underliggande datorcentren kan hantera sina resurser på ett tillförlitligt och effektivt sätt. Frågan hur man ska designa deras resurshanteringssystem är en stor utmaning då de ska kunna hantera mycket stora mängder heterogena resurser som i sin tur ska klara av vitt skilda typer av belastning, ofta med väldigt stora variationer över tid. Därtill ska de typiskt kunna möta en mängd olika krav och målsättningar för hur resurserna ska nyttjas. Autonomiska system kan med fördel användas för att realisera sådana system. Ett autonomt system innehåller ett antal autonoma element som automatiskt kan organisera och hantera sig själva utan stöd av externa regulatorer. Förmågan att hantera sig själva gör dem mycket lämpliga som komponenter i distribuerade system, vilka i sin tur kan bidra till snabbare beslutsprocesser, bättre skalbarhet och högre feltolerans. Denna avhandling fokuserar på algoritmer, modeller och tekniker för autonom hantering av jobb och virtuella resurser i datacenter. Vi introducerar ett decentraliserat resurshanteringssystem som automatiserar resursallokering och konsolidering, schedulerar jobb tillförlitligt med hänsyn till korrelerade fel, samt skalar resurser dynamiskt för att uppnå kostnadseffektivitet. Som en del av detta ramverk introducerar vi en decentraliserad schedulerare som allokerar resurser med hänsyn till att tagna beslut ska vara bra för lång tid och ge låga resurshanteringskostnader för datacenter med dynamisk belastning. Scheduleraren allokerar virtuella maskiner utifrån aktuell belastning och upprätthåller ett effektivt nyttjande av underliggande servrar genom att ta hänsyn till migrationskostnader, topologiska bivillkor och risk för överutnyttjande. Vi introducerar också en resursallokeringsalgoritm som tar hänsyn till korrelerade fel som ett led i planeringen. Avsikten är att kunna uppnå specificerade tillgänglighetskrav för enskilda tjänster trots uppkomst av korrelerade fel. Vi fokuserar främst på korrelerade fel som härrör från problem med elförsörjning och från felande nätverkskomponenter samt deras påverkan på jobb bestående av många identiska del-jobb. Slutligen studerar vi även hur man bäst ska kombinera horisontell och vertikal skalning av resurser. Resultatet är en process som ökar kostnadseffektivitet genom att kombinera de två metoderna och därtill emellanåt förändra fördelning av storlekar på virtuella maskiner.

Place, publisher, year, edition, pages
Umeå University, 2015. 24 p.
Series
UMINF, ISSN 0348-0542 ; 15.19
Keyword
Cloud computing, Scheduling, Resource Management
National Category
Computer Science
Identifiers
urn:nbn:se:umu:diva-112467 (URN)978-91-7601-389-2 (ISBN)
Public defence
2015-01-21, Hörsal A, Samhällsvetarhuset, Umeå, 10:15 (English)
Opponent
Supervisors
Available from: 2015-12-16 Created: 2015-12-08 Last updated: 2016-04-07Bibliographically approved

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Sedaghat, MinaHernandez, FranciscoElmroth, Erik

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