Changes between Version 1 and Version 2 of FirstGenCalyam


Ignore:
Timestamp:
02/11/12 13:00:43 (7 years ago)
Author:
Prasad Calyam
Comment:

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  • FirstGenCalyam

    v1 v2  
    33= Thin Client Performance Benchmarking based Resource Adaptation in Virtual Desktop Clouds =
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    5 Popular applications such as email, image/video galleries, and file storage are increasingly being supported by cloud platforms in residential, academia and industry communities. The next frontier for these user communities will be to transition 'traditional desktops' that have dedicated hardware and software configurations into 'virtual desktop clouds' that are accessible via thin clients. This project aims to develop optimal resource allocation frameworks and performance benchmarking tools that can enable building and managing thin-client based virtual desktop clouds at Internet-scale. Virtual desktop cloud experiments under realistic user and system loads are being conducted by leveraging multiple kinds of GENI resources such as aggregates, measurement services and experimenter workflow tools. Project outcomes will help in minimizing costly cloud resource over-provisioning, and in avoiding thin client protocol configuration guesswork, while delivering optimum user experience.
     5Popular applications such as email, image/video galleries, and file storage are increasingly being supported by cloud platforms in residential, academia and industry communities. The next frontier for these user communities will be to transition 'traditional desktops' that have dedicated hardware and software configurations into 'virtual desktop clouds' that are accessible via thin clients.
     6
     7This project aims to develop optimal resource allocation frameworks and performance benchmarking tools that can enable building and managing thin-client based virtual desktop clouds at Internet-scale. Virtual desktop cloud experiments under realistic user and system loads are being conducted by leveraging multiple kinds of GENI resources such as aggregates, measurement services and experimenter workflow tools. Project outcomes will help in minimizing costly cloud resource over-provisioning, and in avoiding thin client protocol configuration guesswork, while delivering optimum user experience.
    68
    79= Project Team =
     
    2729== Investigation of resource management schemes for thin-client based virtual desktop clouds at Internet-scale ==
    2830
    29 To allocate and manage VDC resources for Internet-scale desktop delivery, existing works focus mainly on managing server-side resources based on utility functions of CPU and memory loads, and do not consider network health and thin-client user experience. Resource allocations without combined utility-directed information of system loads, network health and thin-client user experience in VDC platforms inevitably results in costly guesswork and over-provisioning of resources. In order to address this issue, we developed a utility-directed resource allocation model (U-RAM) that uses offline benchmarking based utility functions of system, network and human components to dynamically (i.e., online) create and place virtual desktops (VDs) in resource pools at distributed data centers, all while optimizing resource allocations along timeliness and coding efficiency quality dimensions. 
     31To allocate and manage VDC resources for Internet-scale desktop delivery, existing works focus mainly on managing server-side resources based on utility functions of CPU and memory loads, and do not consider network health and thin-client user experience. Resource allocations without combined utility-directed information of system loads, network health and thin-client user experience in VDC platforms inevitably results in costly guesswork and over-provisioning of resources.
    3032
    31 To assess the VDC scalability that can be achieved by U-RAM, we conducted experiments guided by realistic utility functions of desktop pools that were obtained from a real-world VDC testbed i.e., [http://vmlab.oar.net VMLab]. We compared performance of U-RAM with different resource allocation models: (i) Fixed RAM (F-RAM): each VD is over provisioned which is common in today’s cloud platforms due to lack of system and network awareness, (ii) Network-aware RAM (N-RAM): Allocation is aware of the required network resources, but over provisions system (RAM and CPU) resources due to lack of system awareness information, (iii) System-aware RAM (S-RAM): Allocation is opposite of N-RAM, and (iv) Greedy RAM (G-RAM): Allocation is aware of the requirement in terms of both the system as well as the network resources based purely conservative rule-of-thumb information, and not based on objective profiling as in the case of U-RAM.
     33In order to address this issue, we developed a utility-directed resource allocation model (U-RAM) that uses offline benchmarking based utility functions of system, network and human components to dynamically (i.e., online) create and place virtual desktops (VDs) in resource pools at distributed data centers, all while optimizing resource allocations along timeliness and coding efficiency quality dimensions. 
     34
     35To assess the VDC scalability that can be achieved by U-RAM, we conducted experiments guided by realistic utility functions of desktop pools that were obtained from a real-world VDC testbed i.e., [http://vmlab.oar.net VMLab]. We compared performance of U-RAM with different resource allocation models:
     36 *  Fixed RAM (F-RAM): each VD is over provisioned which is common in today’s cloud platforms due to lack of system and network awareness
     37 * Network-aware RAM (N-RAM): Allocation is aware of the required network resources, but over provisions system (RAM and CPU) resources due to lack of system awareness information
     38 * System-aware RAM (S-RAM): Allocation is opposite of N-RAM
     39 * Greedy RAM (G-RAM): Allocation is aware of the requirement in terms of both the system as well as the network resources based purely conservative rule-of-thumb information, and not based on objective profiling as in the case of U-RAM.
    3240
    3341= Accomplishments =