umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Decentralized cloud datacenter reconsolidation through emergent and topology-aware behavior
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0001-6894-1438
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Distributed Systems)
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2016 (English)In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 56, 51-63 p.Article in journal (Refereed) Published
Abstract [en]

Consolidation of multiple applications on a single Physical Machine (PM) within acloud data center can increase utilization, minimize energy consumption, and reduceoperational costs. However, these benefits comes at the cost of increasing the complex-ity of the scheduling problem.In this paper, we present a topology-aware resource management framework. Aspart of this framework, we introduce a Reconsolidating PlaceMent scheduler (RPM)that provides and maintains durable allocations with low maintenance costs for datacenters with dynamic workloads. We focus on workloads featuring both short-livedbatch jobs and latency-sensitive services such as interactive web applications. Thescheduler assigns resources to Virtual Machines (VMs) and maintains packing effi-ciency while taking into account migration costs, topological constraints, and the riskof resource contention, as well as the variability of the background load and its com-plementarity to the new VM.We evaluate the model by simulating a data center with over 65000 PMs, structuredas a three-level multi-rooted tree topology. We investigate trade-offs between factorsthat affect the durability and operational cost of maintaining a near-optimal packing.The results show that the proposed scheduler can scale to the number of PMs in thesimulation and maintain efficient utilization with low migration costs.

Place, publisher, year, edition, pages
Elsevier, 2016. Vol. 56, 51-63 p.
Keyword [en]
Cloud computing, Peer to Peer, VM consolidation, Resource management
National Category
Computer and Information Science
Research subject
Computer Systems
Identifiers
URN: urn:nbn:se:umu:diva-109131DOI: 10.1016/j.future.2015.09.023ISI: 000368652500004OAI: oai:DiVA.org:umu-109131DiVA: diva2:855198
Available from: 2015-09-19 Created: 2015-09-19 Last updated: 2017-12-04Bibliographically 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

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Sedaghat, MinaElmroth, Erik
By organisation
Department of Computing Science
In the same journal
Future generations computer systems
Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 174 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf