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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Languages: English
Types: Doctoral thesis
Subjects: UOW3
The use of virtual appliances could provide a flexible solution to services\ud deployment. However, these solutions suffer from several disadvantages: (i)\ud the slow deployment time of services in virtual machines, and (ii) virtual appliances\ud crafted by developers tend to be inefficient for deployment purposes.\ud Researchers target problem (i) by advancing virtualization technologies or\ud by introducing virtual appliance caches on the virtual machine monitor hosts.\ud Others aim at problem (ii) by providing solutions for virtual appliance construction,\ud however these solutions require deep knowledge about the service\ud dependencies and its deployment process.\ud This dissertation aids problem (i) with a virtual appliance distribution\ud technique that first identifies appliance parts and their internal dependencies.\ud Then based on service demand it efficiently distributes the identified\ud parts to virtual appliance repositories. Problem (ii) is targeted with the Automated\ud Virtual appliance creation Service (AVS) that can extract and publish\ud an already deployed service by the developer. This recently acquired virtual\ud appliance is optimized for service deployment time with the proposed\ud virtual appliance optimization facility that utilizes active fault injection to\ud remove the non-functional parts of the appliance. Finally, the investigation\ud of appliance distribution and optimization techniques resulted the definition\ud of the minimal manageable virtual appliance that is capable of updating and\ud configuring its executor virtual machine.\ud The deployment time reduction capabilities of the proposed techniques\ud were measured with several services provided in virtual appliances on three\ud cloud infrastructures. The appliance creation capabilities of the AVS are compared\ud to the already available virtual appliances offered by the various online\ud appliance repositories. The results reveal that the introduced techniques\ud significantly decrease the deployment time of virtual appliance based deployment\ud systems. As a result these techniques alleviated one of the major\ud obstacles before virtual appliance based deployment systems.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 4.1 Introduction 39
    • 4.2 The AVS Client Interface 41
    • 4.2.1 Virtual Appliance extraction 41
    • 4.2.2 Playground operations 43
    • 4.2.3 The upload operation 45
    • 4.3 The Infrastructure as a Service System Interface 48
    • 4.4 Repository Interface and Metadata Collection 50
    • 4.4.1 Virtual Appliance Representation 51 5 Virtual Appliance Size Optimization Facility 55
    • 5.1 Virtual Appliance Optimization Principles 55
    • 5.1.1 The Virtual Appliance Optimization Facility 56
    • 5.1.2 Appliance Contents Removal 58
    • 5.2 Implementation of Virtual Appliance Optimization 62
    • 5.2.1 Implementation of the Item Selection 62
    • 5.2.2 Parallel Validation 68
    • 5.2.3 Virtual Machine Management Strategy 71 6 Partial Virtual Appliance Replication 75
    • 6.1 Introduction 75
    • 6.2 Active Repository Functionality 76
    • 6.2.1 Package decomposition 77
    • 6.2.2 Package merging 80
    • 6.2.3 Package replication 83
    • 6.2.4 Package destruction 88
    • 6.3 Virtual Appliance Rebuilding 91
    • 6.3.1 Rebuilding scenarios and algorithm 91
    • 6.3.2 Rebuilding in active repositories 95
    • 6.3.3 Rebuilding in the IaaS system 99
    • 6.3.4 Reconstructing the virtual appliance 100 7 The Minimal Manageable Virtual Appliance 102
    • 7.1 Introduction 102
    • 7.2 Definition of the manageable virtual appliance 103
    • 7.2.1 Maintaining the Management Capabilities of Virtual
    • Appliances 105
    • 7.2.2 Minimal Manageable Virtual Appliances 106
    • 7.3 Architectural developments 109
    • 7.3.1 Effects on the rebuilding algorithms 109
    • 7.3.2 MMVA based virtual appliance transformation 114
    • 7.3.3 MMVA and the Optimization facility 116
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