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1.发明授权公开(公告)号：US07711520B2
公开(公告)日：2010-05-04
申请号：US12116506
申请日：2008-05-07
IPC分类号： G06F11/30
CPC分类号： H04L43/16 , G06F11/3409 , G06F11/3452 , G06F11/3495 , G06F2201/81 , H04L41/046 , H04L41/5003 , H04L43/0817 , H04L43/0829 , H04L43/0852 , H04L43/0888 , H04L43/0894 , H04L67/1097
摘要： Disclosed is an autonomic abnormality detection device having a plurality of agents, a server with a one or more processors, a data storage device and a corrective actions engine. The device is adapted to detect and diagnose abnormalities in system components. Particularly, the device uses agents to track performance/workload measurements of system components and dynamically compiles a history of those performance/workload measurements for each component. In order to detect abnormalities a processor compares current performance/workload measurements for a component to the compiled histories for that component and for other components. The processor can further be adapted to determine possible causes of a detected abnormality and to report the abnormality, including the possible causes, to a corrective actions engine.
摘要翻译： 公开了具有多个代理的自主异常检测装置，具有一个或多个处理器的服务器，数据存储装置和纠正动作引擎。 该设备适用于检测和诊断系统组件中的异常。 特别地，该设备使用代理跟踪系统组件的性能/工作负载测量，并动态编译每个组件的性能/工作负载测量的历史记录。 为了检测异常，处理器将组件的当前性能/工作负载测量结果与该组件和其他组件的已编译历史进行比较。 处理器还可以适于确定检测到的异常的可能原因，并将包括可能的原因的异常报告给校正动作引擎。
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公开(公告)号：US07673189B2
公开(公告)日：2010-03-02
申请号：US11347999
申请日：2006-02-06
IPC分类号： G06F11/00
CPC分类号： G06F11/3447 , G06F11/3485 , G06F2201/86
摘要： A computer and method for problem detection and determination for automated system management in a system, wherein the method comprises monitoring system state, workload, and performance parameters of the system; comparing the monitored parameters against normal system performance behavior of the system, wherein the normal system performance behavior is maintained as a mapping of a system state and workload-to-performance parameters; summarizing performance abnormalities at a specified layer in the system as computation and data-processing attributes, wherein the performance abnormalities comprise deviations from the normal system performance behavior; correlating the performance abnormalities across multiple layers in the system using an attribute-based framework; and communicating a root-cause of the performance abnormalities.
摘要翻译： 一种用于系统中自动化系统管理的问题检测和确定的计算机和方法，其中所述方法包括监视系统的系统状态，工作负载和性能参数; 将所监视的参数与所述系统的正常系统性能行为进行比较，其中所述正常系统性能行为被维持为系统状态和工作负荷与性能参数的映射; 总结系统中指定层的性能异常，作为计算和数据处理属性，其中性能异常包括与正常系统性能行为的偏差; 使用基于属性的框架将系统中多个层的性能异常相关联; 并传达性能异常的根本原因。
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3.发明申请公开(公告)号：US20080209027A1
公开(公告)日：2008-08-28
申请号：US12116506
申请日：2008-05-07
IPC分类号： G06F15/173
CPC分类号： H04L43/16 , G06F11/3409 , G06F11/3452 , G06F11/3495 , G06F2201/81 , H04L41/046 , H04L41/5003 , H04L43/0817 , H04L43/0829 , H04L43/0852 , H04L43/0888 , H04L43/0894 , H04L67/1097
摘要： Disclosed is an autonomic abnormality detection device having a plurality of agents, a server with a one or more processors, a data storage device and a corrective actions engine. The device is adapted to detect and diagnose abnormalities in system components. Particularly, the device uses agents to track performance/workload measurements of system components and dynamically compiles a history of those performance/workload measurements for each component. In order to detect abnormalities a processor compares current performance/workload measurements for a component to the compiled histories for that component and for other components. The processor can further be adapted to determine possible causes of a detected abnormality and to report the abnormality, including the possible causes, to a corrective actions engine.
摘要翻译： 公开了具有多个代理的自主异常检测装置，具有一个或多个处理器的服务器，数据存储装置和纠正动作引擎。 该设备适用于检测和诊断系统组件中的异常。 特别地，该设备使用代理跟踪系统组件的性能/工作负载测量，并动态编译每个组件的性能/工作负载测量的历史记录。 为了检测异常，处理器将组件的当前性能/工作负载测量结果与该组件和其他组件的已编译历史进行比较。 处理器还可以适于确定检测到的异常的可能原因，并将包括可能的原因的异常报告给校正动作引擎。
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4.发明授权公开(公告)号：US07395187B2
公开(公告)日：2008-07-01
申请号：US11348010
申请日：2006-02-06
IPC分类号： G06F11/30
CPC分类号： H04L43/16 , G06F11/3409 , G06F11/3452 , G06F11/3495 , G06F2201/81 , H04L41/046 , H04L41/5003 , H04L43/0817 , H04L43/0829 , H04L43/0852 , H04L43/0888 , H04L43/0894 , H04L67/1097
摘要： Disclosed is an autonomic abnormality detection device having a plurality of agents, a server with a one or more processors, a data storage device and a corrective actions engine. The device is adapted to detect and diagnose abnormalities in system components. Particularly, the device uses agents to track performance/workload measurements of system components and dynamically compiles a history of those performance/workload measurements for each component. In order to detect abnormalities a processor compares current performance/workload measurements for a component to the compiled histories for that component and for other components. The processor can further be adapted to determine possible causes of a detected abnormality and to report the abnormality, including the possible causes, to a corrective actions engine.
摘要翻译： 公开了具有多个代理的自主异常检测装置，具有一个或多个处理器的服务器，数据存储装置和纠正动作引擎。 该设备适用于检测和诊断系统组件中的异常。 特别地，该设备使用代理跟踪系统组件的性能/工作负载测量，并动态编译每个组件的性能/工作负载测量的历史记录。 为了检测异常，处理器将组件的当前性能/工作负载测量结果与该组件和其他组件的已编译历史进行比较。 处理器还可以适于确定检测到的异常的可能原因，并将包括可能的原因的异常报告给校正动作引擎。
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公开(公告)号：US20080195369A1
公开(公告)日：2008-08-14
申请号：US11674198
申请日：2007-02-13
CPC分类号： G06F11/36 , G06F11/0709 , G06F11/079 , G06F11/3452 , G06F11/3495 , G06F2201/86
摘要： Disclosed are system and method embodiments for determining the root-causes of a performance objective violation, such as an end-to-end service level objection (SLO) violation, in a large-scale system with multi-tiered applications. This determination is made using a hybrid of component-level snapshots of the state of the system during a period in which an abnormal event occurred (i.e., black box mapping) and of known events and their causes (i.e., white-box mapping). Specifically, in response to a query about a violation (e.g., why did the response time for application a1 increase from r1 to r2), a processor will access and correlate the black-box and white-box mappings to determine a short-list of probable causes for the violation.
摘要翻译： 公开了用于在具有多层应用的大规模系统中确定诸如端到端服务级别异议（SLO）违反的性能目标违规的根本原因的系统和方法实施例。 在异常事件发生的时段（即黑框映射）以及已知事件及其原因（即，白盒映射）中，使用系统状态的组件级快照的混合来进行该确定。 具体来说，响应于关于违规的查询（例如，为什么应用的响应时间1从r 1到r 2增加），处理器将访问并关联黑盒和白盒映射以确定短 - 违规行为的可能原因清单。
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公开(公告)号：US08655623B2
公开(公告)日：2014-02-18
申请号：US11674198
申请日：2007-02-13
IPC分类号： G21C17/003
CPC分类号： G06F11/36 , G06F11/0709 , G06F11/079 , G06F11/3452 , G06F11/3495 , G06F2201/86
摘要： Disclosed are system and method embodiments for determining the root-causes of a performance objective violation, such as an end-to-end service level objection (SLO) violation, in a large-scale system with multi-tiered applications. This determination is made using a hybrid of component-level snapshots of the state of the system during a period in which an abnormal event occurred (i.e., black box mapping) and of known events and their causes (i.e., white-box mapping). Specifically, in response to a query about a violation (e.g., why did the response time for application a1 increase from r1 to r2), a processor will access and correlate the black-box and white-box mappings to determine a short-list of probable causes for the violation.
摘要翻译： 公开了用于在具有多层应用的大规模系统中确定诸如端到端服务级别异议（SLO）违反的性能目标违规的根本原因的系统和方法实施例。 在异常事件发生的时段（即黑框映射）以及已知事件及其原因（即，白盒映射）中，使用系统状态的组件级快照的混合来进行该确定。 具体来说，响应于关于违规的查询（例如，为什么应用程序a1的响应时间从r1增加到r2），处理器将访问并关联黑盒和白盒映射，以确定一个短列表 违规的可能原因。
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公开(公告)号：US07444272B2
公开(公告)日：2008-10-28
申请号：US11250066
申请日：2005-10-13
申请人： Guillermo A. Alvarez , Linda M. Duyanovich , John D. Palmer , Sandeep M. Uttamchandani , Li Yin
发明人： Guillermo A. Alvarez , Linda M. Duyanovich , John D. Palmer , Sandeep M. Uttamchandani , Li Yin
CPC分类号： G05B17/02 , G05B13/042
摘要： Embodiments herein present a method, system, computer program product, etc. for automated management using a hybrid of prediction models and feedback-based systems. The method begins by calculating confidence values of models. Next, the method selects a first model based on the confidence values and processes the first model through a constraint solver to produce first workload throttling values. Following this, workloads are repeatedly processed through a feedback-based execution engine, wherein the feedback-based execution engine is controlled by the first workload throttling values. The first workload throttling values are applied incrementally to the feedback-based execution engine, during repetitions of the processing of the workloads, with a step-size that is proportional to the confidence values. The processing of the workloads is repeated until an objective function is maximized, wherein the objective function specifies performance goals of the workloads.
摘要翻译： 本文的实施例提出了使用预测模型和基于反馈的系统的混合的自动化管理的方法，系统，计算机程序产品等。 该方法从计算模型的置信度开始。 接下来，该方法基于置信度值选择第一模型，并通过约束求解器处理第一模型以产生第一工作负载节流值。 此后，通过基于反馈的执行引擎重复处理工作负载，其中基于反馈的执行引擎由第一工作负载节流值控制。 在重复处理工作负载期间，第一个工作负载限制值将逐步应用于基于反馈的执行引擎，步长与置信度值成比例。 重复处理工作负载直到目标函数最大化，其中目标函数指定工作负载的性能目标。
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8.发明授权Stable, minimal skew resource flow control technique in large scale enterprise storage systems 失效在大型企业存储系统中稳定，最小化偏向资源流控制技术公开(公告)号：US07526552B2
公开(公告)日：2009-04-28
申请号：US11211793
申请日：2005-08-25
IPC分类号： G06F15/173 , G06F15/16
CPC分类号： G06F3/0631 , G06F3/0605 , G06F3/0613 , G06F3/067 , H04L47/27
摘要： A system and method of conducting resource flow control sessions in a computer network comprises sending a resource request from a client computer to a server computer; assigning to the client computer a flow control window, wherein a size of a flow control window is based on resources available to the server computer and a level of activity of a corresponding client computer, wherein the server computer is in any of a busy and idle state of activity; determining whether to change the size of the flow control window upon receiving the resource request based on the level of activity of the corresponding client computer and a current utilization of resources during a particular session of use; tracking a number of active sessions of use of the resources in a predetermined time window; and maintaining the flow control window with a maximum queue size per number of sessions value.
摘要翻译： 在计算机网络中进行资源流控制会话的系统和方法包括：从客户计算机向服务器计算机发送资源请求; 向客户端计算机分配流控制窗口，其中流控制窗口的大小基于可用于服务器计算机的资源和相应的客户端计算机的活动级别，其中服务器计算机处于忙碌和空闲状态 活动状态 基于相应的客户端计算机的活动级别以及特定的使用期间的资源的当前利用，确定在接收到资源请求时是否改变流量控制窗口的大小; 在预定时间窗口中跟踪资源的使用的多个活动会话; 并且每个会话值的每个维度具有最大队列大小的流控制窗口。
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公开(公告)号：US09679029B2
公开(公告)日：2017-06-13
申请号：US12942011
申请日：2010-11-08
申请人： Richard Ayala , Kavita Chavda , Sandeep Gopisetty , Seshashayee S. Murthy , Aameek Singh , Sandeep M. Uttamchandani
发明人： Richard Ayala , Kavita Chavda , Sandeep Gopisetty , Seshashayee S. Murthy , Aameek Singh , Sandeep M. Uttamchandani
IPC分类号： G06F17/30
CPC分类号： G06F17/30557
摘要： Embodiments of the present invention provide an approach for adapting an information extraction middleware for a clustered computing environment (e.g., a cloud environment) by creating and managing a set of statistical models generated from performance statistics of operating devices within the clustered computing environment. This approach takes into account the required accuracy in modeling, including computation cost of modeling, to pick the best modeling solution at a given point in time. When higher accuracy is desired (e.g., nearing workload saturation), the approach adapts to use an appropriate modeling algorithm. Adapting statistical models to the data characteristics ensures optimal accuracy with minimal computation time and resources for modeling. This approach provides intelligent selective refinement of models using accuracy-based and operating probability-based triggers to optimize the clustered computing environment, i.e., maximize accuracy and minimize computation time.
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公开(公告)号：US09329908B2
公开(公告)日：2016-05-03
申请号：US12893302
申请日：2010-09-29
申请人： Sandeep Gopisetty , Seshashayee S. Murthy , Aameek Singh , Sandeep M. Uttamchandani , David D. Weck
发明人： Sandeep Gopisetty , Seshashayee S. Murthy , Aameek Singh , Sandeep M. Uttamchandani , David D. Weck
IPC分类号： G06F15/173 , G06F9/50
CPC分类号： H04L43/10 , G06F9/5083 , G06F2209/508 , H04L67/10
摘要： The present invention proactively identifies hotspots in a cloud computing environment through cloud resource usage models that use workload parameters as inputs. In some embodiments the cloud resource usage models are based upon performance data from cloud resources and time series based workload trend models. Hotspots may occur and can be detected at any layer of the cloud computing environment, including the server, storage, and network level. In a typical embodiment, parameters for a workload are identified in the cloud computing environment and inputted into a cloud resource usage model. The model is run with the inputted workload parameters to identify potential hotspots, and resources are then provisioned for the workload so as to avoid these hotspots.
摘要翻译： 本发明通过使用工作负载参数作为输入的云资源使用模型主动地识别云计算环境中的热点。 在一些实施例中，云资源使用模型基于来自云资源和基于时间序列的工作负载趋势模型的性能数据。 热点可能发生，可以在云计算环境的任何层面检测，包括服务器，存储和网络级别。 在典型的实施例中，工作负载的参数在云计算环境中被识别并被输入到云资源使用模型中。 该模型使用输入的工作负载参数运行，以识别潜在的热点，然后为工作负载提供资源，以避免这些热点。
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