umu.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Continuous Datacenter Consolidation
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (UMIT)
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (UMIT)
Visa övriga samt affilieringar
2014 (Engelska)Rapport (Refereegranskat)
Abstract [en]

Efficient mapping of Virtual Machines (VMs) onto physical servers is a key problem for cloud infrastructure providers as hardware utilization directly im- pacts revenue. Today, this mapping is commonly only performed when new VMs are created, but as VM workloads fluctuate and server availability varies, any ini- tial mapping is bound to become suboptimal over time. We introduce a set of heuristic methods for continuous optimization of the VM-to-server mapping based on combina- tions of fundamental management actions, namely suspending and resuming physical machines, migrating VMs, and suspending and resuming VMs. Using these methods cloud infrastructure providers can continuously optimize their server resources regard- less of the predictability of the workload. To verify that our approach is applicable in real-world scenarios, we build a proof-of-concept datacenter management system that implements the proposed algorithms. The feasibility of our approach is evaluated through a combination of simulations and real experiments where our system provi- sions a workload of benchmark applications. Our results indicate that the proposed algorithms are feasible, that the combined management approach achieves the best results, and that the VM suspend and resume mechanism has the largest impact. 

Ort, förlag, år, upplaga, sidor
Umeå: Umeå universitet , 2014. , s. 12
Serie
Report / UMINF, ISSN 0348-0542 ; 2014:08
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:umu:diva-87385OAI: oai:DiVA.org:umu-87385DiVA, id: diva2:709112
Tillgänglig från: 2014-03-31 Skapad: 2014-03-31 Senast uppdaterad: 2018-06-08Bibliografiskt granskad
Ingår i avhandling
1. Algorithms and Systems for Virtual Machine Scheduling in Cloud Infrastructures
Öppna denna publikation i ny flik eller fönster >>Algorithms and Systems for Virtual Machine Scheduling in Cloud Infrastructures
2014 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

With the emergence of cloud computing, computing resources (i.e., networks, servers, storage, applications, etc.) are provisioned as metered on-demand services over net- works, and can be rapidly allocated and released with minimal management effort. In the cloud computing paradigm, the virtual machine (VM) is one of the most com- monly used resource units in which business services are encapsulated. VM schedul- ing optimization, i.e., finding optimal placement schemes for VMs and reconfigu- rations according to the changing conditions, becomes challenging issues for cloud infrastructure providers and their customers.

The thesis investigates the VM scheduling problem in two scenarios: (i) single- cloud environments where VMs are scheduled within a cloud aiming at improving criteria such as load balancing, carbon footprint, utilization, and revenue, and (ii) multi-cloud scenarios where a cloud user (which could be the owner of the VMs or a cloud infrastructure provider) schedules VMs across multiple cloud providers, target- ing optimization for investment cost, service availability, etc. For single-cloud scenar- ios, taking load balancing as the objective, an approach to optimal VM placement for predictable and time-constrained peak loads is presented. In addition, we also present a set of heuristic methods based on fundamental management actions (namely, sus- pend and resume physical machines, VM migration, and suspend and resume VMs), continuously optimizing the profit for the cloud infrastructure provider regardless of the predictability of the workload. For multi-cloud scenarios, we identify key re- quirements for service deployment in a range of common cloud scenarios (including private clouds, bursted clouds, federated clouds, multi-clouds, and cloud brokering), and present a general architecture to meet these requirements. Based on this architec- ture, a set of placement algorithms tuned for cost optimization under dynamic pricing schemes are evaluated. By explicitly specifying service structure, component relation- ships, and placement constraints, a mechanism is introduced to enable service owners the ability to influence placement. In addition, we also study how dynamic cloud scheduling using VM migration can be modeled using a linear integer programming approach.

The primary contribution of this thesis is the development and evaluation of al- gorithms (ranging from combinatorial optimization formulations to simple heuristic algorithms) for VM scheduling in cloud infrastructures. In addition to scientific pub- lications, this work also contributes software tools (in the OPTIMIS project funded by the European Commissions Seventh Framework Programme) that demonstrate the feasibility and characteristics of the approaches presented. 

Abstract [sv]

I datormoln tillhandahålls datorresurser (dvs., nätverk, servrar, lagring, applikationer,

etc.) som tjänster åtkomliga via Internet. Resurserna, som t.ex. virtuella maskiner (VMs), kan snabbt och enkelt allokeras och frigöras alltefter behov. De potentiellt snabba förändringarna i hur många och hur stora VMs som behövs leder till utmanade schedulerings- och konfigureringsproblem. Scheduleringsproblemen uppstår både för infrastrukturleverantörer som behöver välja vilka servrar olika VMs ska placeras på inom ett moln och deras kunder som behöver välja vilka moln VMs ska placeras på.

Avhandlingen fokuserar på VM-scheduleringsproblem i dessa två scenarier, dvs (i) enskilda moln där VMs ska scheduleras för att optimera lastbalans, energiåtgång, resursnyttjande och ekonomi och (ii) situationer där en molnanvändare ska välja ett eller flera moln för att placera VMs för att optimera t.ex. kostnad, prestanda och tillgänglighet för den applikation som nyttjar resurserna. För det förstnämnda scenar- iot presenterar avhandlingen en scheduleringsmetod som utifrån förutsägbara belast- ningsvariationer optimerar lastbalansen mellan de fysiska datorresurserna. Därtill pre- senteras en uppsättning heuristiska metoder, baserade på fundamentala resurshanter- ingsåtgärder, fö att kontinuerligt optimera den ekonomiska vinsten för en molnlever- antör, utan krav på lastvariationernas förutsägbarhet.

För fallet med flera moln identifierar vi viktiga krav för hur resurshanteringstjänster ska konstrueras för att fungera väl i en rad konceptuellt olika fler-moln-scenarier. Utifrån dessa krav definierar vi också en generell arkitektur som kan anpassas till dessa scenarier. Baserat pp vår arkitektur utvecklar och utvärderar vi en uppsättning algoritmer för VM-schedulering avsedda att minimera kostnader för användning av molninfrastruktur med dynamisk prissättning. Användaren ges genom ny funktionalitet möjlighet att explicit specificera relationer mellan de VMs som allokeras och andra bivillkor för hur de ska placeras. Vi demonstrerar också hur linjär heltals- programmering kan användas för att optimera detta scheduleringsproblem.

Avhandlingens främsta bidrag är utveckling och utvärdering av nya metoder för VM-schedulering i datormoln, med lösningar som inkluderar såväl kombinatorisk op- timering som heuristiska metoder. Utöver vetenskapliga publikationer bidrar arbetet även med programvaror för VM-schedulering, utvecklade inom ramen för projektet OPTIMIS som finansierats av EU-kommissionens sjunde ramprogram.

metoder för VM-schedulering i datormoln, med lösningar som inkluderar såväl kombinatorisk op- timering som heuristiska metoder. Utöver vetenskapliga publikationer bidrar arbetet även med programvaror för VM-schedulering, utvecklade inom ramen för projektet OPTIMIS som finansierats av EU-kommissionens sjunde ramprogram.

Ort, förlag, år, upplaga, sidor
Umeå: Umeå̊ Universitet, 2014. s. 33
Serie
Report / UMINF, ISSN 0348-0542 ; 2014:06
Nyckelord
cloud computing, virtual machine, scheduling, systems, algorithms
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:umu:diva-87310 (URN)978-91-7601-019-8 (ISBN)
Disputation
2014-04-25, MIT-Huset, MA121, Umeå Universitet, Umeå, 10:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2014-04-04 Skapad: 2014-03-29 Senast uppdaterad: 2018-06-08Bibliografiskt granskad
2. Dynamic Cloud Resource Management: Scheduling, Migration and Server Disaggregation
Öppna denna publikation i ny flik eller fönster >>Dynamic Cloud Resource Management: Scheduling, Migration and Server Disaggregation
2014 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

A key aspect of cloud computing is the promise of infinite, scalable resources, and that cloud services should scale up and down on demand. This thesis investigates methods for dynamic resource allocation and management of services in cloud datacenters, introducing new approaches as well as improvements to established technologies.Virtualization is a key technology for cloud computing as it allows several operating system instances to run on the same Physical Machine, PM, and cloud services normally consists of a number of Virtual Machines, VMs, that are hosted on PMs. In this thesis, a novel virtualization approach is presented. Instead of running each PM isolated, resources from multiple PMs in the datacenter are disaggregated and exposed to the VMs as pools of CPU, I/O and memory resources. VMs are provisioned by using the right amount of resources from each pool, thereby enabling both larger VMs than any single PM can host as well as VMs with tailor-made specifications for their application. Another important aspect of virtualization is live migration of VMs, which is the concept moving VMs between PMs without interruption in service. Live migration allows for better PM utilization and is also useful for administrative purposes. In the thesis, two improvements to the standard live migration algorithm are presented, delta compression and page transfer reordering. The improvements can reduce migration downtime, i.e., the time that the VM is unavailable, as well as the total migration time. Postcopy migration, where the VM is resumed on the destination before the memory content is transferred is also studied. Both userspace and in-kernel postcopy algorithms are evaluated in an in-depth study of live migration principles and performance.Efficient mapping of VMs onto PMs is a key problem for cloud providers as PM utilization directly impacts revenue. When services are accepted into a datacenter, a decision is made on which PM should host the service VMs. This thesis presents a general approach for service scheduling that allows for the same scheduling software to be used across multiple cloud architectures. A number of scheduling algorithms to optimize objectives like revenue or utilization are also studied. Finally, an approach for continuous datacenter consolidation is presented. As VM workloads fluctuate and server availability varies any initial mapping is bound to become suboptimal over time. The continuous datacenter consolidation approach adjusts this VM-to-PM mapping during operation based on combinations of management actions, like suspending/resuming PMs, live migrating VMs, and suspending/resuming VMs. Proof-of-concept software and a set of algorithms that allows cloud providers to continuously optimize their server resources are presented in the thesis.

Ort, förlag, år, upplaga, sidor
Umeå: Umeå universitet, 2014. s. 26
Serie
Report / UMINF, ISSN 0348-0542 ; 2014:09
Nyckelord
Cloud computing, virtualization, distributed infrastructure, live migration, scheduling
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:umu:diva-87904 (URN)978-91-7601-038-9 (ISBN)
Disputation
2014-05-06, Naturvetarhuset, N320, Umeå universitet, Umeå, 10:15 (Engelska)
Opponent
Handledare
Tillgänglig från: 2014-04-15 Skapad: 2014-04-14 Senast uppdaterad: 2018-06-08Bibliografiskt granskad

Open Access i DiVA

fulltext(948 kB)629 nedladdningar
Filinformation
Filnamn FULLTEXT01.pdfFilstorlek 948 kBChecksumma SHA-512
2872cce29d1e01fcb74fac298cbac4a2f5a9465fc9c435ecb6d837d51581560f0085d4ece6fafec63909ec865786dffc5c7c3745b1c1de55323e4bbd65273f7d
Typ fulltextMimetyp application/pdf

Personposter BETA

Svärd, PetterLi, WubinWadbro, EddieTordsson, JohanElmroth, Erik

Sök vidare i DiVA

Av författaren/redaktören
Svärd, PetterLi, WubinWadbro, EddieTordsson, JohanElmroth, Erik
Av organisationen
Institutionen för datavetenskap
Datavetenskap (datalogi)

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 629 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

urn-nbn

Altmetricpoäng

urn-nbn
Totalt: 786 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf