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    • 1. 发明授权
    • Execution allocation cost assessment for computing systems and environments including elastic computing systems and environments
    • 包括弹性计算系统和环境在内的计算系统和环境的执行分配成本评估
    • US08239538B2
    • 2012-08-07
    • US12609970
    • 2009-10-30
    • Xinwen ZhangOnur AciicmezSimon J. GibbsAnugeetha KunjithapathamSangoh JeongDoreen Cheng
    • Xinwen ZhangOnur AciicmezSimon J. GibbsAnugeetha KunjithapathamSangoh JeongDoreen Cheng
    • G06F15/173
    • H04L29/08144G06F9/5044G06F9/5094H04L29/08306H04N21/23103Y02D10/22
    • Techniques for assessing the cost of allocation of execution and affecting the allocation of execution are disclosed. The cost of allocation of execution to or between a first computing device (e.g., a mobile device) and one or more computing resource providers (e.g., one or more Clouds) can be determined during runtime of the executable code. It will be appreciated that a computing system can operate independently of the first computing device and one or more computing resource providers and provide execution allocation cost assessment as a service to the first computing device and/or one or more computing resource providers. Execution allocation cost can be assessed (or determined) based on execution allocation data pertaining to the first computing device and/or one or more computing resource providers. By way of example, power consumption of a mobile device can be used as a factor in determining how to allocate individual components of an application program (e.g., weblets) between a mobile phone and a Cloud. The invention is especially suited for Elastic computing environment and systems. In an Elastic computing environment, scalable and dynamic external computing resources can be used in order to effectively extend the computing capabilities beyond that which can be provided by internal computing resources of a computing system or environment.
    • 披露了评估分配成本和影响执行分配的技术。 可以在可执行代码的运行时间期间确定向第一计算设备(例如,移动设备)和一个或多个计算资源提供者(例如,一个或多个云)分配执行的成本。 应当理解,计算系统可以独立于第一计算设备和一个或多个计算资源提供者操作,并且将作为服务的执行分配成本评估提供给第一计算设备和/或一个或多个计算资源提供者。 可以基于与第一计算设备和/或一个或多个计算资源提供者有关的执行分配数据来评估(或确定)执行分配成本。 作为示例,可以使用移动设备的功率消耗作为确定如何在移动电话和云之间分配应用程序(例如,冒号)的各个组件的因素。 本发明特别适用于弹性计算环境和系统。 在弹性计算环境中,可以使用可扩展和动态的外部计算资源,以便有效地将计算能力扩展到可以由计算系统或环境的内部计算资源提供的能力。
    • 2. 发明申请
    • EXECUTION ALLOCATION COST ASSESSMENT FOR COMPUTING SYSTEMS AND ENVIRONMENTS INCLUDING ELASTIC COMPUTING SYSTEMS AND ENVIRONMENTS
    • 包括弹性计算系统和环境在内的计算系统和环境的执行成本分摊费用评估
    • US20100131592A1
    • 2010-05-27
    • US12609970
    • 2009-10-30
    • Xinwen ZhangOnur AciicmezSimon J. GibbsAnugeetha KunjithapathamSangoh JeongDoreen Cheng
    • Xinwen ZhangOnur AciicmezSimon J. GibbsAnugeetha KunjithapathamSangoh JeongDoreen Cheng
    • G06F9/50G06F15/16G06F15/173
    • H04L29/08144G06F9/5044G06F9/5094H04L29/08306H04N21/23103Y02D10/22
    • Techniques for assessing the cost of allocation of execution and affecting the allocation of execution are disclosed. The cost of allocation of execution to or between a first computing device (e.g., a mobile device) and one or more computing resource providers (e.g., one or more Clouds) can be determined during runtime of the executable code. It will be appreciated that a computing system can operate independently of the first computing device and one or more computing resource providers and provide execution allocation cost assessment as a service to the first computing device and/or one or more computing resource providers. Execution allocation cost can be assessed (or determined) based on execution allocation data pertaining to the first computing device and/or one or more computing resource providers. By way of example, power consumption of a mobile device can be used as a factor in determining how to allocate individual components of an application program (e.g., weblets) between a mobile phone and a Cloud. The invention is especially suited for Elastic computing environment and systems. In an Elastic computing environment, scalable and dynamic external computing resources can be used in order to effectively extend the computing capabilities beyond that which can be provided by internal computing resources of a computing system or environment.
    • 披露了评估分配成本和影响执行分配的技术。 可以在可执行代码的运行时间期间确定向第一计算设备(例如,移动设备)和一个或多个计算资源提供者(例如,一个或多个云)分配执行的成本。 应当理解,计算系统可以独立于第一计算设备和一个或多个计算资源提供者操作,并且将作为服务的执行分配成本评估提供给第一计算设备和/或一个或多个计算资源提供者。 可以基于与第一计算设备和/或一个或多个计算资源提供者有关的执行分配数据来评估(或确定)执行分配成本。 作为示例,可以使用移动设备的功率消耗作为确定如何在移动电话和云之间分配应用程序(例如,冒号)的各个组件的因素。 本发明特别适用于弹性计算环境和系统。 在弹性计算环境中,可以使用可扩展和动态的外部计算资源,以便有效地将计算能力扩展到可以由计算系统或环境的内部计算资源提供的能力。
    • 6. 发明授权
    • Execution allocation cost assessment for computing systems and environments including elastic computing systems and environments
    • 包括弹性计算系统和环境在内的计算系统和环境的执行分配成本评估
    • US08560465B2
    • 2013-10-15
    • US12710204
    • 2010-02-22
    • Sangoh JeongSimon J. GibbsXinwen ZhangAnugeetha Kunjithapatham
    • Sangoh JeongSimon J. GibbsXinwen ZhangAnugeetha Kunjithapatham
    • G06N5/00
    • G06N5/02G06F9/5066
    • Techniques for allocating individually executable portions of executable code for execution in an Elastic computing environment are disclosed. In an Elastic computing environment, scalable and dynamic external computing resources can be used in order to effectively extend the computing capabilities beyond that which can be provided by internal computing resources of a computing system or environment. Machine learning can be used to automatically determine whether to allocate each individual portion of executable code (e.g., a Weblet) for execution to either internal computing resources of a computing system (e.g., a computing device) or external resources of an dynamically scalable computing resource (e.g., a Cloud). By way of example, status and preference data can be used to train a supervised learning mechanism to allow a computing device to automatically allocate executable code to internal and external computing resources of an Elastic computing environment.
    • 公开了用于在弹性计算环境中分配用于执行的可执行代码的单独可执行部分的技术。 在弹性计算环境中,可以使用可扩展和动态的外部计算资源,以便有效地将计算能力扩展到可以由计算系统或环境的内部计算资源提供的能力。 机器学习可用于自动确定是否将可执行代码(例如,Weblet)的每个单独部分分配给计算系统(例如,计算设备)的内部计算资源或动态可扩展计算资源的外部资源 (例如,云)。 作为示例,状态和偏好数据可以用于训练监督学习机制,以允许计算设备自动地将可执行代码分配给弹性计算环境的内部和外部计算资源。