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    • 1. 发明申请
    • METHOD AND APPARATUS FOR ACCELERATION BY PREFETCHING ASSOCIATED OBJECTS
    • 通过预先设定相关对象加速的方法和装置
    • WO2008112649A1
    • 2008-09-18
    • PCT/US2008/056431
    • 2008-03-10
    • RIVERBED TECHNOLOGY, INC.HUANG, CharlesGUPTA, NitinKESWANI, VivasvatROBINSON, Bart
    • HUANG, CharlesGUPTA, NitinKESWANI, VivasvatROBINSON, Bart
    • G06F15/16
    • H04L67/28
    • Association information is used to build association trees to associate base pages and embedded objects at a proxy. An association tree has a root node containing a URL for a base page, and zero or more leaf nodes each containing a URL for an embedded object. In most cases an association tree will maintain the invariant that all leaves contain distinct URLs. However, it is also possible to have an association tree in which the same URL appears in multiple nodes Given a number of association trees and a base-page URL, a prefetch system finds the root or Interior node corresponding to that URL (if any) and traverses the tree from that node, prefetching URLs until the URL of the last leaf node is prefetched. The prefetching starts the process of bringing over the various embedded objects before the user or program would ordinarily fetch them.
    • 关联信息用于构建关联树以将代理服务器的基本页面和嵌入的对象关联。 关联树具有包含基页的URL的根节点,以及包含嵌入对象的URL的零个或多个叶节点。 在大多数情况下,关联树将保留所有叶包含不同URL的不变量。 然而,也可能有一个关联树,其中相同的URL出现在多个节点中。给定多个关联树和基页URL,预取系统找到与该URL对应的根节点或内部节点(如果有的话) 并从该节点遍历树,预取URL直到预取最后一个叶节点的URL。 预取开始在用户或程序通常抓取它们之前引导各种嵌入对象的过程。
    • 3. 发明申请
    • AUTOMATIC FRAMING SELECTION
    • 自动框架选择
    • WO2007016236A2
    • 2007-02-08
    • PCT/US2006/029181
    • 2006-07-26
    • RIVERBED TECHNOLOGY, INC.WU, David Tze-SiLASSEN, SorenSUBBANA, KartikGUPTA, NitinKESWANI, Vivasvat
    • WU, David Tze-SiLASSEN, SorenSUBBANA, KartikGUPTA, NitinKESWANI, Vivasvat
    • H04J11/00
    • 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,并自动确定并应用最佳框架启发式。 构筑启发式规则为构建网络流量指定了不同的规则。 在网络流量应用框架启发式的同时,针对网络流量推测性地评估替代框架启发式。 这些评估的结果被用来排列框架启发式。 选择具有最佳排名的框架启发式用于构建后续网络流量。 每个客户端/服务器通信流可能具有单独的框架启发式。 基于字节计数和/或时间或者基于指示帧出现的合理点的业务特性,帧启发法可以是确定性的。 可用框架启发式的选择可部分由手动配置来确定,该手动配置指定哪些框架启发式可用,并且部分由自动过程确定,以从可用框架启发式集合中确定适用于当前网络流量的最佳框架启发式。 / p>

    • 5. 发明申请
    • SERIAL CLUSTERING
    • 串行集群
    • WO2007055757A2
    • 2007-05-18
    • PCT/US2006/029082
    • 2006-07-26
    • RIVERBED TECHNOLOGY, INC.WU, 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。
    • 6. 发明申请
    • CONGESTION MANAGEMENT OVER LOSSY NETWORK CONNECTIONS
    • 通过丢失网络连接阻止管理
    • WO2007016311A2
    • 2007-02-08
    • PCT/US2006/029337
    • 2006-07-26
    • RIVERBED TECHNOLOGY, INC.MCCANNE, StevenGUPTA, Nitin
    • MCCANNE, StevenGUPTA, Nitin
    • G01R31/08
    • 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,该分组丢失分析器区分由于拥塞和由于网络连接的有损性而导致的分组丢失。 损失分析仪观察包丢失模式,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FEC已被使用,丢失分析器确定丢失的数据包是否可能被接收网络设备恢复。 如果丢失的分组可以通过FEC纠正,则丢失分析仪假定没有网络拥塞存在,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。 如果丢失分析仪确定某些丢失的数据包不能被接收网络设备恢复,则丢失分析仪会认为网络拥塞导致这些数据包丢失并降低数据速率。
    • 8. 发明申请
    • VIRTUALIZED DATA STORAGE SYSTEM ARCHITECTURE
    • 虚拟化数据存储系统架构
    • WO2010111312A2
    • 2010-09-30
    • PCT/US2010/028375
    • 2010-03-23
    • RIVERBED TECHNOLOGY, INC.WU, David Tze-SiMCCANNE, StevenDEMMER, Michael J.GUPTA, Nitin
    • WU, David Tze-SiMCCANNE, StevenDEMMER, Michael J.GUPTA, Nitin
    • G06F15/16
    • 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.
    • 虚拟存储阵列将通过广域网连接的数据中心的分支数据存储整合。 虚拟存储阵列对存储客户端显示为本地数据存储; 然而,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,然后将存储块缓存在分支位置。 虚拟存储阵列利用对与存储块相关联的高级数据结构的语义和结构的理解,以预测存储客户端在不久的将来可能要求哪些存储块。 虚拟存储阵列确定所请求的存储块和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附加高级数据结构实体。 从此,虚拟存储阵列识别用于预取的附加存储块。