公开(公告)号：US20140074763A1

公开(公告)日：2014-03-13

申请号：US14020711

申请日：2013-09-06

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Sangoh Jeong ,  Simon J. Gibbs ,  Xinwen Zhang ,  Anugeetha Kunjithaptham

IPC: G06N5/02

CPC classification number: G06N5/02 ,  G06F9/5066

Abstract: 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.

Abstract translation: 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。