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    • 1. 发明申请
    • SERIAL CLUSTERING
    • 串行集群
    • WO2007055757A3
    • 2008-01-17
    • PCT/US2006029082
    • 2006-07-26
    • RIVERBED TECHNOLOGY INCWU DAVID TZE-SIGUPTA NITINLY KAND
    • WU DAVID TZE-SIGUPTA NITINLY KAND
    • H04J1/16
    • H04L47/12H04L47/10H04L47/125H04L69/16H04L69/162H04L69/165H04L69/32
    • SERIAL CLUSTERING ABSTRACT OF THE DISCLOSURE Serial clustering uses two or more network devices connected in series via a local and/or wide-area network to provide additional capacity when network traffic exceeds the processing capabilities of a single network device. When a first network device reaches its capacity limit, any excess network traffic beyond that limit is passed through the first network device unchanged. A network device connected in series with the first network device intercepts and will process the excess network traffic provided that it has sufficient processing capacity. Additional network devices can process remaining network traffic in a similar manner until all of the excess network traffic has been processed or until there are no more additional network devices. Network devices may use rules to determine how to handle network traffic. Rules may be based on the attributes of received network packets, attributes of the network device, or attributes of the network.
    • 串行集群摘要串行集群使用两个或多个通过本地和/或广域网串联连接的网络设备,以便在网络流量超过单个网络设备的处理能力时提供额外的容量。 当第一个网络设备达到其容量限制时,超出该限制的任何超量网络流量不变地通过第一个网络设备。 与第一网络设备串联连接的网络设备将拦截并处理多余的网络流量,前提是它具有足够的处理能力。 额外的网络设备可以以类似的方式处理剩余的网络流量,直到已经处理了所有超量的网络流量,或者直到没有更多的额外的网络设备。 网络设备可能会使用规则来确定如何处理网络流量。 规则可以基于接收的网络分组的属性,网络设备的属性或网络的属性。
    • 2. 发明申请
    • SERIAL CLUSTERING
    • 串行集群
    • WO2007055757B1
    • 2008-02-28
    • PCT/US2006029082
    • 2006-07-26
    • RIVERBED TECHNOLOGY INCWU DAVID TZE-SIGUPTA NITINLY KAND
    • WU DAVID TZE-SIGUPTA NITINLY KAND
    • H04J1/16
    • H04L47/12H04L47/10H04L47/125H04L69/16H04L69/162H04L69/165H04L69/32
    • Serial clustering uses two or more network devices connected in series via a local and/or wide-area network to provide additional capacity when network traffic exceeds the processing capabilities of a single network device. When a first network device reaches its capacity limit, any excess network traffic beyond that limit is passed through the first network device unchanged. A network device connected in series with the first network device intercepts and will process the excess network traffic provided that it has sufficient processing capacity. Additional network devices can process remaining network traffic in a similar manner until all of the excess network traffic has been processed or until there are no more additional network devices. Network devices may use rules to determine how to handle network traffic. Rules may be based on the attributes of received network packets, attributes of the network device, or attributes of the network.
    • 当网络流量超过单个网络设备的处理能力时,串行集群使用通过本地和/或广域网络串联连接的两个或多个网络设备来提供额外的容量。 当第一个网络设备达到其容量限制时,超出该限制的任何超量网络流量不会通过第一个网络设备。 与第一网络设备串联连接的网络设备将拦截并处理多余的网络流量,只要其具有足够的处理能力即可。 额外的网络设备可以以类似的方式处理剩余的网络流量,直到已经处理了所有超量的网络流量,或者直到没有更多的额外的网络设备。 网络设备可能会使用规则来确定如何处理网络流量。 规则可以基于接收的网络分组的属性,网络设备的属性或网络的属性。
    • 3. 发明申请
    • VIRTUALIZED DATA STORAGE SYSTEM ARCHITECTURE
    • 虚拟化数据存储系统体系结构
    • WO2010111312A3
    • 2010-12-29
    • PCT/US2010028375
    • 2010-03-23
    • RIVERBED TECHNOLOGY INCWU DAVID TZE-SIMCCANNE STEVENDEMMER MICHAEL JGUPTA NITIN
    • WU DAVID TZE-SIMCCANNE STEVENDEMMER MICHAEL JGUPTA NITIN
    • G06F13/00
    • G06F17/30233G06F3/0643G06F3/0653G06F3/067G06F12/0862G06F17/30132G06F2212/6024
    • Virtual storage arrays consolidate branch data storage at data centers connected via wide area networks. Virtual storage arrays appear to storage clients as local data storage; however, virtual storage arrays actually store data at the data center. The virtual storage arrays overcomes bandwidth and latency limitations of the wide area network by predicting and prefetching storage blocks, which are then cached at the branch location. Virtual storage arrays leverage an understanding of the semantics and structure of high-level data structures associated with storage blocks to predict which storage blocks are likely to be requested by a storage client in the near future. Virtual storage arrays determine the association between requested storage blocks and corresponding high-level data structure entities to predict additional high-level data structure entities that are likely to be accessed. From this, the virtual storage array identifies the additional storage blocks for prefetching.
    • 虚拟存储阵列整合了通过广域网连接的数据中心的分支数据存储。 虚拟存储阵列似乎将存储客户端视为本地数据存储; 但是,虚拟存储阵列实际上将数据存储在数据中心。 虚拟存储阵列通过预测和预取存储块来克服广域网的带宽和延迟限制,然后将这些存储块缓存到分支位置。 虚拟存储阵列充分了解与存储块关联的高级数据结构的语义和结构,以预测存储客户端在不久的将来可能需要哪些存储块。 虚拟存储阵列确定请求的存储块与相应的高级数据结构实体之间的关联,以预测可能被访问的其他高级数据结构实体。 由此,虚拟存储阵列识别用于预取的附加存储块。
    • 4. 发明申请
    • AUTOMATIC FRAMING SELECTION
    • 自动框架选择
    • WO2007016236A3
    • 2007-05-10
    • PCT/US2006029181
    • 2006-07-26
    • RIVERBED TECHNOLOGY INCWU DAVID TZE-SILASSEN SORENSUBBANA KARTIKGUPTA NITINKESWANI VIVASVAT
    • WU DAVID TZE-SILASSEN SORENSUBBANA KARTIKGUPTA NITINKESWANI VIVASVAT
    • H04J3/16
    • H04L47/27H04L12/28H04L43/16H04L47/26H04L69/16H04L69/163
    • Network traffic is monitored and an optimal framing heuristic is automatically determined and applied. Framing heuristics specify different rules for framing network traffic. While a framing heuristic is applied to the network traffic, alternative framing heuristics are speculatively evaluated for the network traffic. The results of these evaluations are used to rank the framing heuristics. The framing heuristic with the best rank is selected for framing subsequent network traffic. Each client/server traffic flow may have a separate framing heuristic. The framing heuristics may be deterministic based on byte count and/or time or based on traffic characteristics that indicate a plausible point for framing to occur. The choice of available framing heuristics may be determined partly by manual configuration, which specifies which framing heuristics are available, and partly by automatic processes, which determine the best framing heuristic to apply to the current network traffic from the set of available framing heuristics.
    • 监控网络流量,并自动确定和应用最佳的框架启发式。 成帧启发式规定了组网网络流量的不同规则。 虽然将框架启发式应用于网络流量,但是针对网络流量推测性地评估了替代成帧启发式算法。 这些评估的结果用于对框架启发式进行排序。 选择具有最佳排名的成帧启发式来构建后续网络流量。 每个客户端/服务器流量流可以具有单独的成帧启发式。 帧启发式可以是基于字节计数和/或时间的确定性的,或者基于指示发生框架的合理点的业务特性。 可选帧启发式可以部分地通过手动配置来确定,该手动配置指定哪些帧启发式是可用的,并且部分地由自动进程确定,自动进程决定了应用于可用成帧启发式组的当前网络业务的最佳成帧启发式。
    • 7. 发明申请
    • CONGESTION MANAGEMENT OVER LOSSY NETWORK CONNECTIONS
    • 通过网络连接失败的控制管理
    • WO2007016311A3
    • 2007-05-03
    • PCT/US2006029337
    • 2006-07-26
    • RIVERBED TECHNOLOGY INCMCCANNE STEVENGUPTA NITIN
    • MCCANNE STEVENGUPTA NITIN
    • H04J1/16H04J3/14
    • H04L47/10H04L1/0002H04L1/004H04L1/0057H04L1/1809H04L1/187H04L1/188H04L43/0835H04L43/0847H04L47/11H04L47/25H04L47/27
    • A network stack includes a packet loss analyzer that distinguishes between packet losses due to congestion and due to lossyness of network connections. The loss analyzer observes the packet loss patterns for comparison with a packet loss model. The packet loss model may be based on a Forward Error Correction (FEC) system. The loss analyzer determines if lost packets could have been recovered by a receiving network device, if FEC had been used. If the lost packets could have been corrected by FEC, the loss analyzer assumes that no network congestion exists and that the packet loss comes from the lossy aspects of the network, such as radio interference for wireless networks. If the loss analyzer determines that some of the lost packet could not have been recovered by the receiving network device, the loss analyzer assumes that network congestion causes these packet losses and reduces the data rate.
    • 网络堆栈包括分组丢失分析器,其区分由于拥塞引起的分组丢失以及由于网络连接的有损性。 损耗分析仪观察丢包模式,以便与丢包模型进行比较。 分组丢失模型可以基于前向纠错(FEC)系统。 如果使用了FEC,丢失分析器将确定丢失的数据包是否已被接收网络设备恢复。 如果丢失的分组可能被FEC纠正,则丢失分析器假设没有网络拥塞,并且分组丢失来自网络的有损方面,例如无线网络的无线电干扰。 如果丢失分析器确定某些丢失的分组不能被接收网络设备恢复,则丢失分析器假设网络拥塞导致这些分组丢失并降低数据速率。
    • 10. 发明申请
    • METHOD AND SYSTEM FOR MEASURING AND ANALYZING THE PRODUCTION DATA DIRECTLY FROM MACHINE
    • 用于直接从机器测量和分析生产数据的方法和系统
    • WO2010092588A2
    • 2010-08-19
    • PCT/IN2009000296
    • 2009-05-22
    • GUPTA NITINKOTWAL RUPESH VASANT
    • GUPTA NITINKOTWAL RUPESH VASANT
    • G05B23/0235Y02P90/86
    • The present invention relates to a method for measuring and analyzing the production data directly from machines. The said method comprises the steps of : a. measuring the parameters relating to machine and parameters of the process to be carried on by machine; b. storing values of parameters measured for the analysis purpose; c. providing standard values of said parameters being provided by the user; d. comparing said standard values and the measured values of the parameters for checking performance of machine and consistency in product quality; e. deciding whether the machine performance and product quality are consistently maintained to required standards; and f. displaying the standard values and measured values of said parameters, and decision taken in respect of machine performance and product quality. Also the system for implementing the aforesaid method is disclosed.
    • 本发明涉及直接从机器测量和分析生产数据的方法。 所述方法包括以下步骤:a。 测量与机器相关的参数和机器进行过程的参数; 湾 存储为分析目的而测量的参数值; C。 提供由用户提供的所述参数的标准值; 天。 比较所述标准值和用于检查机器性能和产品质量一致性的参数的测量值; 即 决定机器性能和产品质量是否始终保持到要求的标准; 和f。 显示所述参数的标准值和测量值,以及关于机器性能和产品质量的决定。 另外还公开了用于实现上述方法的系统。