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Modeling and Placement of Cloud Services with Internal Structure
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.
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2016 (English)In: IEEE Transactions on Cloud Computing, ISSN 2168-7161, Vol. 4, no 4, 429-439 p.Article in journal (Refereed) Published
Abstract [en]

Virtual machine placement is the process of mapping virtual machines to available physical hosts within a datacenter or on a remote datacenter in a cloud federation. Normally, service owners cannot influence the placement of service components beyond choosing datacenter provider and deployment zone at that provider. For some services, however, this lack of influence is a hindrance to cloud adoption. For example, services that require specific geographical deployment (due e.g. to legislation), or require redundancy by avoiding co-location placement of critical components. We present an approach for service owners to influence placement of their service components by explicitly specifying service structure, component relationships, and placement constraints between components. We show how the structure and constraints can be expressed and subsequently formulated as constraints that can be used in placement of virtual machines in the cloud. We use an integer linear programming scheduling approach to illustrate the approach, show the corresponding mathematical formulation of the model, and evaluate it using a large set of simulated input. Our experimental evaluation confirms the feasibility of the model and shows how varying amounts of placement constraints and data center background load affects the possibility for a solver to find a solution satisfying all constraints within a certain time-frame. Our experiments indicate that the number of constraints affects the ability of finding a solution to a higher degree than background load, and that for a high number of hosts with low capacity, component affinity is the dominating factor affecting the possibility to find a solution.

Place, publisher, year, edition, pages
IEEE Computer Society, 2016. Vol. 4, no 4, 429-439 p.
Keyword [en]
service management, service structure, placement, affinity, collocation, scheduling, integer linear programming, cloud computing
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-80125DOI: 10.1109/TCC.2014.2362120ISI: 000390560200005OAI: oai:DiVA.org:umu-80125DiVA: diva2:646909
Funder
eSSENCE - An eScience Collaboration
Available from: 2013-09-10 Created: 2013-09-10 Last updated: 2017-01-23Bibliographically approved
In thesis
1. Enabling Technologies for Management of Distributed Computing Infrastructures
Open this publication in new window or tab >>Enabling Technologies for Management of Distributed Computing Infrastructures
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Computing infrastructures offer remote access to computing power that can be employed, e.g., to solve complex mathematical problems or to host computational services that need to be online and accessible at all times. From the perspective of the infrastructure provider, large amounts of distributed and often heterogeneous computer resources need to be united into a coherent platform that is then made accessible to and usable by potential users. Grid computing and cloud computing are two paradigms that can be used to form such unified computational infrastructures.

Resources from several independent infrastructure providers can be joined to form large-scale decentralized infrastructures. The primary advantage of doing this is that it increases the scale of the available resources, making it possible to address more complex problems or to run a greater number of services on the infrastructures. In addition, there are advantages in terms of factors such as fault-tolerance and geographical dispersion. Such multi-domain infrastructures require sophisticated management processes to mitigate the complications of executing computations and services across resources from different administrative domains.

This thesis contributes to the development of management processes for distributed infrastructures that are designed to support multi-domain environments. It describes investigations into how fundamental management processes such as scheduling and accounting are affected by the barriers imposed by multi-domain deployments, which include technical heterogeneity, decentralized and (domain-wise) self-centric decision making, and a lack of information on the state and availability of remote resources.

Four enabling technologies or approaches are explored and developed within this work: (I) The use of explicit definitions of cloud service structure as inputs for placement and management processes to ensure that the resulting placements respect the internal relationships between different service components and any relevant constraints. (II) Technology for the runtime adaptation of Virtual Machines to enable the automatic adaptation of cloud service contexts in response to changes in their environment caused by, e.g., service migration across domains. (III) Systems for managing meta-data relating to resource usage in multi-domain grid computing and cloud computing infrastructures. (IV) A global fairshare prioritization mechanism that enables computational jobs to be consistently prioritized across a federation of several decentralized grid installations.

Each of these technologies will facilitate the emergence of decentralized computational infrastructures capable of utilizing resources from diverse infrastructure providers in an automatic and seamless manner.

Place, publisher, year, edition, pages
Umeå: Umeå Universitet, 2013. 64 p.
Series
Report / UMINF, ISSN 0348-0542 ; 13.19
Keyword
grid computing, cloud computing, accounting, billing, contextualization, monitoring, structure, fairshare, scheduling, federated
National Category
Computer Science
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-80129 (URN)978-91-7459-704-2 (ISBN)
Public defence
2013-10-17, KBC-huset, Stora hörsalen KBC, KB3B1, Umeå Universitet, Umeå, 13:15 (English)
Opponent
Supervisors
Funder
EU, FP7, Seventh Framework Programme, 215605EU, FP7, Seventh Framework Programme, 257115Swedish Research Council, 621-2005-3667eSSENCE - An eScience Collaboration
Note

Note that the author changed surname from Henriksson to Espling in 2011

Available from: 2013-09-23 Created: 2013-09-10 Last updated: 2013-09-19Bibliographically 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. Placement and Monitoring of Orchestrated Cloud Services
Open this publication in new window or tab >>Placement and Monitoring of Orchestrated Cloud Services
2015 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Cloud computing offers pay-per-use on-demand access to computer resources for hosting program execution environments for software service deployment. Management of cloud resources includes determining, based on current monitored resource availability, which part(s) of a computational infrastructure should host such program execution environments in a process called placement. Our work defines directives that lets consumers of cloud resources influence placement to express relationships between cloud services (orchestration) and deployment constraints to uphold for related service components, without surrendering the ultimate control over placement from the infrastructure owner. The infrastructure owner remains free to define their policies and placement optimization criteria, e.g., to consolidate work that needs to be done to as few physical host machines as possible for power savings reasons. We show how the placement process can be adjusted to take such influence into account and validate through simulations that the adjustments produce the correct result without too large computational impact on the placement process itself. Further, we present a technique for transferring large data files between cloud data centers that operate in (separate) cloud federations that avoids repeated transfers in a delegation chain between members of (different) cloud federations. Finally, we present a non-invasive method of extracting monitoring data from a service deployed in a cloud federation, and a framework for making monitoring information available and understandable in spite of technical differences between monitoring systems used in cloud federations.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2015. 36 p.
Series
Report / UMINF, ISSN 0348-0542 ; 15.02
National Category
Computer Science
Identifiers
urn:nbn:se:umu:diva-98030 (URN)978-91-7601-205-5 (ISBN)
Presentation
2015-01-20, N230, Naturvetarhuset, Umeå universitet, Umeå, 10:15 (English)
Opponent
Supervisors
Available from: 2015-01-15 Created: 2015-01-14 Last updated: 2015-01-15Bibliographically approved

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Espling, DanielLarsson, LarsLi, WubinTordsson, JohanElmroth, Erik
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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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