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Divide the task, multiply the outcome: cooperative VM consolidation
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.
2014 (English)In: Proceedings of The 6th IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2014), IEEE conference proceedings, 2014, , 6 p.300-305 p.Conference paper, Published paper (Refereed)
Abstract [en]

Efficient resource utilization is one of the mainconcerns of cloud providers, as it has a direct impact onenergy costs and thus their revenue. Virtual machine (VM)consolidation is one the common techniques, used by infrastruc-ture providers to efficiently utilize their resources. However,when it comes to large-scale infrastructures, consolidationdecisions become computationally complex, since VMs aremulti-dimensional entities with changing demand and unknownlifetime, and users often overestimate their actual demand.These uncertainties urges the system to take consolidationdecisions continuously in a real time manner.In this work, we investigate a decentralized approach forVM consolidation using Peer to Peer (P2P) principles. Weinvestigate the opportunities offered by P2P systems, as scalableand robust management structures, to address VM consol-idation concerns. We present a P2P consolidation protocol,considering the dimensionality of resources and dynamicityof the environment. The protocol benefits from concurrencyand decentralization of control and it uses a dimension awaredecision function for efficient consolidation. We evaluate theprotocol through simulation of 100,000 physical machinesand 200,000 VM requests. Results demonstrate the potentialsand advantages of using a P2P structure to make resourcemanagement decisions in large scale data centers. They showthat the P2P approach is feasible and scalable and producesresource utilization of 75% when the consolidation aim is 90%.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014. , 6 p.300-305 p.
Keyword [en]
Cloud computing, Peer to Peer, Gossip protocols, VM consolidation, Resource management
National Category
Engineering and Technology
Research subject
Computing Science
Identifiers
URN: urn:nbn:se:umu:diva-98898DOI: 10.1109/CloudCom.2014.81OAI: oai:DiVA.org:umu-98898DiVA: diva2:783995
Conference
The 6th IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2014), 15-18 Dec. 2014 Singapore
Available from: 2015-01-28 Created: 2015-01-28 Last updated: 2015-12-15Bibliographically approved
In thesis
1. 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, MinaHernández-Rodriguez, FranciscoElmroth, Erik

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CiteExportLink to record
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Citation style
  • apa
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  • de-DE
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