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`US 20130322242A1
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`United States
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`a2) Patent Application Publication co) Pub. No.: US 2013/0322242 Al
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`Swensonetal.
`(43) Pub. Date:
`Dec. 5, 2013
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`(54) REAL-TIME NETWORK MONITORING AND
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`SUBSCRIBER IDENTIFICATION WITH AN
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`ON-DEMAND APPLIANCE
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`(71)
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`(72)
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`(21)
`(22)
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`(52) U.S. CL
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`CPC ween HO4W 28/06 (2013.01); HO4W 24/00
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`(2013.01)
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`USPC iecccccesectseessesesescssenenes 370/232; 370/252
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`(67)
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`ABSTRACT
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`Applicant: Skyfire Labs, Inc., Mountain View, CA
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`(US)
`Inventors: Erik R. Swenson, San Jose, CA (US);
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`Nitin Bhandari, Fremont, CA (US)
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`Asystem and a methodare disclosed for selectively monitor-
`Appl. No.: 13/907,847
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`ing trallic in a service provider network. The system receives
`a ——
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`a notice for a beginning of a network data flow, which
`May31, 2013
`Filed:
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`responds to a request from a user device for content at an
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`origin server. The systemthen determines whether to monitor
`Related U.S. Application Data
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`(60) Provisional application No. 61/654,689, filed on Jun,_the data flow from theorigin server to the user device. Tf’ so
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`1, 2012, provisionalapplication No. 61/754,391. filed
`determined, the system collects statistic information of the
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`on Jan. 18. 2013.
`data flowandstores the statistic information to a flow record
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`in a database. The system also maps the flowrecord to a
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`subscriber of the service provider network by analyzing the
`Publication Classification
`statistic information ofthe data flow and estimates bandwidth
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`provided to the data flow by the service provider’s network
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`based on the analysis ofthe statistic informationofthe data
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`flow.
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`(51)
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`Int. Cl.
`HOAW 28/06
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`(2006.01)
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`Exhibit 1007
`Cisco v. Orckit — IPR2023-00554
`Page 1 of 23
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`Exhibit 1007
`Cisco v. Orckit – IPR2023-00554
`Page 1 of 23
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`

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`US 2013/0322242 Al
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`Page 2 of 23
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`Patent Application Publication
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`Dec. 5, 2013 Sheet 2 of 8
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`US 2013/0322242 Al
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`PROCESSOR
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`Exhibit 1007
`Cisco v. Orckit — IPR2023-00554
`Page 3 of 23
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`

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`Patent Application Publication
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`Dec. 5, 2013 Sheet 3 of 8
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`Exhibit 1007
`Cisco v. Orckit — IPR2023-00554
`Page 4 of 23
`
`Exhibit 1007
`Cisco v. Orckit – IPR2023-00554
`Page 4 of 23
`
`

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`Patent Application Publication
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`Dec. 5, 2013 Sheet 4 of 8
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`US 2013/0322242 Al
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`Exhibit 1007
`Cisco v. Orckit — IPR2023-00554
`Page 5 of 23
`
`Exhibit 1007
`Cisco v. Orckit – IPR2023-00554
`Page 5 of 23
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`

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`Patent Application Publication
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`Dec. 5, 2013 Sheet 5 of 8
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`Exhibit 1007
`Cisco v. Orckit — IPR2023-00554
`Page 6 of 23
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`Exhibit 1007
`Cisco v. Orckit – IPR2023-00554
`Page 6 of 23
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`Cisco v. Orckit — IPR2023-00554
`Page 7 of 23
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`Cisco v. Orckit – IPR2023-00554
`Page 7 of 23
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`Exhibit 1007
`Cisco v. Orckit — IPR2023-00554
`Page 8 of 23
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`Exhibit 1007
`Cisco v. Orckit – IPR2023-00554
`Page 8 of 23
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`

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`Exhibit 1007
`Cisco v. Orckit — IPR2023-00554
`Page 9 of 23
`
`

`

`
`
`US 2013/0322242 Al
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`
`
`Dec. 5, 2013
`
`
`
`REAL-TIME NETWORK MONITORING AND
`
`
`
`SUBSCRIBER IDENTIFICATION WITH AN
`
`
`
`
`ON-DEMAND APPLIANCE
`
`
`
`
`
`CROSS REFERENCE TO RELATED
`
`
`
`APPT.ICATIONS
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`[0001] This application claims the benefit of U.S. Provi-
`
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`
`
`
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`sional Application No. 61/654,689 filed on Jun. 1, 2012 and
`
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`
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`U.S. Provisional Application No. 61/754,391 filed on Dec.
`
`
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`
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`21, 2012, both ofwhichare incorporated byreference in their
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`entirety.
`
`BACKGROUND
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`[0002]
`1. Field ofArt
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`[0003] The disclosure generally relates to improving user
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`experience on a network, and more specifically, to monitoring
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`bandwidth consumption of the manydevices connected to a
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`given node in the network.
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`[0004]
`2. Description of the Related Art
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`[0005] Mobile devices, such as smart phones andtablets,
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`have becomeprevalent in recent years. Giventhe fast advance
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`in mobile computing power and far-reaching wireless Inter-
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`het access, more and more users view streamed videos on
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`their mobile devices. The detection ofnetwork congestion has
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`become increasingly important
`for network operators
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`attempting to maximize user experience on the network. Even
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`as network operators are ever increasing the capacity of their
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`networks, the demand for bandwidth is growing at an even
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`faster pace. Managing network growth and dealing with con-
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`gestion in the infrastructure is particularly important in the
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`mobile space becauseof the high cost of radio spectrum and
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`radio access network (RAN) equipmentutilized by wireless
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`mobile networks. These high costs prevent mobile service
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`providers from engineering excess capacity into each net-
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`work access point through the purchase of additional RAN
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`infrastructure. ‘he samesituation can, however, also happens
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`to other types of network infrastructure.
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`[0006] Existing network clements can give opcrators a
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`view into the current state oftraffic in their network, but they
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`do not provide a measure of “goodness,” i-c., how much
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`elasticity is left or how much more dala can the network
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`handle. This measure is important for multimedia content
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`delivery since a good user experience usually depends on the
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`network’s ability to deliver data in a reliable and sustainable
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`fashion. A minimumdata rate is required to prevent stalling
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`and re-buffering during the streaming ofmultimedia content,
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`hence ensuring sufficient bandwidthis important to quality of
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`experience. Typically, multimedia content providers are suf-
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`ficiently equipped to deliver multimedia content at levels far
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`beyond the capabilities of wireless intrastructure. Hence, the
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`burden falls on wireless service providers to implementnet-
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`work data optimization to ease the traffic burden and maxi-
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`mize the experience of each and every user on the network.
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`Currently, however, mobile service providersare often forced
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`0 use very coarse tools that havelittle visibility into which
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`network segments are congested andtend to apply optimiza-
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`ion to flows that may not need any optimization.
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`[0007] Typically, mobile service providers use inline net-
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`work appliances that monitorevery bit of subscribertraffic in
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`order to make estimates of network throughput. This puts a
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`huge burden on the system since it must scale to handle
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`hundreds of thousands to millions of network requests per
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`second through a single network access point. Furthermore,
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`network service providers often must utilize these monitoring
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`techniques ona micro-scale (c.g., per RAN equipment instal-
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`lation) in orderto react to the condition ofthe network, which
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`results in increased cast. In addition, a large portion of web
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`traflic consists of small object requests, which can obscure
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`network monitoring at any level due to their short lifetime and
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`bursty characteristics.
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` BRIEF DESCRIPTION OF DRAWINGS
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`[0008] The disclosed embodiments have other advantages
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`and features which will be more readily apparent [rom the
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`detailed description, the appended claims, and the accompa-
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`nying figures (or drawings). A briefintroductionofthe figures
`is below.
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`[0009]
`FIG. 1 illustrates a high-level block diagram of an
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`example communications environment for selective on-de-
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`mand real-time network monitoring and subscriber identifi-
`cation.
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`[0010]
`FIG. 2 illustrates one embodiment of components of
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`an example machine able to read instructions from a machine-
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`readable medium and execute them in a processor (or con-
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`FIG. 3 illustrates one embodiment of an example
`[0011]
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`architecture of a network controller for providing selective
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`real-time network monitoring and subscriber identification.
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`FIGS. 4A-4B illustrate embodiments of example
`[0012]
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`working modesofthe network controller for providing selec-
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`tive on-demand network monitoring and subscriber identifi-
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`[0013]
`FIG. 5 illustrates onc example embodimentof event
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`trace ofan example network controller in “continue” working
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`modefor selective on-demandreal-time network monitoring
`and subscriberidentification.
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`[0014]
`FIG. 6 illustrates one example embodimentof event
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`trace of an example networkcontroller in “counting” working
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`mode for selective on-demandreal-time network monitoring
`and subscriberidentification.
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`[0015]
`FIG. 7 illustrates one embodiment of components of
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`an example flow cache managed bya network controller.
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`DETAILED DESCRIPTION
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`[0016] The Iigures ([IGS.) and the following description
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`relate to preferred embodiments by way ofillustration only.It
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`should be noted that from the following discussion, alterna-
`tive embodiments of the structures and methods disclosed
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`herein will be readily recognized as viable alternatives that
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`may be employed without departing from the principles of
`whatis claimed.
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`[0017] Reference will now be made in detail to several
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`embodiments, examples of which are illustrated in the
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`accompanying figures. It is noted that wherever practicable
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`similar orlike reference numbers may be used in the figures
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`and may indicate similar or like functionality. The figures
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`depict embodimentsof the disclosed system (or method)for
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`purposesofillustration only. One skilled in the art will readily
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`recognize from the following description that alternative
`embodiments of the structures and methodsillustrated herein
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`may be employed without departing from the principles
`described herein.
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`Overview
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`[0018] Embodiments disclosed include a network control-
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`ler system for real-time data gathering on thestate of existing
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`Exhibit 1007
`Cisco v. Orckit — IPR2023-00554
`Page 10 of 23
`
`Exhibit 1007
`Cisco v. Orckit – IPR2023-00554
`Page 10 of 23
`
`

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`the origin server latency and detecting, network congestion
`network traffic flows and mapping flow data to respective
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`more accurately than small flows. For example, a reasonable
`users in the network ta predict available bandwidth and level
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`size threshold for separating a large object from a small object
`of congestion. By gathering a history offlow statistics in the
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`can be set between 512 kB to 1 MB, and 50 kB and up for
`network, the network controller system establishesa relation-
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`medium-sized objects. Other valuesare also possible.
`ship between base stations (or other network segments) and
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`In some embodiments, the bandwidth attained by a
`[0021]
`their capability to deliver the amount of data typically
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`single [low may be sullicient to determinethe capacity olf the
`required bya particular user ofthe network. The very recent
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`network segment(s) the flow traverses. Therefore, with a
`history ofnetwork flowscan be usedto predict the near future
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`fairly small deploymentofnetwork controller(s), an accurate
`congestionsin a substantiallyreal-time fashion. Furthermore,
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`detection of key network congestion points can be derived.
`the history of flow statistics can be used to build a long-term
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`Specifically, one does not need to monitor every flow going
`map of user behavior on the network, which can moreeffec-
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`tively predict on demand data delivery requirements for the throughanetwork segment to detect congestions. Since video
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`collection ofusers utilizing a given network access point in a
`currently comprises around 50%ofthetraffic on a network
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`consistent manner. The network controller keeps a flow state
`but only around 5% of total flows, a relatively small number
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`database, which groups flow data in a number of ways, such
`of flow samples of large objects can mapa statistically sig-
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`as onperstation/cell tower, per subscriber, per time-of-day, or
`nificant portion of the network.
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`per geography arca basis. As newflows are presented to the
`[0022] The unpredictable and transient nature of network
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`systemfor inspection, database can be queried to estimate the
`congestion means that mitigation of the network congestion
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`network congestion level for new flows to determine whether
`will be too late if not acted upon in near real-time after
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`existing, new or future [lows require optimizations in orderto
`congestionis detected. In one embodiment, the network con-
`maintain the desired level of user satisfaction.
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`troller selects large video or image flows through an on-
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`demandvideo optimizer to optimize large object delivery and
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`In one embodiment, an on-demand network moni-
`[0019]
`thus available network bandwidth. With the controller and/or
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`lormg methodis adopted to gather data about network [lows
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`optimizerto intelligently and selectively handle the measure-
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`as theytraverse the network. For example, network flows can
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`ment and optimization, these operations are offloaded from
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`be monitored selectively or on-demand based onthe types of
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`network routing appliances.
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`the content carried in the flows. Furthermore, the network
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`monitoring can also be performedselectively at inline level,
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`as well as out-of-band to improveefficiency. Both TCP and
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`UDPflows are monitored to gather information about the
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`state ofthe network, such as the average network throughput
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`for each flowand end-to-endlatency between, for example, a
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`client device and anorigin server providing multimedia con-
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`tent to the client device. For each TCP or UDP flow, the
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`systemtracks the numberof bytes sent (and in some embodi-
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`ments acknowledged). In TCP, the current window size may
`also be tracked. Records on network flowsare storedin a flow
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`statistics database, which can be indexed bysubscriber iden-
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`tification (ID),cell tower (basestation), and network segment
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`etc. As many flowrecords accumulate, this database repre-
`sents both historical and current network condition and
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`capacity for delivering data. Network throughput can be mea-
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`sured by calculating an average number of bytes delivered
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`overa period oftime. Steps maybe takentofilter out spurious
`data from small flows with size less than a certain threshold
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`hat, when measured, cause very noisy results in measuring
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`bandwidth and/or latency. For example, any flow having
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`delivery tune of less than 500 mscanbe filtered.
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`[0020]
`In another embodiment,large objects, such as video
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`files and data, traversing the network are monitored and
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`paced. Rather than just measuring the bandwidth associated
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`with large object delivery, estimates for future bandwidth
`needs ofthe network are determined based on the measure-
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`ments. In turn, large abjects may be selectively optimized to
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`preserve network throughput. For video objects, streaming
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`bit rate of the flow provides a parameter, which can be com-
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`pared against network capacily lo determineifthe network is
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`able to sustain the required level of throughput. It is often
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`advantageous to pace the transfer speed to not exceeding a
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`known cap. If a flow can be delivered at a rate between the
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`minimumlevel neededto keep the video from stalling and the
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`upperlimit of the pacing limit, then the network segment on
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`whichthe flow traversesis said to be capable of sustainthe bit
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`rate required for the flow. Large objectslike video and images
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`take a period oftimeto be delivered, which aids in measuring
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`Real-Time Traffic Monitoring
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`[0023]
`FIG. 1 illustrates a high-level block diagram ofan
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`example communications environment 100 for selective on-
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`demandreal-time network monitoring and subscriber identi-
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`fication. The environment 100 comprises user devices 110, an
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`origin server 160, a steering device 130, a networkcontroller
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`140, a video optimizer 150, and a network 120. The network
`120 is a communication networkthat transmits data between
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`the user devices 110, the steering device 130 and the origin
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`server 160 and/or the video optimizer 150. In one embadi-
`ment the network 120 includes wireless network and the
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`Internet.
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`[0024] A networkefficiencystrategy that aspires to keep
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`capital expenditure from outpacing revenues has to be bal-
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`anced with demands from consumers for better user experi-
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`ences that rely increasingly on higher data usage. loday,
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`mobile operators are employing a varictyof tools to manage
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`capacity including data usage caps, Wi-Fi offload and intel-
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`ligent optimization. The environment 100 demonstrates such
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`a solution that provides a unified foundation with deep ses-
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`sion intelligence,
`integrated services management, and
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`dynamic adaptabilityto fit any service offering. Together, the
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`network controller 140 and the video optimizer 150 deliver a
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`world-class media optimization solution that bringsa surgical
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`capacity advantage to wireless operators as well as Internet
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`service providers withbetter peak capacity savings thanalter-
`native solutions.
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`[0025]
`In one embodiment, the user devices 110 are com-
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`puling devices with network capabilities. Oftentimes, for
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`example, the user devices 110 are wireless enabled mobile
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`computing device with a web browser and media display
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`capability. The user devices 110 as mobile computing devices
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`mayinclude laptops, netbooks, tablets, smart telephones, or
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`personal digital assistants (PDAs). While only two user
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`devices 110A and 110Bareillustrated in FTG.1, the environ-
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`ment 100 mayinclude thousandsor millions of such devices.
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`The web browsers maybe software applications running on
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`US 2013/0322242 Al
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`Dec. 5, 2013
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`Exhibit 1007
`Cisco v. Orckit — IPR2023-00554
`Page 11 of 23
`
`Exhibit 1007
`Cisco v. Orckit – IPR2023-00554
`Page 11 of 23
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`US 2013/0322242 Al
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`Dec. 5, 2013
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`monitoring and optimization. Knowledge on the current net-
`mobile devices 110 for retrieving web content fromthe origin
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`work state, such as congestion, deemscritical when it comes
`server 160 and presenting the web content on a display
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`to data optimization.
`coupled to the mobile device. Web content accessed by the
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`user devices 110 include text, images, audio and video con-
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`[0029] Asaflowis sent to the network controller 140 for
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`tent. The multimedia content can be played back by the
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`inspection, historical network traffic data stored at the net-
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`browsers, for example, HTML5 compatible browsers, plug-
`workcontroller 140 mayhe searched. Thehistorical network
`traffic data includes information such as subscriber informa-
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`in ora standalone media player. The browsers can also invoke
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`tion, the cell towers to which the user devices attached, rout-
`the media players or plug-ins available on the user devices
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`110 and passes images, audio and/or video to the media
`ers through whichthe traffic is passing, geographyregions,
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`the backhaul scgments, and time-of-day of the flows. For
`player or plug-in for playback.
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`example, in a mobile network, the cell tower to which a user
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`Thesteering device 130 maybe a load balancer or a
`[0026]
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`device is attached can be most useful, sinceit is the location
`router located between the user device 110 and the network
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`where most congestion occurs due to limited bandwidth and
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`120. The steering device 130 provides the user device 110
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`high cost of the radio access network infrastructure. The
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`network controller 140 looksinto the historicaltraffic data for
`with access to the network and thus, provides the gateway
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`through which the user devicetraffic flows onto the network
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`the average of the bandwidth per user at the particular cell
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`tower. The network controller 140 can then estimate the
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`and vice versa. In one embodiment, the steering device 130
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`categorizes traffic routed throughit to identify flows ofinter-
`amountof bandwidthor degree ofcongestionfor the new flow
`basedonthe historical record.
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`est for further inspection al the network controller 140. Alter-
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`natively, the network controller 140 interfaces with the steer-
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`[0030] The video optimizer 150 is a computer server that
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`device
`130 to
`coordinate
`the monitoring and
`ing
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`provides video and image optimization and delivers opti-
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`categorization of network traffic, such as identifying large
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`mized video and image content to the user devices 110 via the
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`and small objects in HTTPtraffic flows. In this case, the
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`network 120. The video and image optimization is an on-
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`steering device 130 receives instructions from the network
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`demand service provided through the transcoding of the
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`controller 140 based on the desired criteria for categorizing
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`video and image content. For example, when a user device
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`flows of interest for further inspection.
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`attempts to retrieve video from the origin server 160, the
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`network controller 140 may decide that the flow mectscertain
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`[0027] However, information on the wireless/cellular user
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`criteria for content optimization. ‘The network controller 140
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`devices 110 side is often not available at the steering device
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`130 that sits between the cellular network and the wired
`then redirected the user devices 110 to the video optimizer
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`150 to retrieve the optimized content. The video optimizer
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`Internet. For cxample, there is often no information about the
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`identifiers of the towers associated with the mobile devices
`150 receives informationin the redirect request fromthe user
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`devices 110 or from the network controller 140 about the
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`110. Tower association information only broadcasted when
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`video or image content to be optimized andretrieve the video
`the mobile devicesfirst attached to the network. In addition,
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`or image content from the correspondingorigin server 160 for
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`user devices 110 do not usually report any identification
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`optimization and subsequentdeliveryto the user devices 110.
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`information except their IP addresses. Therefore, monitoring
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`ofthe networktraffic and detection ofthe congestion is auto-
`[0031] Thedisclosed embodiments focus on the video opti-
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`mated and managedby the detector 140 so that network can
`mization because video is of far greater importance than all
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`be optimized for end user’s experience without the mobile
`other traffic types when network congestion is considered.
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`user’s knowledge.
`Video traffic makes up around halfofall network traffic—and
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`the percentage is growing every year. Therefore, optimizing
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`In contrast to conventional inline TCP throughput
`[0028]
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`video traffic massively reduces congestion in the network.
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`monitoring devices that monitor every single data packets
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`Video flows or streams are also long lived, having large
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`transmitted and received, the network controller 140 is an
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`packet size, and demanding high bitrates, monitoring video
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`“out-of-band” computerserver that interfaces with the steer-
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`streams is an effective ways of detecting congestion in the
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`ing device 130 to selectively inspect user flows ofinterest.
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`network. Furthermore, because video streams require steady
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`The network controller 140 may further identify user flows
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`and consistent bandwidth, they are amongfirst to be impacted
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`(e.g., among the flows of interest) for optimization. In one
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`when congestion occurs and available network bandwidth
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`embodiment,
`the network controller 140 may be imple-
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`reduces. By contrast, web page text and images are gencrally
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`mentedat the steering device 130 to monitortraffic. In other
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`not affected under mild network congestion with unnotice-
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`embodiments, the network controller 140 is coupled to and
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`able longer load times. Video optimization differs in one key
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`communicates with the steering device 130 fortraffic m