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A General Approach to Service Deployment in Cloud Environments
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.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2012 (English)In: Cloud and Green Computing (CGC 2012): 2012 Second International Conference on, IEEE Computer Society, 2012, 17-24 p.Conference paper, Published paper (Refereed)
Abstract [en]

The cloud computing landscape has recently developed into a spectrum of cloud architectures, leading to a broad range of management tools for similar operations but specialized for certain deployment scenarios. This both hinders the efficient reuse of algorithmic innovations within cloud management operations and increases the heterogeneity between different management systems. Our overarching goal is to overcome these problems by developing tools general enough to support the full range of popular architectures. In this contribution, we analyze commonalities in recently proposed cloud models (private clouds, multi-clouds, bursted clouds, federated clouds, etc.), and demonstrate how a key management functionality - service deployment - can be uniformly performed in all of these by a carefully designed system. The design of our service deployment framework is validated through a demonstration of how it can be used to deploy services, perform bursting and brokering, as well as mediate a cloud federation in the context of the OPTIMIS Toolkit.

Place, publisher, year, edition, pages
IEEE Computer Society, 2012. 17-24 p.
Keyword [en]
Cloud Computing, Cloud Architecture, Service Deployment
National Category
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-79784DOI: 10.1109/CGC.2012.90ISBN: 978-0-7695-4864-7 (print)ISBN: 978-1-4673-3027-5 Print (print)OAI: oai:DiVA.org:umu-79784DiVA: diva2:644797
Conference
the 2nd International Conference on Cloud and Green Computing, Xiangtan, 1-3 November 2012
Available from: 2013-09-02 Created: 2013-09-02 Last updated: 2014-04-14Bibliographically 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
3. Dynamic Cloud Resource Management: Scheduling, Migration and Server Disaggregation
Open this publication in new window or tab >>Dynamic Cloud Resource Management: Scheduling, Migration and Server Disaggregation
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2014. 26 p.
Series
Report / UMINF, ISSN 0348-0542 ; 2014:09
Keyword
Cloud computing, virtualization, distributed infrastructure, live migration, scheduling
National Category
Computer Science
Identifiers
urn:nbn:se:umu:diva-87904 (URN)978-91-7601-038-9 (ISBN)
Public defence
2014-05-06, Naturvetarhuset, N320, Umeå universitet, Umeå, 10:15 (English)
Opponent
Supervisors
Available from: 2014-04-15 Created: 2014-04-14 Last updated: 2014-04-14Bibliographically approved

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Li, WubinSvärd, PetterTordsson, JohanElmroth, Erik
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  • apa
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Output format
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