US9270725B2 - Method and apparatus for capacity dimensioning in a communication network - Google Patents
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L65/00—Network arrangements, protocols or services for supporting real-time applications in data packet communication
- H04L65/80—Responding to QoS
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0876—Network utilisation, e.g. volume of load or congestion level
- H04L43/0882—Utilisation of link capacity
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L65/00—Network arrangements, protocols or services for supporting real-time applications in data packet communication
- H04L65/60—Network streaming of media packets
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L65/00—Network arrangements, protocols or services for supporting real-time applications in data packet communication
- H04L65/60—Network streaming of media packets
- H04L65/61—Network streaming of media packets for supporting one-way streaming services, e.g. Internet radio
- H04L65/612—Network streaming of media packets for supporting one-way streaming services, e.g. Internet radio for unicast
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/2866—Architectures; Arrangements
- H04L67/30—Profiles
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
- H04W28/24—Negotiating SLA [Service Level Agreement]; Negotiating QoS [Quality of Service]
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- H—ELECTRICITY
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- H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
- H04W28/18—Negotiating wireless communication parameters
- H04W28/20—Negotiating bandwidth
Definitions
- Packet based video is one of the fastest growing internet applications.
- video streaming services as well as any other similar streaming service with real-time near-deterministic and timely delivery requirements, employing Internet protocol (IP) can be classified into two categories: video over IP networks and video over the Internet.
- IP Internet protocol
- video over IP networks video is delivered using dedicated IP networks.
- dedicated network allows the actuation of various admission control and traffic prioritization policies at the routers and switches to provide higher priority to video and other real-time traffic and, thereby protecting the more stringent quality of service (QoS) requirements of video.
- QoS quality of service
- Video over the Internet uses public Internet infrastructures to deliver streaming video. This is also referred to as video-over-the-top and is growing rapidly. However, video over the Internet is carried just like any other data that is carried via hypertext transfer protocol (HTTP) using transmission control protocol (TCP). As a result, an insufficient amount of bandwidth on various links may be available, thereby causing the QoS requirements of the video to not be met.
- HTTP hypertext transfer protocol
- TCP transmission control protocol
- the present disclosure teaches a method, computer readable medium and apparatus for calculating a capacity for high speed packet access data in a link in a communications network.
- the method comprises initializing parameters associated with streaming data, long elastic data and short elastic data, determining, via a processor, a capacity value such that a quality of service metric is met for the streaming data, the long elastic data and the short elastic data and provisioning the link with the capacity value if the quality of service metric is met
- FIG. 1 illustrates one example of a communications network architecture
- FIG. 2 illustrates a block diagram of a data architecture within a link of the communications network
- FIG. 3 illustrates a high level flowchart of one embodiment of a method for calculating a capacity for high speed packet access data in a link in a communications network
- FIG. 4 illustrates another high level flowchart of one embodiment of a method for calculating a capacity for high speed packet access data in a link in a communications network
- FIG. 5 illustrates a more detailed flowchart of one embodiment of a method for calculating a capacity for high speed packet access data in a link in a communications network
- FIG. 6 illustrates a more detailed flowchart of a queuing model block
- FIG. 7 illustrates a more detailed flowchart of a ⁇ matrix computation
- FIG. 8 illustrates a more detailed flowchart of a main loop
- FIG. 9 illustrates a more detailed flowchart of a performance estimation block
- FIG. 10 illustrates a high-level block diagram of a general-purpose computer suitable for use in performing the functions described herein.
- the present disclosure broadly discloses a method, computer readable medium and an apparatus for calculating a capacity for high speed packet access data in a link in a communications network.
- the link of particular interest may be an access link that connects a wireless base station (e.g., in 3G or 4G wireless networks) to a radio access network (RAN), e.g., an IuB link in 3G or an S1u link in LTE.
- FIG. 1 is a block diagram depicting one example of a communications network architecture 100 related to the current disclosure.
- the communications network architecture 100 comprises a 3G or a 4G cellular network such as a universal mobile telecommunications system (UMTS) network, a long term evolution (LTE) network and the like.
- UMTS universal mobile telecommunications system
- LTE long term evolution
- the communications network architecture 100 may include other types of communications networks such as general packet radio services (GPRS) networks, global system for mobile communication (GSM) networks or enhanced data rates for GSM evolution (EDGE) networks, and the like, by substituting the appropriate hardware and/or hardware configurations for the respective networks.
- GPRS general packet radio services
- GSM global system for mobile communication
- EDGE enhanced data rates for GSM evolution
- FIG. 1 is a block diagram depicting one example of a communications network architecture 100 related to the current disclosure.
- the communications network architecture 100 includes one or more user endpoints or user equipment (UE) 102 , a base station 104 , a radio network controller (RNC) 106 , the Internet 108 and an application server (AS) 112 .
- UE user equipment
- RNC radio network controller
- AS application server
- the user endpoints 102 may be any type of user device, such as for example, a mobile telephone, a smart phone, a messaging device, a tablet computer, a laptop computer, an air card and the like.
- the user endpoint 102 may communicate wirelessly with elements of the communications network architecture 100 .
- the base station 104 may be an eNodeB.
- the RNC 106 provides features such as packet scheduling, radio resource control (RRC) and handover.
- the AS 112 may include a processor and memory as described in FIG. 10 below and may be used to perform the modeling techniques discussed below.
- the RNC 106 and the AS 112 may be part of a core network of the communication network architecture 100 .
- the communications network architecture 100 may also include additional hardware or network components that are not illustrated depending on the type of network.
- the communications network may also include a serving general packet radio services (GPRS) support node (SGSN), a gateway GPRS support node (GGSN) and the like.
- GPRS general packet radio services
- GGSN gateway GPRS support node
- MME mobile management entity
- SGW serving gateway
- HSS home subscriber server
- PCRF policy and charging rules function
- PDN gateway packet data network gateway
- data flows from the user endpoint device 102 via various links in the communications network architecture 100 to send or receive data to and from various destinations or sources via the Internet 108 .
- FIG. 1 illustrates a link 110 between the base station 104 and the RNC 106 .
- the link 110 may be an IuB interface of an UMTS communications network or an S1u link in an LTE communications network.
- the link 110 may be any link or interface of any type of communications network.
- FIG. 2 illustrates a block diagram of a data architecture 200 within the link 110 .
- the data flowing through the link 110 may be divided into different types of data.
- a first portion 204 of the link 110 may include conversational data 210 (e.g., voice, call oriented data, admission controlled data, per-call bandwidth data and the like) and stream (R99) data 208 (e.g., audio streaming, call oriented data, admission controlled data, per-call bandwidth data, and the like).
- the capacity for the link 110 to handle conversational data 210 and the stream (R99) 208 data may use one type of modeling, such as for example, a Kaufman-Roberts dimensional modeling method.
- a second portion 202 of the link 110 may include high speed packet access (HSPA) data 206 (or any other broadband data) that uses transmission control protocol (TCP).
- HSPA data 206 may include, e.g., web browsing data, flow oriented and elastic data, rate adaptable data, finite access bandwidth data and the like.
- TCP transmission control protocol
- dimensioning models used for HSPA data 206 lumped all the data as a single type of data, i.e., elastic data.
- Elastic data may be defined as a data type that is bit conserving (i.e., it sends all of the data), but where the time for transmission may change depending on the available bandwidth. In other words, there is no time conservation for elastic data and the transmission duration can change.
- HSPA streaming data (e.g., video over the Internet) is becoming a substantial portion of the HSPA data traffic that flows through the link 110 .
- HSPA streaming data is not the same as the streaming data such as the conversational data 210 or the stream (R99) data 208 discussed above.
- the HSPA streaming data should not be confused with streaming data that is transmitted via dedicated IP networks. Rather, the streaming data that is part of the HSPA data 206 is transmitted via TCP.
- streaming data may be defined as time conserving, but not bit conserving. For example, if there is congestion, bits may be dropped (or coding rate reduced at the server) and a downgraded quality of the streaming data may be transmitted. In other words, the duration of the streaming data does not change. For example, if a video is five minutes long, the duration of the transmission will be five minutes.
- the present disclosure also pertains to an elastic data traffic class (i.e., the non-stream portion of the HSPA data 206 ) that competes against streaming video for bandwidth at various network resources.
- the elastic data sessions are in turn comprised of long elastic sessions (e.g., large file transfer sessions and the like) and short elastic sessions (e.g., email sessions, web surfing sessions and the like) that employ TCP.
- long elastic sessions e.g., large file transfer sessions and the like
- short elastic sessions e.g., email sessions, web surfing sessions and the like
- the former may be abbreviated as long elastic data and the latter as short elastic data.
- streaming data, long elastic data and short elastic data have unique behavioral attributes and different performance criteria. Therefore, the previously used dimensioning techniques to calculate a required capacity for the link 110 may no longer be accurate.
- the present disclosure provides a new dimensioning technique to calculate a required capacity for the second portion 202 of the link 110 that takes into consideration the different types of HSPA data 206 (e.g., streaming data, long elastic data and short elastic data).
- the new dimensioning technique may be applied as part of a process that can be performed by the AS 112 or a general purpose computer as disclosed in FIG. 10 .
- FIG. 3 illustrates a high level flowchart of a method 300 for one embodiment of calculating a capacity for HSPA data in a link in a communications network.
- the method 300 may be implemented by the AS 112 or a general purpose computer having a processor, a memory and input/output devices as discussed below with reference to FIG. 10 .
- the method 300 begins at step 302 and proceeds to step 304 .
- the method 300 initializes parameters associated with streaming data, long elastic data and short elastic data. The specific parameters are discussed below with reference to FIGS. 4-9 .
- the method 300 determines, via a processor, a capacity value such that a QoS metric is met for the streaming data, the long elastic data and the short elastic data.
- the determining step may be an iterative process.
- one of the initial parameters may be an initial capacity value. Based upon the initial capacity value, it may be determined if the QoS metric is met for the streaming data, the long elastic data and the short elastic data. If not, the initial capacity value may be revised higher and the determining step may be repeated until the QoS metric is met.
- the method 300 provisions the link with the capacity value if the quality of service metric is met.
- the capacity value is the appropriate allocation that meets the quality of service metrics.
- the method 300 ends at step 310 .
- FIG. 4 illustrates a high level flowchart of a method 400 of another embodiment for calculating a capacity for high speed packet access data in a link in a communications network.
- the method 400 may be implemented by the AS 112 or a general purpose computer having a processor, a memory and input/output devices as discussed below with reference to FIG. 10 .
- the method 400 begins at step 402 where the method 400 is initialized with various input parameters.
- the input parameters may include an aggregate busy hour data traffic intensity for the streaming data (K s ), an aggregate busy hour data traffic intensity for the long elastic data (K el ), an aggregate busy hour data traffic intensity for the short elastic data (K es ), a peak rate of the streaming data (Q s ), a packet loss probability for the elastic data (e.g., the long elastic data and the short elastic data) (PLR), a round trip delay time for the elastic data (e.g., the long elastic data and the short elastic data) (RTT), a maximum transmission control protocol segment size (MSS) and a representative file size for the short elastic data (FS).
- K s aggregate busy hour data traffic intensity for the streaming data
- K el an aggregate busy hour data traffic intensity for the long elastic data
- K es an aggregate busy hour data traffic intensity for the short elastic data
- Q s peak rate of the streaming data
- PLR packet loss probability for the elastic data
- the Q s is dictated by the application drain rate of the streaming data.
- Q s is a known fixed value based upon documented attributes of the streaming applications.
- the method 400 enters the input parameters from step 402 into the new multi-class capacity dimensioning algorithm.
- the method 400 determines a capacity value (C) that is used to engineer the capacity of a link, e.g., the link 110 .
- the method 400 may be executed based upon one or more different bandwidth sharing profiles 408 among contending applications. Thus, depending on the sharing profile that is used, the computed outcome for C may vary between profiles.
- a first profile applies an even splitting of capacity among active sessions.
- the first profile may be represented as shown below in Equation (1):
- ⁇ s represents a share of the capacity assigned to each of the x s streaming data sessions
- ⁇ e represents a share of the capacity assigned to each of the x e elastic data sessions.
- Equation (2) For a scenario with more than two classes (e.g., a 3-class scenario comprised of streaming data, long elastic data and short elastic data), the share of bandwidth captured by each active session belonging to class i, ⁇ i (x), is given by Equation (2):
- a second profile applies a peak rate proportional bandwidth sharing profile.
- the second profile may be represented as shown below in Equations (3) and (4):
- the ⁇ s and the ⁇ e may be calculated by Equations (5) and (6):
- Q s Q s C , Eq . ⁇ ( 5 )
- ⁇ e Q e C , Eq . ⁇ ( 6 )
- Q s represents the peak rate of the streaming data
- Q e represents the peak rate of the elastic data
- C represents the target capacity of the link, which is being evaluated for QoS compliance in the current instance of the dimensioning process.
- the value of Q s is determined based upon the type of streaming data, as discussed above.
- the value of Q e may be calculated depending on the type of elastic data. For example, a peak rate of the long elastic data Q el and a peak rate of the short elastic data Q es may be calculated.
- the values for Q el and Q es may be calculated by considering the behavior of the long elastic and short elastic data under different PLR and RTT. In one embodiment, Equations (7) and (8) below can be used:
- the ⁇ e may be further categorized into the normalized peak rate for the long elastic data ⁇ el and the normalized peak rate for the short elastic data ⁇ es .
- the values for ⁇ el and ⁇ es are provided below by Equations (9) and (10):
- Equation (11) For a scenario with more than two classes (e.g., a 3-class scenario comprised of streaming data, long elastic data and short elastic data), the share of bandwidth captured by each active session belonging to class i, ⁇ i (x), is given by Equation (11):
- FIG. 5 illustrates a more detailed flowchart of one embodiment of a method 500 for calculating a capacity for high speed multi-class access packet data in a link in a communications network.
- FIG. 5 provides more detail to some of the steps illustrated in FIG. 4 .
- the method 500 may be implemented by the AS 112 or a general purpose computer having a processor, a memory and input/output devices as discussed below with reference to FIG. 10 .
- the method 500 begins at step 502 and proceeds to step 504 .
- an initial lower bound on the capacity, C is set and included in the input parameter list 516 .
- the method 500 then proceeds to step 506 , where the input parameters from block 516 and a bandwidth sharing profile from block 518 are fed into a queuing model block 506 .
- the input parameters may include, C, K s , K el , K es , Q s , PLR, RTT, MSS and FS.
- the profile may be one of a plurality of different types of sharing profiles. For example, two possible sharing profiles may be an equal sharing profile or a proportional sharing profile. The equations for the two types of sharing profiles are provided by Equations (1)-(4) and (11) above.
- the queuing model block 506 determines the computational priority ordering of the streaming data, the long elastic data and the short elastic data, and sets the bounds of a three dimensional state transition diagram. It should be noted that if the long elastic data and the short elastic data are lumped together as a single elastic data class, the state transition diagram may be simplified into a two dimensional state transition diagram.
- the queuing model block 506 attempts to perform conservative bounding of the state transition diagrams for various performance metrics, for example, sub-par customer time fraction (SCTF) for the streaming data and a sub-par customer data fraction (SCDF) for the elastic data.
- SCDF sub-par customer time fraction
- SCDF el long elastic data sub-par customer data fraction
- SCDF es short elastic data sub-par customer data fraction
- the SCTF can be mapped to an objective streaming video quality metric, such as for example, the probability of stall or the Peak Signal to Noise Ratio (PSNR).
- PSNR Peak Signal to Noise Ratio
- the illustrative examples and equations below are provided for a two-dimensional state transition diagram.
- the equations may easily be modified for the three-dimensional state transition diagram (or any arbitrary N-dimensional state transition diagram).
- expressions with “e” may be substituted with “(el+es)”.
- the conservative bounding may be determined by calculating a pessimistic weight function ⁇ hacek over ( ⁇ ) ⁇ (x s ,x e ) for each state (x s ,x e ).
- the technique involves following a so-called heaviest weight path along the edges of the state space (e.g., a square for 2-D and a cube for 3-D).
- ⁇ 2 (x 1 ,x 2 ) depends on both the stream and elastic occupancies (i.e., x 1 and x 2 ) ensues from the fact that, unlike stream traffic, elastic traffic is bit conserving but not time conserving. In other words, the duration of an elastic session stretches or shrinks depending on the congestion level, till the last bit is successfully transmitted.
- the ⁇ parameters are alternately calculated in terms of the known aggregate busy hour traffic intensities or volumes ⁇ K s ,K el ,K es ⁇ and peak rates ⁇ Q s , Q el ,Q es ⁇ .
- the ⁇ terms in the denominators of the above equations contain ⁇ terms, which divide into corresponding ⁇ terms in the numerator such that these equations can be restated in terms of terms of ⁇ terms. While ⁇ 's and ⁇ 's are typically unavailable, the ⁇ terms can be calculated alternately from traffic volumes and peak rates as shown later, thus facilitating the computational steps to be described.
- the infinite state-space ⁇ (0,0) . . . ( ⁇ , ⁇ ) ⁇ is approximated by a finite state-space ⁇ (0,0) . . . (K,K) ⁇ , with K being chosen so as to achieve an acceptable degree of numerical accuracy.
- the infinite state-space ⁇ (0,0,0) . . . ( ⁇ , ⁇ , ⁇ ) ⁇ will be approximated by the finite state-space ⁇ (0,0,0) . . . (K, K, K) ⁇ .
- Equation (13) may be expressed in the following efficient recursive format:
- Equation (14) for the optimistic bound can also be expressed in an analogous recursive format with the associated efficient implementation.
- the recursive formats and associated efficient implementations may be used in the algorithmic descriptions to be given.
- the conservative (pessimistic) bounds of the performance metrics SCTF and SCDF can be calculated using Equations (16) and (17) below:
- SCTF ⁇ ( x s , x e ) ⁇ : ⁇ ⁇ s ⁇ ( x s , x e ) ⁇ SST ⁇ x s ⁇ ⁇ ⁇ ( x s , x e ) ⁇ ( x s , x e ) ⁇ x s ⁇ ⁇ ⁇ ( x s , x e ) , Eq .
- SCDF ⁇ ( x s , x e ) ⁇ : ⁇ ⁇ e ⁇ ( x s , x e ) ⁇ ET ⁇ x s ⁇ ⁇ e ⁇ ( x s , x e ) ⁇ ⁇ ⁇ ( x s , x e ) ⁇ ( x s , x e ) ⁇ x s ⁇ ⁇ e ⁇ ( x s , x e ) ⁇ ⁇ ⁇ ( x s , x e ) , Eq .
- ET represents a customer stipulated data throughput rate acceptability threshold for each elastic session (e.g., 800 kilobytes per second (Kbps)
- SST represents a customer stipulated data throughput rate acceptability threshold for each stream session (e.g., 800 Kbps)
- ⁇ e (x s ,x e ) is defined by Equation (4) above.
- the ET would have two distinct components, ELT and EST, for long elastic data throughput rate acceptability threshold and short elastic data throughput rate acceptability threshold, respectively, where appropriate.
- any of the equations above for two classes may be generalized to an arbitrary number of classes.
- any of the equations may be generalized for three classes to be applied specifically to a system having the streaming data, the long elastic data and the short elastic data as distinct components.
- the method 500 then proceeds to a performance estimation block 508 .
- the performance metrics such as SCTF, SCDF el and SCDF es , may be calculated via equations, such as Equations (16) and (17) above.
- the method 500 then proceeds to step 510 , where the performance metrics are compared against a target value for each of the performance metrics.
- a target value for each of the performance metrics there may be a predefined SCTF target value (TGT SCTF ), a predefined long elastic SCDF target value (TGT SCDF long ) and a predefined short elastic SCDF target value (TGT SCDF short ).
- the TGT SCTF , TGT SCDF long and TGT SCDF short may be configurable values.
- the TGT SCTF may be set to 0.1%
- the TGT SCDF long may be set to 10%
- the TGT SCDF short may be set to 1%.
- the numerical values are only provided as examples and should not be considered limiting.
- the method 500 determines whether the QoSmet variable is true or false. If the variable QoSmet is false, then the method 500 proceeds to step 514 , where the value of C is updated in the input parameter list 516 . In other words, at step 514 the method 500 determined that the initial value of C was not a sufficient amount of capacity for the QoS to be met for each of the different types of traffic depending on the type of sharing profile that was used to perform the calculations. As a result, another iteration of the calculations must be performed with the intent of converging to the optimal value for C.
- the search for optimum capacity, C is effectuated by linearly increasing the value of C, as shown in FIG. 5 .
- a faster logarithmic search method may be used to facilitate the determination for the optimum capacity, C.
- the value of C may be exponentially increased to 20,000 Kbps, then to 40,000 kbps, and then to 80,000 Kbps and so forth, until a smallest upper bound (e.g., 40,000 Kbps) that satisfies the metrics, and a highest lower bound (e.g., 20,000 Kbps) that does not satisfy the metrics, are identified.
- the performance evaluation algorithm is run for a capacity, C, equal to the mid-point between the upper and lower bounds (i.e., 30,000 Kbps, for the example quoted). If the metrics are satisfied at the mid-point, then the mid-point becomes a new upper bound and the lower bound is maintained at its previous value. Otherwise the mid-point becomes a new lower bound and the upper bound is maintained at its previous value. Now the procedure is repeated using the new pair of lower and upper bounds. The ensuing recursion is continued till the upper and lower bounds are within an acceptable margin of error, at which point the value of the upper bound is output as the optimal capacity C.
- this version of the search outer loop of the algorithm implementation involves a logarithmic number of steps, and would converge significantly faster than a linear implementation, shown for illustrative purposes in FIG. 5 . Further details for performing the logarithmic search may be found with reference to co-pending U.S. patent application Ser. No. 12/655,236 filed on Dec. 23, 2009, which is incorporated by reference in its entirety.
- step 520 the method outputs C as the engineered capacity and the method 500 ends.
- FIG. 6 illustrates a more detailed flowchart of a method 600 that is one embodiment of a way to perform the queuing model block 506 illustrated in FIG. 5 .
- the method 600 starts at step 602 and proceeds to 604 .
- the method 600 receives the input parameters from block 516 as illustrated in FIG. 5 .
- the input parameters are used to calculate various values, such as for example, Q el , Q es , ⁇ s , ⁇ el , ⁇ es , ⁇ s , ⁇ el and ⁇ es .
- the equations for calculating the values of Q el , Q es , ⁇ s , ⁇ el and ⁇ es are discussed above in Equations (5) and (7)-(10).
- ⁇ s , ⁇ el and ⁇ es represent a normalized stream session erlangs, a normalized long elastic session erlangs and a normalized short elastic session erlangs.
- the values for ⁇ s , ⁇ el and ⁇ es may be alternatively calculated using the Equations (18)-(20) below:
- ⁇ represents a capacity assigned to a particular session.
- the ⁇ matrix provides a matrix that describes the per-session capacity assigned to each of the particular types of sessions ⁇ i (x), under the exhaustive set of states of the system ⁇ x ⁇ (the system state being defined by a vector of the number of active sessions belonging to the distinct classes under consideration). It should be noted that the values of the ⁇ matrix entries would depend on the particular bandwidth sharing profile specified.
- the method 600 then proceeds to step 608 , wherein the highest algorithmic priority, index value of 1, is assigned to the streaming data, s.
- this assignment does not imply any kind of traffic prioritization in the physical world. For example, absolute traffic-dependent priorities are feasible only in dedicated private networks, and not in the HTTP/TCP public internet scenario. Rather, the above assignment has meaning only in an algorithmic sense, and is a consequence of the ordering implied by Equation (12), in light of the property described above, whereby the departure rates of stream sessions are independent of congestion and hence independent of the number of active elastic sessions, while the departure rates of elastic sessions (long, as well as short) do depend on congestion, hence on the occupancy levels of all classes.
- the method 600 determines which bandwidth sharing profile was applied. For example, referring back to FIG. 5 , at step 518 , a bandwidth sharing profile was selected and entered into the queuing model block 506 . If the equal bandwidth sharing profile was selected, then the method 600 proceeds to step 612 .
- the method 600 maps indices 2 and 3 to the long elastic data and the short elastic data according to the mathematical expression “Map indices 2, 3 ⁇ el, es ⁇ : ⁇ 2 ⁇ 3 .” In other words, under the equal bandwidth sharing profile, the indices for the two elastic sub-classes are assigned in the order of increasing peak rates, which is the appropriate ordering to satisfy Equation (12) in this context.
- the method 600 proceeds to step 614 .
- the method 600 maps indices 2 and 3 to the long elastic data and the short elastic data according to the mathematical expression “Map indices 2, 3 ⁇ el,es ⁇ : ⁇ 2 ⁇ 3 .”
- the indices for the two elastic sub-classes are assigned in the order of decreasing peak rates, which is the appropriate ordering to satisfy Equation (12) in this context.
- the method 600 then proceeds to step 618 where the main loop is performed.
- the main loop calculates the steady state probabilities for each state within the bounds of the pessimistic weight function or optimistic weight function, as discussed earlier. The details of the main loop are described further below with reference to FIG. 8 , in the context of conservative bounds; as will be explained, the sequence of certain operations therein may be reversed to arrive at optimistic bounds instead.
- the method 600 remaps the steady state distribution, ⁇ , in accordance with the original ordering of the streaming data, the long elastic data and the short elastic data.
- the method 600 then proceeds to step 622 , where the method 600 exits at step 622 back to the performance estimation block 508 , illustrated in FIG. 5 .
- FIG. 7 illustrates a more detailed flowchart of a method 700 for the ⁇ matrix computation 606 illustrated in FIG. 6 .
- the ⁇ matrix provides information as to how bandwidth is being shared by the active session belonging to competing data types (e.g., stream session data sessions, long elastic data sessions and short elastic data sessions) for each state of the state transition diagram.
- the method 700 starts at step 702 and then proceeds to step 704 .
- the method 700 indexes the three traffic types in order of increasing peak rates as shown, and initializes the values for the ⁇ matrix.
- the computation begins at state ⁇ 0,0,0 ⁇ for x 1 , x 2 and x 3 .
- steps 712 , 714 , 716 , 718 and 720 are the maximum occupancy level of each class considered (i.e., the approximation of the infinite state-space by a finite one).
- steps 712 - 720 represent mathematically steps for ensuring that all the states are visited. In this case, for example, in the order [0,0,0], [1,0,0], . . . [K,0,0], [0,1,0], . . . [0,0,K], . . . [K,K,K].
- the method 700 proceeds to step 722 where the bandwidth shares for each index are remapped to the traffic classes. For example, the ⁇ 1 may be mapped to ⁇ s , the ⁇ 2 may be mapped to ⁇ el and the ⁇ 3 may be mapped to ⁇ es (if ⁇ 1 ⁇ 2 ⁇ 3 ).
- the method 700 ends at 724 and returns to step 606 of FIG. 6 .
- FIG. 8 illustrates a more detailed flowchart of a method 800 for performing the main loop step 618 of FIG. 6 .
- FIG. 8 illustrates one embodiment that applies the pessimistic weight function, ⁇ hacek over ( ⁇ ) ⁇ , discussed above. With that objective, method 800 goes through the heaviest weight path starting with x 3 , then x 2 and x 1 , as shown. It should be noted that if the optimistic weight function, ⁇ circumflex over ( ⁇ ) ⁇ , were to be used then the method 800 may be modified to go through the lightest weight path starting with x 1 to x 3 .
- the probability variables ⁇ [0,0,0] . . .
- ⁇ [K,K,K] are initially used to compute and store the weight functions ⁇ hacek over ( ⁇ ) ⁇ [0,0,0] . . . [K,K,K]; subsequently, a normalization step is applied to convert them to probabilities.
- ⁇ [0,0,0] ⁇ hacek over ( ⁇ ) ⁇ [0,0,0] was initialized to 1 in step 616 prior to the invocation of method 800 .
- the method 800 starts at step 802 and then proceeds to step 804 .
- the method 800 proceeds to step 806 .
- Step 806 along with the loop test in step 808 executes Equation (13A) in conjunction with the associated efficient implementation, for the highest index 3.
- Equation (13A) ⁇ hacek over ( ⁇ ) ⁇ [0,0,1] through ⁇ hacek over ( ⁇ ) ⁇ [0,0,K] are computed in the first pass.
- x 2 is incremented to 1 in step 810 , determines if x 1 is less than K at step 812 and the method 800 proceeds to step 816 where ⁇ hacek over ( ⁇ ) ⁇ [0,1,0] is computed via the logic of Equation (13A).
- step 804 x 3 is reset to 1 in step 804 and the algorithm returns to step 806 to compute ⁇ hacek over ( ⁇ ) ⁇ [0,1,1] through ⁇ hacek over ( ⁇ ) ⁇ [0,1,K].
- This circuit involving steps 806 , 808 , 810 , 812 , 818 and 804 continues until ⁇ hacek over ( ⁇ ) ⁇ [0,0,1] through ⁇ hacek over ( ⁇ ) ⁇ [0,K,K] are generated.
- step 814 increments x 1 to 1.
- step 816 if the test passes (i.e., x 1 ⁇ K is yes), the method 800 proceeds to step 820 where ⁇ hacek over ( ⁇ ) ⁇ [1,0,0] is computed.
- ⁇ 's departure rates
- the method 800 proceeds to step 822 where the variable norm is computed and x 2 is reset to 0. It should be noted that the variable norm may be continually updated upon computation of the weight for each and every state in steps 806 , 818 , 822 and 822 .
- step 804 x 3 is reset to 1, following which ⁇ hacek over ( ⁇ ) ⁇ [1,0,1] is computed in step 806 .
- the circuit involving steps 806 , 808 , 810 , 812 , 818 and 804 is invoked a second time until ⁇ hacek over ( ⁇ ) ⁇ [1,0,1] through ⁇ hacek over ( ⁇ ) ⁇ [1,K, K] are generated.
- step 814 method 800 again proceeds to step 814 to increment x 1 .
- the macro circuit involving steps 806 , 808 , 810 , 812 , 814 , 816 , 818 , 804 , 820 and 822 eventually generates all of ⁇ hacek over ( ⁇ ) ⁇ [0,0,1] through ⁇ hacek over ( ⁇ ) ⁇ [K,K,K], at which point the test fails in step 816 (i.e. x 1 ⁇ K is no) bringing method 800 to step 824 .
- the value of the normalization variable norm at this point equals the sum of the weight function values for all the system states (the infinite state-space being approximated as K 3 finite states).
- the weight function value for each state is divided by norm to convert to the corresponding state probability.
- the method 800 then proceeds to step 826 where the method 800 ends and returns to step 618 of FIG. 6 .
- the optimistic probability bounds may be computed instead of the pessimistic bounds simply by reversing the order of the indices. For example, by using x 1 in place of x 3 in steps 804 , 806 and 808 and x 3 in the place of x 1 in steps 814 , 816 and 820 , the computation sequence can be changed to generate the optimistic weight functions ⁇ circumflex over ( ⁇ ) ⁇ [0,0,0] . . . ⁇ circumflex over ( ⁇ ) ⁇ [K,K,K] ⁇ instead of the pessimistic weight functions as is being accomplished in FIG. 8 with the order shown.
- FIG. 9 illustrates a more detailed flowchart of a method 900 for performing the performance estimation block step 508 of FIG. 5 .
- the method 900 starts at step 902 from the queuing model block 506 of FIG. 5 and proceeds to step 904 .
- the method 900 initializes values to begin calculation of the performance metric parameters SCTF, SCDF el and SCDF es .
- SCTF performance metric parameter
- SCDF performance metric parameter
- the parameters SCTF numer, SCDFnumer[el] and SCDFnumer[es] represent the numerator value (e.g., the numerator of Equations (16) and (17)) for calculating the performance metric parameters SCTF, SCDF el and SCDF es , respectively.
- the parameters SCTFdenom, SCDFdenom[el] and SCDFdenom[es] represent the denominator value (e.g., the denominator of Equations (16) and (17)) for calculating the performance metric parameters SCTF, SCDF el and SCDF es , respectively.
- the method 900 then proceeds to step 906 .
- the method 900 at step 906 calculates the parameters SCTFdenom, SCTF numer, SCDFdenom[el], SCDFnumer[el], SCDFdenom[es] and SCDFnumer[es] for each state. For example, the calculation is defined by Equations (16) and (17) described above.
- steps 908 - 916 represent mathematically steps for ensuring that all the states are visited. In this case, for example, in the order [0,0,0], [1,0,0], . . . [K,0,0], [0,1,0], . . . [0,0,K], . . . [K,K,K].
- the values of the performance metric parameters SCTF, SCDF el and SCDF es are calculated using the final values of the parameters SCTFdenom, SCTF numer, SCDFdenom[el], SCDFnumer[el], SCDFdenom[es] and SCDFnumer[es].
- the method 900 proceeds to step 920 where the values of SCTF, SCDF el and SCDF es are outputted to step 510 of FIG. 5 .
- one or more steps of the methods described herein may include a storing, displaying and/or outputting step as required for a particular application.
- any data, records, fields, and/or intermediate results discussed in the methods can be stored, displayed, and/or outputted to another device as required for a particular application.
- steps or blocks in FIGS. 3 and 9 that recite a determining operation, or involve a decision do not necessarily require that both branches of the determining operation be practiced. In other words, one of the branches of the determining operation can be deemed as an optional step.
- FIG. 10 depicts a high-level block diagram of a general-purpose computer suitable for use in performing the functions described herein.
- the system 1000 comprises a processor element 1002 (e.g., a CPU), a memory 1004 , e.g., random access memory (RAM) and/or read only memory (ROM), a module 1005 for calculating a capacity for high speed packet access data in a link in a communications network, and various input/output devices 1006 (e.g., storage devices, including but not limited to, a tape drive, a floppy drive, a hard disk drive or a compact disk drive, a receiver, a transmitter, a speaker, a display, a speech synthesizer, an output port, and a user input device (such as a keyboard, a keypad, a mouse, and the like)).
- a processor element 1002 e.g., a CPU
- memory 1004 e.g., random access memory (RAM) and/or read only memory (ROM)
- the present disclosure can be implemented in software and/or in a combination of software and hardware, e.g., using application specific integrated circuits (ASIC), a general purpose computer or any other hardware equivalents.
- the present module or process 1005 for calculating a capacity for high speed packet access data in a link in a communications network can be loaded into memory 1004 and executed by processor 1002 to implement the functions as discussed above.
- the present method 1005 for calculating a capacity for high speed packet access data in a link in a communications network (including associated data structures) of the present disclosure can be stored on a non-transitory (tangible or physical) computer readable storage medium, e.g., RAM memory, magnetic or optical drive or diskette and the like.
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Abstract
Description
where γs represents a share of the capacity assigned to each of the xs streaming data sessions and γe represents a share of the capacity assigned to each of the xe elastic data sessions.
with the convention that the result of any summation where the upper index is less than the lower index equals zero.
where αs represents a normalized peak rate to pipe capacity for the streaming data and αe represents a normalized peak rate to pipe capacity for the elastic data.
where Qs represents the peak rate of the streaming data, Qe represents the peak rate of the elastic data and C represents the target capacity of the link, which is being evaluated for QoS compliance in the current instance of the dimensioning process. The value of Qs is determined based upon the type of streaming data, as discussed above.
where the variables, FS, MSS, RTT and PLR are known input parameters, as discussed above.
wherein λ's, as defined earlier, represent mean session arrival rates and {hacek over (Ψ)}(0,0)={circumflex over (Ψ)}(0,0)=1. As discussed above, the φ terms in the denominators of the above equations contain μ terms, which divide into corresponding λ terms in the numerator such that these equations can be restated in terms of terms of ρ terms. While λ's and μ's are typically unavailable, the ρ terms can be calculated alternately from traffic volumes and peak rates as shown later, thus facilitating the computational steps to be described.
Based on the recursive variant Equation (13A), the following algorithmic sequence may be followed to exhaustively compute the weight functions {{hacek over (Ψ)}(0,0) . . . {hacek over (Ψ)}(K,K)} for all the system states {[0,0] . . . [K, K]}:
Set {hacek over (Ψ)}(0,0) = 1; | ||
For (i=0 to K){ |
If (i>0) then calculate {hacek over (Ψ)}(0,i) from Equation (13A); | |
For (j=1 to K) |
Calculate {hacek over (Ψ)}(j,i) from Equation (13A); |
} | ||
{hacek over (π)}(x s ,x e)={hacek over (π)}(0,0){hacek over (Ψ)}(x s ,x e), where {hacek over (π)}(0,0)=1/Σ(x
where ET represents a customer stipulated data throughput rate acceptability threshold for each elastic session (e.g., 800 kilobytes per second (Kbps)), SST represents a customer stipulated data throughput rate acceptability threshold for each stream session (e.g., 800 Kbps) and γe(xs,xe) is defined by Equation (4) above. In the 3-dimensional model, it should be noted that the ET would have two distinct components, ELT and EST, for long elastic data throughput rate acceptability threshold and short elastic data throughput rate acceptability threshold, respectively, where appropriate.
x 1 =x 2 =x 3=0;π[0,0,0]=norm=1; Eq. (21)
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US9935994B2 (en) | 2018-04-03 |
US20120151078A1 (en) | 2012-06-14 |
US20160173558A1 (en) | 2016-06-16 |
US8595374B2 (en) | 2013-11-26 |
US20140082203A1 (en) | 2014-03-20 |
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