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Contextualization: Dynamic Configuration of Virtual Machines
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
University of Leeds.
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
University of Leeds.
Visa övriga samt affilieringar
(Engelska)Manuskript (preprint) (Övrigt vetenskapligt)
Abstract [en]

Virtual Machines (VMs) are commonly used as building blocks of IaaS cloud services. The number of running VM instances can be adjusted during runtime to achieve elasticity in the capacity of the service. New VM instances are based on templates that contain the basic configuration of the VM. Instance specific settings, settings unique to the infrastructure to which the instance is being deployed, are normally injected to the VM during the deployment phase through means of contextualization. In this work we present a layered approach to contextualization that supports different classes of contextualization data through the use of virtual devices. Inside each VM, data from different classes are layered on top of each other to create a unified file hierarchy using a small, custom file system. Context data can be updated during runtime by updating the contents of the virtual devices, making this approach the first contextualization approach to natively support recontextualization. Recontextualization enables run-time reconfiguration of a running service and can act as a trigger and key enabler of self-* techniques running inside the VM, allowing the service itself an unambiguous trigger for, e.g., further optimization in response to a changing environment. The runtime reconfiguration using recontextualization and its potential gains are shown in an example with a distributed file system, demonstrating the feasibility of the approach.

Nyckelord [en]
Cloud Computing; Contextualization, Recontextualization, Configuration, Virtual Machine
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
datalogi
Identifikatorer
URN: urn:nbn:se:umu:diva-80128OAI: oai:DiVA.org:umu-80128DiVA, id: diva2:646918
Projekt
OPTIMIS
Forskningsfinansiär
EU, FP7, Sjunde ramprogrammet, 257115
Anmärkning

Submitted 2013

Tillgänglig från: 2013-09-10 Skapad: 2013-09-10 Senast uppdaterad: 2021-03-18Bibliografiskt granskad
Ingår i avhandling
1. Enabling Technologies for Management of Distributed Computing Infrastructures
Öppna denna publikation i ny flik eller fönster >>Enabling Technologies for Management of Distributed Computing Infrastructures
2013 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Umeå: Umeå Universitet, 2013. s. 64
Serie
Report / UMINF, ISSN 0348-0542 ; 13.19
Nyckelord
grid computing, cloud computing, accounting, billing, contextualization, monitoring, structure, fairshare, scheduling, federated
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
datalogi
Identifikatorer
urn:nbn:se:umu:diva-80129 (URN)978-91-7459-704-2 (ISBN)
Disputation
2013-10-17, KBC-huset, Stora hörsalen KBC, KB3B1, Umeå Universitet, Umeå, 13:15 (Engelska)
Opponent
Handledare
Forskningsfinansiär
EU, FP7, Sjunde ramprogrammet, 215605EU, FP7, Sjunde ramprogrammet, 257115Vetenskapsrådet, 621-2005-3667eSSENCE - An eScience Collaboration
Anmärkning

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

Tillgänglig från: 2013-09-23 Skapad: 2013-09-10 Senast uppdaterad: 2021-03-18Bibliografiskt granskad

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Espling, DanielTordsson, JohanElmroth, Erik

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Datavetenskap (datalogi)

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