公开(公告)号：US20150324695A1

公开(公告)日：2015-11-12

申请号：US14273566

申请日：2014-05-09

Applicant: Xerox Corporation

Inventor： Sakyajit Bhattacharya ,  Tridib Mukherjee ,  Koustuv Dasgupta ,  Shruti Kunde

IPC: G06N5/04 ,  H04L29/08

CPC classification number: G06N5/04 ,  G06N99/005 ,  H04L67/10

Abstract: Methods and systems for creating one or more statistical classifiers. A first set of performance parameters, corresponding to the one or more applications and the one or more computing infrastructures, is extracted from a historical data pertaining to the execution of the one or more applications on the one or more computing infrastructures. Further, a set of application-specific and a set of infrastructure-specific parameters are selected, from the first set of performance parameters, based on one or more statistical techniques. A similarity between each pair of the applications, each pair of the computing infrastructures, and each pair of possible combinations of an application and a computing infrastructure is determined. One or more statistical classifiers are created, based on the determined similarity.

Abstract translation: 用于创建一个或多个统计分类器的方法和系统。 从与一个或多个计算基础设施上的一个或多个应用的​​执行有关的历史数据中提取对应于一个或多个应用和一个或多个计算基础设施的第一组性能参数。 此外，基于一种或多种统计技术，从第一组性能参数中选择一组特定应用和一组基础设施特定参数。 确定每对应用程序，每对计算基础设施以及应用程序和计算基础设施的每对可能组合之间的相似性。 基于所确定的相似度，创建一个或多个统计分类器。

公开(公告)号：US20150294230A1

公开(公告)日：2015-10-15

申请号：US14250407

申请日：2014-04-11

Applicant: Xerox Corporation

Inventor： Tridib Mukherjee ,  Sakyajit Bhattacharya ,  Koustuv Dasgupta

IPC: G06N7/00 ,  G06N99/00 ,  G06F17/30 ,  H04L29/08

CPC classification number: G06F16/285 ,  G06Q30/02 ,  G06Q50/01 ,  H04L67/306

Abstract: Some embodiments are directed to a system for identifying clusters from a plurality of users using cloud services. A behavior collection module is configured to obtain user preferences for the plurality of users, and an EM module to configured estimate at least one parameter of a distance-based model by the Expectation-Maximization (EM) algorithm for various values of G (number of clusters). A selection module is configured to compute Bayesian Information Criteria (BIC) with the at least one estimated parameter obtained from the EM module for various values of G, compare BICs obtained for various values of G, select the model with the highest BIC as the best model (best model including the plurality of clusters) and use estimated latent variables of the best model to build a classifier. A characterization module is configured to classify each user into a cluster of the best model using the classifier, and to determine ranking preference of each cluster.

Abstract translation: 一些实施例涉及用于使用云服务从多个用户中识别群集的系统。 行为收集模块被配置为获得多个用户的用户偏好，并且EM模块被配置为通过期望最大化（EM）算法估计基于距离的模型的至少一个参数，用于G的各种值 集群）。 选择模块被配置为使用从EM模块获得的各种G值的至少一个估计参数来计算贝叶斯信息准则（BIC），对于G的各种值获得的比较BIC，选择具有最高BIC的模型作为最佳 模型（包括多个集群的最佳模型），并使用最佳模型的估计潜在变量构建分类器。 表征模块被配置为使用分类器将每个用户分类为最佳模型的集群，并且确定每个集群的排名偏好。

公开(公告)号：US20150281117A1

公开(公告)日：2015-10-01

申请号：US14242165

申请日：2014-04-01

Applicant: Xerox Corporation

Inventor： Shruti Kunde ,  Tridib Mukherjee ,  Sujit Gujar

IPC: H04L12/911 ,  H04L12/24

CPC classification number: G06F9/46 ,  H04L41/5019

Abstract: Methods and systems for sharing computational resources. A request from a first node is received for the one or more computational resources. The request comprises a service level agreement (SLA) associated with the requested one or more computational resources. The request is compared with one or more advertisements sent by at least two second nodes, other than the first node. The one or more advertisements correspond to an availability of a set of computational resources associated with each of the at least two second nodes. A portion of computational resources from the set of computational resources associated with each of the at least two second nodes is allocated to the first node, based on the comparison, such that a combination of the portion of computational resources satisfy the SLA associated with the request.

Abstract translation: 用于共享计算资源的方法和系统。 为一个或多个计算资源接收来自第一节点的请求。 该请求包括与所请求的一个或多个计算资源相关联的服务水平协议（SLA）。 该请求与除了第一节点之外的至少两个第二节点发送的一个或多个广告进行比较。 所述一个或多个广告对应于与所述至少两个第二节点中的每一个相关联的一组计算资源的可用性。 基于该比较，来自与所述至少两个第二节点中的每一个相关联的所述一组计算资源的计算资源的一部分被分配给所述第一节点，使得所述部分计算资源的组合满足与所述请求相关联的SLA 。

公开(公告)号：US09124498B2

公开(公告)日：2015-09-01

申请号：US13767070

申请日：2013-02-14

Applicant: Xerox Corporation

Inventor： Gueyoung Jung ,  Tridib Mukherjee

IPC: G06F15/173 ,  H04L12/26 ,  H04L29/08 ,  G06F9/50 ,  H04L12/24

CPC classification number: H04L43/0876 ,  G06F9/5072 ,  H04L41/5009 ,  H04L41/5051 ,  H04L43/08 ,  H04L67/10 ,  H04L67/34

Abstract: A method and system identifies a cloud configuration for deploying a software application. A performance of a target application and workload is characterized. A set of benchmark applications is then deployed into at least one target cloud infrastructure. The target infrastructure is characterized using the set of benchmarking applications. The performance of the target application is represented with a set of bins each corresponding to a resource subsystem of a virtual machine and a performance score that is required to deploy the target application and meet the target performance. The bins are filled with performance values for selected target virtual machines. Using the filled bins, a set of virtual machines needed to satisfy the target cloud infrastructure is determined. A recommendation is provided for the set of virtual machines to use in deploying the software application.

Abstract translation: 方法和系统识别用于部署软件应用程序的云配置。 表征目标应用程序和工作负载。 然后将一组基准测试应用程序部署到至少一个目标云基础设施中。 目标基础设施的特点是使用一套基准测试应用程序。 目标应用程序的性能表现为一组分组，每个分组对应于虚拟机的资源子系统，以及部署目标应用程序并满足目标性能所需的性能分数。 这些垃圾箱已填充所选目标虚拟机的性能值。 使用填充的箱子，确定满足目标云基础架构所需的一组虚拟机。 对于在部署软件应用程序中使用的一组虚拟机提供了一个建议。

公开(公告)号：US20150134396A1

公开(公告)日：2015-05-14

申请号：US14077584

申请日：2013-11-12

Applicant: Xerox Corporation

Inventor： Tridib Mukherjee ,  Sujit Gujar

IPC: G06Q10/06

CPC classification number: G06Q10/06315

Abstract: Disclosed embodiment illustrated herein methods and systems for allocating one or more tasks to at least one computing device. The method includes, in a marketplace server, receiving a beacon message from the at least one computing device. The beacon message comprises information on availability of one or more computational resources associated with the at least one computing device. A service level agreement is defined for each of the one or more tasks based on the availability of the one or more computational resources. The one or more tasks are allocated to the at least one computing device based on the service level agreement and the availability of the one or more computational resources.

Abstract translation: 本文所示的公开的实施例用于将一个或多个任务分配给至少一个计算设备的方法和系统。 该方法在市场服务器中包括从至少一个计算设备接收信标消息。 信标消息包括关于与至少一个计算设备相关联的一个或多个计算资源的可用性的信息。 基于一个或多个计算资源的可用性，为一个或多个任务中的每一个定义服务水平协议。 基于服务级别协议和一个或多个计算资源的可用性，将一个或多个任务分配给至少一个计算设备。

公开(公告)号：US20150019301A1

公开(公告)日：2015-01-15

申请号：US13940318

申请日：2013-07-12

Applicant: Xerox Corporation

Inventor： Gueyoung Jung ,  Naveen Sharma ,  Tridib Mukherjee ,  Frank Michael Goetz

IPC: G06Q10/06

CPC classification number: G06Q10/06393

Abstract: A system and method for providing cloud performance capability estimation and supporting recommender systems by simulating bottleneck and its migration for any given complex application in a cost-efficient way are provided. To do this, first, the system and method builds an abstract performance model for an application based on the resource usage pattern of the application in an in-house test-bed (i.e., a white-box environment). Second, it computes relative performance scores of many different cloud configurations given from black-boxed clouds using a cloud metering system. Third, it applies the collected performance scores into the abstract performance model to estimate performance capabilities and potential bottleneck situations of those cloud configurations. Finally, using the model, it can support recommender systems by providing performance estimates and simulations of bottlenecks and bottleneck migrations between resource sub-systems while new resources are added or replaced.

Abstract translation: 提供了一种通过以成本有效的方式模拟瓶颈及其对任何给定复杂应用程序的迁移来提供云性能能力估计和支持推荐系统的系统和方法。 为此，首先，系统和方法基于内部测试台（即，白盒环境）中应用程序的资源使用模式为应用程序构建抽象性能模型。 其次，它使用云计量系统来计算从黑匣子云中提供的许多不同云配置的相对性能分数。 第三，将收集的绩效分数应用于抽象绩效模型，以估计这些云配置的性能能力和潜在瓶颈。 最后，通过使用该模型，可以通过在添加或替换新资源的同时提供资源子系统之间的瓶颈和瓶颈迁移的性能估计和模拟来支持推荐系统。