Migrating virtual machines with adaptive compression
    31.
    发明授权
    Migrating virtual machines with adaptive compression 有权
    使用自适应压缩迁移虚拟机

    公开(公告)号:US08694685B2

    公开(公告)日:2014-04-08

    申请号:US13035365

    申请日:2011-02-25

    IPC分类号: G06F15/16

    CPC分类号: G06F9/4862

    摘要: A method, data processing system, and computer program product for managing images. A processor unit divides an image for a virtual machine into blocks. The processor unit determines whether to compress the blocks prior to transmitting the blocks to a destination for the image. In response to a determination to compress the blocks, the processor unit compresses the blocks to form a compressed set of blocks. The processor unit sends the compressed set of blocks and any uncompressed blocks remaining in the blocks to the destination for the image.

    摘要翻译: 一种用于管理图像的方法,数据处理系统和计算机程序产品。 处理器单元将虚拟机的图像划分成块。 处理器单元在将块发送到图像的目的地之前确定是否压缩块。 响应于压缩块的确定,处理器单元压缩块以形成压缩的块集合。 处理器单元将压缩的块集合和块中剩余的任何未压缩块发送到图像的目的地。

    Method and system for application migration using per-application persistent configuration dependency
    33.
    发明授权
    Method and system for application migration using per-application persistent configuration dependency 有权
    使用每应用程序持久配置依赖的应用程序迁移方法和系统

    公开(公告)号:US08572625B2

    公开(公告)日:2013-10-29

    申请号:US12535035

    申请日:2009-08-04

    IPC分类号: G06F9/46 G06F11/00

    CPC分类号: G06F9/4856

    摘要: A system and method for determining application dependent components includes capturing interactions of an application stored in memory of a first environment with other components at runtime. The interactions are parsed and categorized to determine dependency information. The application is migrated to a new environment using the dependency information to reconfigure the application after migration without application-specific knowledge.

    摘要翻译: 用于确定应用相关组件的系统和方法包括在运行时捕获存储在第一环境的存储器中的应用与其他组件的交互。 交互被解析并分类以确定依赖关系信息。 使用依赖性信息将应用程序迁移到新环境,以便在迁移后重新配置应用程序,而不需要应用程序特定的知识。

    METHOD AND APPARATUS FOR SECURITY-AWARE ELASTICITY OF APPLICATION AND SERVICES
    34.
    发明申请
    METHOD AND APPARATUS FOR SECURITY-AWARE ELASTICITY OF APPLICATION AND SERVICES 有权
    用于安全性应用和服务弹性的方法和装置

    公开(公告)号:US20130247135A1

    公开(公告)日:2013-09-19

    申请号:US13419037

    申请日:2012-03-13

    IPC分类号: G06F21/00

    CPC分类号: G06F21/604 G06F2209/5019

    摘要: In a method for scaling up/down security (non-functional) components of an application, determine (a) types of interactions and a number of each type of interaction each non-security (functional) component has with security components for a plurality of requests. Determine, based on (a) and an expected number of incoming requests to the application, (b) types of requests to and interactions with the security components involving the non-security components and (c) a number of requests to and interactions with the security components involving non-security components for each type of request to the security components involving non-security components. Determine, for each security component, a capacity required for each type of request involving the non-security components and a capacity required for each type of interaction involving the non-security components. Change the capacities of the security components to new capacities, wherein the new capacities are based on (a), (c) and the determined capacities.

    摘要翻译: 在用于扩展/降低应用程序的安全性(非功能))组件的方法中,确定(a)每个非安全(功能)组件具有的多个交互的安全组件的交互类型和每种类型的交互的数量, 要求。 根据(a)和对应用程序的传入请求的预期数量确定(b)与涉及非安全组件的安全组件的请求类型和与其相互作用,以及(c)与 涉及非安全组件的安全组件,用于针对涉及非安全组件的安全组件的每种请求类型。 为每个安全组件确定涉及非安全组件的每种类型的请求所需的容量以及涉及非安全组件的每种类型的交互所需的容量。 将安全组件的能力改变为新能力,其中新能力基于(a),(c)和确定的能力。

    Estimation of transit demand models for enhancing ridership
    37.
    发明授权
    Estimation of transit demand models for enhancing ridership 有权
    估算增加乘客人数的过境需求模型

    公开(公告)号:US08306848B1

    公开(公告)日:2012-11-06

    申请号:US13153725

    申请日:2011-06-06

    IPC分类号: G06F17/30

    CPC分类号: G06F17/30241

    摘要: A method of estimating a transit demand graph includes collecting conditional information that includes at least one condition that when satisfied converts at least one non-rider into a rider, generating a non-rider transit demand graph by satisfying one of the conditions, and generating a normalized transit demand graph from the non-rider transit demand graph and a rider transit demand graph. The riders use public transit and the non-riders do not use public transit. The non-rider transit demand graph shows the demand of the non-riders for a public transit route. The rider transit demand graph shows the demand of riders for the same public transit route.

    摘要翻译: 一种估计过境需求图的方法包括收集条件信息,该条件信息包括当满足将至少一个非骑手转换为骑手时至少一个条件,通过满足条件之一生成非骑车者过境需求图, 来自非乘客过境需求图的标准化过境需求图和乘客过境需求图。 乘客使用公共交通工具,非乘客不使用公共交通工具。 非乘客过境需求图显示了非公共交通路线的非乘客需求。 乘客过境需求图显示了同一条公共交通路线的乘客的需求。

    Fine-Grained Cloud Management Control Using Nested Virtualization
    38.
    发明申请
    Fine-Grained Cloud Management Control Using Nested Virtualization 失效
    使用嵌套虚拟化的细粒度云管理控制

    公开(公告)号:US20120260247A1

    公开(公告)日:2012-10-11

    申请号:US13080015

    申请日:2011-04-05

    IPC分类号: G06F9/455

    摘要: A computer implemented method, a computer program product and a data processing system allocate resources within a computing organization. A hypervisor layer is implemented on an underlying hardware. The hypervisor layer comprises a set of virtual machines. A first pseudo-hypervisor layer is then implemented within a first one of the set of virtual machines of the hypervisor layer. The first pseudo-hypervisor layer comprises a second set of virtual machines. A first software component is located within a first virtual machine of the second set of virtual machines of the first pseudo-hypervisor layer. A second software component is collocated within a second virtual machine of the second set of virtual machines of the first pseudo-hypervisor layer.

    摘要翻译: 计算机实现的方法,计算机程序产品和数据处理系统在计算组织内分配资源。 管理程序层在底层硬件上实现。 管理程序层包括一组虚拟机。 然后,在管理程序层的一组虚拟机中的第一个虚拟机管理程序层中实现第一伪管理程序层。 第一伪管理程序层包括第二组虚拟机。 第一软件组件位于第一伪虚拟机管理程序层的第二组虚拟机的第一虚拟机内。 第二软件组件并置在第一伪虚拟机管理程序层的第二组虚拟机的第二虚拟机内。

    TASK PRIORITIZATION MANAGEMENT IN A VIRTUALIZED ENVIRONMENT
    39.
    发明申请
    TASK PRIORITIZATION MANAGEMENT IN A VIRTUALIZED ENVIRONMENT 有权
    虚拟化环境中的任务优先管理

    公开(公告)号:US20120185848A1

    公开(公告)日:2012-07-19

    申请号:US13007895

    申请日:2011-01-17

    IPC分类号: G06F9/455

    CPC分类号: G06F9/45533 G06F9/5077

    摘要: A method, computer program product, and system for managing tasks for a virtual machine are presented. An amount of resources to perform a task for the virtual machine are identified in response to receiving a request to perform the task for the virtual machine in a set of data processing systems. A set of resources in the set of data processing systems available to complete the task for the virtual machine are identified. A set of priorities for a set of phases of the task are identified. Operations are scheduled on the set of resources to perform the task for the virtual machine based on the set of priorities identified for the set of phases of the task.

    摘要翻译: 提出了一种用于管理虚拟机的任务的方法,计算机程序产品和系统。 响应于在一组数据处理系统中接收到执行虚拟机的任务的请求来识别用于执行虚拟机的任务的资源的量。 识别可用于完成虚拟机任务的一组数据处理系统中的一组资源。 确定了一系列任务阶段的优先事项。 根据为任务的一组阶段确定的优先级集合,在该组资源上安排操作以执行虚拟机的任务。

    Method and apparatus to support application and network awareness of collaborative applications using multi-attribute clustering

    公开(公告)号:US07975039B2

    公开(公告)日:2011-07-05

    申请号:US12535436

    申请日:2009-08-04

    IPC分类号: G06F15/173

    CPC分类号: H04L69/329 H04L41/00

    摘要: A method of clustering communication nodes based on network attributes such as network delays and forwarding capacity; on communication interest attributes; and on application attributes such as quality of service preferences/constraints in providing communications between users and application servers. A multi-attribute communication feature vector is formed. That vector is comprised of network attributes, communication interests attributes, and quality of service requirements and is used to form efficient group communication mechanisms for distributed collaborative applications. Then the multi-attribute communication feature vectors are clustered. The clustering methods for multi-type attribute feature vectors are: iterative clustering using a generalized distance space with normalized attribute subspace metrics; fusion clustering, and nested clustering.