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1. 发明授权

US10110425B2 Differentiated service-based graceful degradation layer 有权
公开(公告)号：US10110425B2
公开(公告)日：2018-10-23
申请号：US14706460
申请日：2015-05-07
申请人： Accenture Global Services Limited
发明人： Teresa Tung , Shaw-Yi Chaw , Qing Xie , Qian Zhu
IPC分类号： G06F15/167 , H04L12/24 , G06F9/50 , H04L29/08 , H04L12/26
摘要： The differentiated service-based graceful degradation layer (DSGDL) allows cloud-based architectures to operate through and recover from periods of limited capability. The DSGDL protects and continues serving higher priority requests with the best possible response even as the underlying cloud-based services deteriorate. The DSGDL offloads lower priority requests to lower-grade secondary capability that can be dynamically provisioned in order to reserve the best capability for maintaining high priority service (e.g., by re-directing lower priority requests to a slightly out-of-date cached dataset, and reserve the primary consistent database for higher priority requests). The DSGDL 1) implements an overlay network over existing cloud services to route and enforce priority requests, and 2) provisions on-demand computing nodes and sites to provide secondary capability for service requests as needed.

2. 发明授权

US09720746B2 Analytics for application programming interfaces 有权
公开(公告)号：US09720746B2
公开(公告)日：2017-08-01
申请号：US14822189
申请日：2015-08-10
申请人： Accenture Global Services Limited
发明人： Qian Zhu , Teresa Tung , Benjamin Grimmer
IPC分类号： G06F9/54 , H04L29/08 , H04L29/06 , G06N7/00 , G06N5/04 , G06F9/44 , G06F11/30
CPC分类号： G06F9/541 , G06F8/74 , G06F8/75 , G06F11/302 , G06F11/3072 , G06N5/045 , G06N7/005 , H04L67/32 , H04L67/42
摘要： API analytics systems and methods are provided. Frequently occurring API usage patterns may be determined from API call data collected from across an API ecosystem. Alternatively or in addition, a classification structure, such as a decision tree, may be generated from the API usage patterns. A type of activity that resulted in a set of API calls being invoked may be determined from the classification structure. A similarity or difference between the set of API calls and the frequently occurring API usage patterns may also be determined and/or identified.

3. 发明授权

US09344380B2 Performance interference model for managing consolidated workloads in QoS-aware clouds 有权
标题翻译：用于管理QoS感知云中的统一工作负载的性能干扰模型
公开(公告)号：US09344380B2
公开(公告)日：2016-05-17
申请号：US14697310
申请日：2015-04-27
申请人： Accenture Global Services Limited
发明人： Qian Zhu , Teresa Tung
IPC分类号： G06F15/173 , G06F15/16 , H04L12/923 , H04L29/08 , H04L12/927 , H04L12/24 , H04L12/26 , G06F9/50
CPC分类号： G06F9/5011 , G06F9/4881 , G06F9/5027 , G06F9/5072 , G06F9/5077 , G06F9/5083 , G06F2209/5019 , H04L29/08144 , H04L41/147 , H04L43/062 , H04L43/08 , H04L43/0876 , H04L43/0882 , H04L43/50 , H04L47/762 , H04L47/805 , H04L67/10 , H04L67/30 , H04L67/303
摘要： The workload profiler and performance interference (WPPI) system uses a test suite of recognized workloads, a resource estimation profiler and influence matrix to characterize un-profiled workloads, and affiliation rules to identify optimal and sub-optimal workload assignments to achieve consumer Quality of Service (QoS) guarantees and/or provider revenue goals. The WPPI system uses a performance interference model to forecast the performance impact to workloads of various consolidation schemes (e.g., consolidation strategies) usable to achieve cloud provider and/or cloud consumer goals, and uses the test suite of recognized workloads, the resource estimation profiler and influence matrix, affiliation rules, and performance interference model to perform off-line modeling to determine the initial assignment selections and consolidation strategy to use to deploy the workloads. The WPPI system uses an online consolidation algorithm, the offline models, and online monitoring to determine virtual machine to physical host assignments responsive to real-time conditions to meet cloud provider and/or cloud consumer goals.
摘要翻译：工作负载分析器和性能干扰（WPPI）系统使用经过认可的工作负载的测试套件，资源估算分析器和影响矩阵来表征未分析的工作负载，以及归属规则，以确定最佳和次优的工作负载分配，以实现消费者的服务质量（QoS）保证和/或提供者收入目标。 WPPI系统使用性能干扰模型来预测可用于实现云提供商和/或云消费者目标的各种合并方案（例如，整合策略）对工作负载的性能影响，并使用已识别工作负载的测试套件，资源估计分析器并影响矩阵，关联规则和性能干扰模型，以执行离线建模，以确定用于部署工作负载的初始分配选择和整合策略。 WPPI系统使用在线整合算法，离线模型和在线监控来根据实时情况确定虚拟机到物理主机分配，以满足云提供商和/或云消费者目标。

4. 发明授权

US09298525B2 Adaptive fault diagnosis 有权
标题翻译：自适应故障诊断
公开(公告)号：US09298525B2
公开(公告)日：2016-03-29
申请号：US13772135
申请日：2013-02-20
申请人： Accenture Global Services Limited
发明人： Qian Zhu , Teresa Tung , Qing Xie
IPC分类号： G06F11/00 , G06F11/07 , G06Q10/00
CPC分类号： G06F11/0703 , G06F11/0709 , G06F11/079 , G06Q10/00 , G06Q10/0639
摘要： According to an example, an adaptive fault diagnosis system may include a memory storing machine readable instructions to receive metrics and events from an enterprise system, and use a substitution graph to determine if a received metric or a received event belongs to a cluster that includes one or more correlated metrics and/or events grouped based on similarity. If the received metric or the received event belongs to the cluster, the memory may further store machine readable instructions to use a detection graph to determine if the received metric or the received event is identifiable to form a fault pattern by traversing a fault path of the detection graph. Further, the memory may further store machine readable instructions to diagnose a fault based on the traversal of the fault path of the detection graph. The system may include a processor to implement the machine readable instructions.
摘要翻译：根据示例，自适应故障诊断系统可以包括存储机器可读指令以从企业系统接收度量和事件的存储器，并且使用替换图来确定所接收的度量或接收到的事件是否属于包括一个的集群或更多相关度量和/或基于相似性分组的事件。如果接收的度量或接收到的事件属于集群，则存储器可以进一步存储机器可读指令以使用检测图来确定接收到的度量或接收到的事件是否可被识别以通过遍历故障路径形成故障模式检测图。此外，存储器还可以存储机器可读指令以基于检测图的故障路径的遍历来诊断故障。该系统可以包括用于实现机器可读指令的处理器。

5. 发明申请

US20150244563A1 DIFFERENTIATED SERVICE-BASED GRACEFUL DEGRADATION ERROR 审中-公开
标题翻译：基于不同服务的严重降解错误
公开(公告)号：US20150244563A1
公开(公告)日：2015-08-27
申请号：US14706460
申请日：2015-05-07
申请人： Accenture Global Services Limited
发明人： Teresa Tung , Shaw-Yi Chaw , Qing Xie , Qian Zhu
IPC分类号： H04L12/24 , H04L12/26 , H04L29/08
CPC分类号： H04L41/069 , G06F9/5072 , G06F2209/5021 , H04L41/5016 , H04L43/16 , H04L67/1008 , H04L67/1097 , H04L67/322
摘要： The differentiated service-based graceful degradation layer (DSGDL) allows cloud-based architectures to operate through and recover from periods of limited capability. The DSGDL protects and continues serving higher priority requests with the best possible response even as the underlying cloud-based services deteriorate. The DSGDL offloads lower priority requests to lower-grade secondary capability that can be dynamically provisioned in order to reserve the best capability for maintaining high priority service (e.g., by re-directing lower priority requests to a slightly out-of-date cached dataset, and reserve the primary consistent database for higher priority requests). The DSGDL 1) implements an overlay network over existing cloud services to route and enforce priority requests, and 2) provisions on-demand computing nodes and sites to provide secondary capability for service requests as needed.
摘要翻译：基于差异化的基于服务的优雅降级层（DSGDL）允许基于云的体系结构在有限能力的时期内运行和恢复。即使基础的基于云的服务恶化，DSGDL保护并继续以尽可能高的响应提供更高优先级的请求。 DSGDL将较低优先级的请求卸载到较低级次要功能，可以进行动态配置，以便保留维护高优先级服务的最佳能力（例如，通过将较低优先级请求重定向到稍微过时的缓存数据集，并为较高优先级请求保留主要一致数据库）。 DSGDL 1）通过现有云服务实现覆盖网络，以路由和执行优先级请求，以及2）为按需计算节点和站点提供配置，以便根据需要为服务请求提供次要能力。

6. 发明授权

US09672085B2 Adaptive fault diagnosis 有权
公开(公告)号：US09672085B2
公开(公告)日：2017-06-06
申请号：US15050008
申请日：2016-02-22
申请人： Accenture Global Services Limited
发明人： Qian Zhu , Teresa Tung , Qing Xie
IPC分类号： G06F11/00 , G06F11/07 , G06Q10/00 , G06Q10/06
CPC分类号： G06F11/0703 , G06F11/0709 , G06F11/079 , G06Q10/00 , G06Q10/0639
摘要： According to an example, an adaptive fault diagnosis system may include a memory storing machine readable instructions to receive metrics and events from an enterprise system, and use a substitution graph to determine if a received metric or a received event belongs to a cluster that includes one or more correlated metrics and/or events grouped based on similarity. If the received metric or the received event belongs to the cluster, the memory may further store machine readable instructions to use a detection graph to determine if the received metric or the received event is identifiable to form a fault pattern by traversing a fault path of the detection graph. Further, the memory may further store machine readable instructions to diagnose a fault based on the traversal of the fault path of the detection graph. The system may include a processor to implement the machine readable instructions.

7. 发明授权

US09588816B2 Performance interference model for managing consolidated workloads in QOS-aware clouds 有权
标题翻译：用于管理QOS感知云中的统一工作负载的性能干扰模型
公开(公告)号：US09588816B2
公开(公告)日：2017-03-07
申请号：US15134811
申请日：2016-04-21
申请人： Accenture Global Services Limited
发明人： Qian Zhu , Teresa Tung
IPC分类号： G06F15/173 , G06F15/16 , G06F9/50 , H04L29/08 , H04L12/923 , H04L12/927 , G06F9/48 , H04L12/24 , H04L12/26
CPC分类号： G06F9/5011 , G06F9/4881 , G06F9/5027 , G06F9/5072 , G06F9/5077 , G06F9/5083 , G06F2209/5019 , H04L29/08144 , H04L41/147 , H04L43/062 , H04L43/08 , H04L43/0876 , H04L43/0882 , H04L43/50 , H04L47/762 , H04L47/805 , H04L67/10 , H04L67/30 , H04L67/303
摘要： The workload profiler and performance interference (WPPI) system uses a test suite of recognized workloads, a resource estimation profiler and influence matrix to characterize un-profiled workloads, and affiliation rules to identify optimal and sub-optimal workload assignments to achieve consumer Quality of Service (QoS) guarantees and/or provider revenue goals. The WPPI system uses a performance interference model to forecast the performance impact to workloads of various consolidation schemes (e.g., consolidation strategies) usable to achieve cloud provider and/or cloud consumer goals, and uses the test suite of recognized workloads, the resource estimation profiler and influence matrix, affiliation rules, and performance interference model to perform off-line modeling to determine the initial assignment selections and consolidation strategy to use to deploy the workloads. The WPPI system uses an online consolidation algorithm, the offline models, and online monitoring to determine virtual machine to physical host assignments responsive to real-time conditions to meet cloud provider and/or cloud consumer goals.
摘要翻译：工作负载分析器和性能干扰（WPPI）系统使用经过认可的工作负载的测试套件，资源估算分析器和影响矩阵来表征未分析的工作负载，以及归属规则，以确定最佳和次优的工作负载分配，以实现消费者的服务质量（QoS）保证和/或提供者收入目标。 WPPI系统使用性能干扰模型来预测可用于实现云提供商和/或云消费者目标的各种合并方案（例如，整合策略）对工作负载的性能影响，并使用已识别工作负载的测试套件，资源估计分析器并影响矩阵，关联规则和性能干扰模型，以执行离线建模，以确定用于部署工作负载的初始分配选择和整合策略。 WPPI系统使用在线整合算法，离线模型和在线监控来根据实时情况确定虚拟机到物理主机分配，以满足云提供商和/或云消费者目标。

8. 发明申请

US20150128156A1 ANALYTICS FOR APPLICATION PROGRAMMING INTERFACES 有权
标题翻译：应用编程接口分析
公开(公告)号：US20150128156A1
公开(公告)日：2015-05-07
申请号：US14074342
申请日：2013-11-07
申请人： Accenture Global Services Limited
发明人： Qian Zhu , Teresa Tung , Benjamin Grimmer
IPC分类号： G06F9/54 , H04L29/06 , H04L29/08
CPC分类号： G06F9/541 , G06F8/74 , G06F8/75 , G06F11/302 , G06F11/3072 , G06N5/045 , G06N7/005 , H04L67/32 , H04L67/42
摘要： API analytics systems and methods are provided. Frequently occurring API usage patterns may be determined from API call data collected from across an API ecosystem. Alternatively or in addition, a classification structure, such as a decision tree, may be generated from the API usage patterns. A type of activity that resulted in a set of API calls being invoked may be determined from the classification structure. A similarity or difference between the set of API calls and the frequently occurring API usage patterns may also be determined and/or identified.
摘要翻译：提供API分析系统和方法。经常发生的API使用模式可以从API生态系统收集的API调用数据中确定。或者或另外，可以从API使用模式生成诸如决策树的分类结构。可以从分类结构确定导致调用一组API调用的一种活动。 API集合和经常出现的API使用模式之间的相似性或差异也可以被确定和/或识别。

9. 发明申请

US20160232036A1 PERFORMANCE INTERFERENCE MODEL FOR MANAGING CONSOLIDATED WORKLOADS IN QOS-AWARE CLOUDS 有权
标题翻译：用于管理QOS-AWARE云中综合工作量的性能干扰模型
公开(公告)号：US20160232036A1
公开(公告)日：2016-08-11
申请号：US15134811
申请日：2016-04-21
申请人： Accenture Global Services Limited
发明人： Qian Zhu , Teresa Tung
IPC分类号： G06F9/50 , G06F9/48 , H04L29/08
CPC分类号： G06F9/5011 , G06F9/4881 , G06F9/5027 , G06F9/5072 , G06F9/5077 , G06F9/5083 , G06F2209/5019 , H04L29/08144 , H04L41/147 , H04L43/062 , H04L43/08 , H04L43/0876 , H04L43/0882 , H04L43/50 , H04L47/762 , H04L47/805 , H04L67/10 , H04L67/30 , H04L67/303
摘要： The workload profiler and performance interference (WPPI) system uses a test suite of recognized workloads, a resource estimation profiler and influence matrix to characterize un-profiled workloads, and affiliation rules to identify optimal and sub-optimal workload assignments to achieve consumer Quality of Service (QoS) guarantees and/or provider revenue goals. The WPPI system uses a performance interference model to forecast the performance impact to workloads of various consolidation schemes (e.g., consolidation strategies) usable to achieve cloud provider and/or cloud consumer goals, and uses the test suite of recognized workloads, the resource estimation profiler and influence matrix, affiliation rules, and performance interference model to perform off-line modeling to determine the initial assignment selections and consolidation strategy to use to deploy the workloads. The WPPI system uses an online consolidation algorithm, the offline models, and online monitoring to determine virtual machine to physical host assignments responsive to real-time conditions to meet cloud provider and/or cloud consumer goals.
摘要翻译：工作负载分析器和性能干扰（WPPI）系统使用经过认可的工作负载的测试套件，资源估算分析器和影响矩阵来表征未分析的工作负载，以及归属规则，以确定最佳和次优的工作负载分配，以实现消费者的服务质量（QoS）保证和/或提供者收入目标。 WPPI系统使用性能干扰模型来预测可用于实现云提供商和/或云消费者目标的各种合并方案（例如，整合策略）对工作负载的性能影响，并使用已识别工作负载的测试套件，资源估计分析器并影响矩阵，关联规则和性能干扰模型，以执行离线建模，以确定用于部署工作负载的初始分配选择和整合策略。 WPPI系统使用在线整合算法，离线模型和在线监控来根据实时情况确定虚拟机到物理主机分配，以满足云提供商和/或云消费者目标。

10. 发明申请

US20150347201A1 ANALYTICS FOR APPLICATION PROGRAMMING INTERFACES 有权
公开(公告)号：US20150347201A1
公开(公告)日：2015-12-03
申请号：US14822189
申请日：2015-08-10
申请人： Accenture Global Services Limited
发明人： Qian Zhu , Teresa Tung , Benjamin Grimmer
IPC分类号： G06F9/54 , H04L29/06 , H04L29/08 , G06N7/00 , G06N5/04
CPC分类号： G06F9/541 , G06F8/74 , G06F8/75 , G06F11/302 , G06F11/3072 , G06N5/045 , G06N7/005 , H04L67/32 , H04L67/42
摘要： API analytics systems and methods are provided. Frequently occurring API usage patterns may be determined from API call data collected from across an API ecosystem. Alternatively or in addition, a classification structure, such as a decision tree, may be generated from the API usage patterns. A type of activity that resulted in a set of API calls being invoked may be determined from the classification structure. A similarity or difference between the set of API calls and the frequently occurring API usage patterns may also be determined and/or identified.

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