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Modeling for Dynamic Cloud Scheduling via Migration of Virtual Machines
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.
2011 (English)In: Cloud Computing Technology and Science (CloudCom), IEEE Computer Society, 2011, 163-171 p.Conference paper, Published paper (Refereed)
Abstract [en]

Cloud brokerage mechanisms are fundamental to reduce the complexity of using multiple cloud infrastructures to achieve optimal placement of virtual machines and avoid the potential vendor lock-in problems. However, current approaches are restricted to static scenarios, where changes in characteristics such as pricing schemes, virtual machine types, and service performance throughout the service life-cycle are ignored. In this paper, we investigate dynamic cloud scheduling use cases where these parameters are continuously changed, and propose a linear integer programming model for dynamic cloud scheduling. Our model can be applied in various scenarios through selections of corresponding objectives and constraints, and offers the flexibility to express different levels of migration overhead when restructuring an existing infrastructure. Finally, our approach is evaluated using commercial clouds parameters in selected simulations for the studied scenarios. Experimental results demonstrate that, with proper parametrizations, our approach is feasible.

Place, publisher, year, edition, pages
IEEE Computer Society, 2011. 163-171 p.
Keyword [en]
cloud computing, dynamic scheduling, virtual machine placement, migration overhead, linear integer programming
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-50836DOI: 10.1109/CloudCom.2011.31ISBN: 978-1-4673-0090-2 (print)OAI: oai:DiVA.org:umu-50836DiVA: diva2:469899
Conference
The 3rd IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2011) November 29 - December 1, Athens, Greece
Available from: 2011-12-27 Created: 2011-12-27 Last updated: 2014-03-31Bibliographically approved
In thesis
1. Virtual Machine Placement in Cloud Environments
Open this publication in new window or tab >>Virtual Machine Placement in Cloud Environments
2012 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

With the emergence of cloud computing, computing resources (i.e., networks, servers, storage, applications, and services) are provisioned as metered on-demand services over networks, and can be rapidly allocated and released with minimal management effort. In the cloud computing paradigm, the virtual machine is one of the most commonly used resource carriers in which business services are encapsulated. Virtual machine placement optimization, i.e., finding optimal placement schemes for virtual machines, and reconfigurations according to the changes of environments, become challenging issues.

The primary contribution of this licentiate thesis is the development and evaluation of our combinatorial optimization approaches to virtual machine placement in cloud environments. We present modeling for dynamic cloud scheduling via migration of virtual machines in multi-cloud environments, and virtual machine placement for predictable and time-constrained peak loads in single-cloud environments. The studied problems are encoded in a mathematical modeling language and solved using a linear programming solver. In addition to scientific publications, this work also contributes in the form of software tools (in EU-funded project OPTIMIS) that demonstrate the feasibility and characteristics of the approaches presented.

Place, publisher, year, edition, pages
Umeå: Umeå Universitet, 2012. 18 p.
Series
UMINF / Department of Computing Science, Umeå University, ISSN ISSN 0348-0542 ; 2012:13
National Category
Computer Systems
Identifiers
urn:nbn:se:umu:diva-83385 (URN)978-91-7459-453-9 (ISBN)
Presentation
2012-06-07, MIT-huset, MA121, Umeå universitet, Umeå, 10:05 (English)
Opponent
Supervisors
Available from: 2014-03-26 Created: 2013-11-22 Last updated: 2014-03-26Bibliographically approved
2. Algorithms and Systems for Virtual Machine Scheduling in Cloud Infrastructures
Open this publication in new window or tab >>Algorithms and Systems for Virtual Machine Scheduling in Cloud Infrastructures
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Umeå: Umeå̊ Universitet, 2014. 33 p.
Series
Report / UMINF, ISSN 0348-0542 ; 2014:06
Keyword
cloud computing, virtual machine, scheduling, systems, algorithms
National Category
Computer Science
Identifiers
urn:nbn:se:umu:diva-87310 (URN)978-91-7601-019-8 (ISBN)
Public defence
2014-04-25, MIT-Huset, MA121, Umeå Universitet, Umeå, 10:00 (English)
Opponent
Supervisors
Available from: 2014-04-04 Created: 2014-03-29 Last updated: 2014-04-17Bibliographically approved

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Li, WubinTordsson, JohanElmroth, Erik

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