Apparatuses and methods for estimating throughput in accordance with quality of service prioritization and carrier aggregation to facilitate network resource dimensioning

Aspects of the subject disclosure may include, for example, calculating a throughput of each cell of a plurality of cells of a communication network, calculating a total throughput of the plurality of cells, and distributing, in accordance with a first distribution, carrier aggregated traffic amongst the plurality of cells, wherein each cell obtains a respective portion of the carrier aggregated traffic as part of the first distribution in accordance with the throughput of the cell and the total throughput. Other embodiments are disclosed.

FIELD OF THE DISCLOSURE

The subject disclosure relates to apparatuses and methods for estimating throughput in accordance with quality of service prioritization and carrier aggregation.

BACKGROUND

As the world becomes increasingly connected via vast communication networks and communication devices, additional challenges are created/generated from the perspective of provisioning and managing network resources. For example, from a perspective of a network operator, a policy that favors cost reduction (e.g., cost minimization) while deemphasizing (e.g., disregarding/ignoring) quality of service (QoS) parameters runs a risk of degradation in terms of a user's quality of experience (QoE). The reduction in QoE may tend to alienate/annoy the user, potentially to the point that the user may terminate service with the network operator. On the other hand, a policy that conservatively allocates resources (e.g., spectrum, bandwidth, etc.) to ensure high levels of QoS or QoE, without taking into account fine-grain QoS considerations, runs a risk of wasteful/unnecessary surplus investment.

DETAILED DESCRIPTION

The subject disclosure describes, among other things, illustrative embodiments for distributing traffic and allocating resources to achieve maximal UE throughput amongst a plurality of cells of a network or system. Other embodiments are described in the subject disclosure.

One or more aspects of the subject disclosure include, in whole or in part, distributing carrier aggregated traffic amongst a plurality of cells of a network, initializing a first plurality of scheduler weights for each cell of the plurality of cells, wherein for each cell, each of the first plurality of weights is associated with a respective traffic class included in a plurality of traffic classes, calculating an average user equipment (UE) throughput of each cell of the plurality of cells, distinctly for the carrier aggregated (CA) and non-aggregated (non-CA) components of each class of the plurality of classes, calculating a total average UE throughput of the carrier aggregated component of each of the plurality of classes in accordance with the calculating of the throughput of each cell (by aggregation), and redistributing the carrier aggregated traffic amongst the plurality of cells based on the corresponding throughput for each cell and the total throughput.

One or more aspects of the subject disclosure include, in whole or in part, calculating a UE throughput of each cell of a plurality of cells of a communication network, calculating a total carrier aggregated UE throughput of each class among a plurality of classes in accordance with the calculating of the component throughput of each cell, and redistributing carrier aggregated traffic amongst the plurality of cells, wherein each cell obtains a respective portion of the carrier aggregated traffic as part of the redistributing in proportion to a ratio of the throughput from the cell relative to the total throughput.

One or more aspects of the subject disclosure include, in whole or in part, calculating a throughput of each cell of a plurality of cells of a communication network, calculating a total carrier aggregated UE throughput of each classes among a plurality of classes, and distributing, in accordance with a first distribution, carrier aggregated traffic amongst the plurality of cells, wherein each cell obtains a respective portion of the carrier aggregated traffic as part of the first distribution in accordance with the throughput of the cell and the total throughput.

Referring now toFIG. 1, a block diagram is shown illustrating an example, non-limiting embodiment of a communications network100in accordance with various aspects described herein. For example, communications network100can facilitate in whole or in part distributing carrier aggregated traffic amongst a plurality of cells of a network, initializing a first plurality of weights for each cell of the plurality of cells, wherein for each cell, each of the first plurality of weights is associated with a respective traffic class included in a plurality of traffic classes, calculating a throughput of each cell of the plurality of cells, calculating a total throughput of the plurality of cells in accordance with the calculating of the throughput of each cell, and redistributing the carrier aggregated traffic amongst the plurality of cells based on the throughput of each cell and the total throughput. Communications network100can facilitate in whole or in part calculating a throughput of each cell of a plurality of cells of a communication network, calculating a total throughput of the plurality of cells in accordance with the calculating of the throughput of each cell, and redistributing carrier aggregated traffic amongst the plurality of cells, wherein each cell obtains a respective portion of the carrier aggregated traffic as part of the redistributing in proportion to a ratio of the throughput of the cell relative to the total throughput. Communications network100can facilitate in whole or in part calculating a throughput of each cell of a plurality of cells of a communication network, calculating a total throughput of the plurality of cells, and distributing, in accordance with a first distribution, carrier aggregated traffic amongst the plurality of cells, wherein each cell obtains a respective portion of the carrier aggregated traffic as part of the first distribution in accordance with the throughput of the cell and the total throughput.

FIG. 2Ais a block diagram illustrating an example, non-limiting embodiment of a system200afunctioning within, or operatively overlaid upon, the communication network100ofFIG. 1in accordance with various aspects described herein. In particular, the system200amay include a tower/base station202athat may be used to provide service to one or more communication devices, e.g., communication devices206a,210a,214a,218a,222a, and226a. The tower202amay be communicatively linked/coupled to backhaul infrastructure (not shown inFIG. 2A) via wired and/or wireless connections.

The coverage provided by the tower202amay be divided into multiple sectors/faces, such as for example three sectors/faces denoted as sector/face A, second/face B, and sector/face C inFIG. 2A. Each of the sectors/faces may be further divided into multiple cells, e.g., cell234ainFIG. 2A. Each cell within a sector/face may operate at a distinct carrier frequency. The use of multiple carrier frequencies within a sector/face may enhance a data carrying capacity, which in turn may enhance a quality of experience (QoE) or quality of service (QoS).

In the instance of the exemplary system200ashown inFIG. 2A, the communication devices206aand210amay obtain service via the sector/face A, the communication devices214a-222amay obtain service via the sector/face B, and the communication device226amay obtain service via the sector/face C. However, one or more of the communication devices206a-226amay be a mobile device and may migrate from a scope of coverage associated with a first sector/face (e.g., sector/face A) to a scope of coverage associated with a second sector/face (e.g., sector/face B). In this regard, the tower202amay facilitate a handover of service (e.g., a handover of a communication session) from the first sector/face to the second sector/face. Still further, in some embodiments a handover of service may be provided from the tower202ato another tower (not shown inFIG. 2A) if a communication device leaves the range of coverage provided by any of the sectors/faces associated with the tower202a.

In accordance with aspects of this disclosure, one or more of the communication devices206a-226amay be configured to simultaneously connect to multiple carriers/cells in order to increase the throughput realized by the communication device(s). For example, in relation to an execution of a streaming video application by a communication device, the (simultaneous) utilization of multiple carriers by the communication device (the condition of which is referred to herein as carrier aggregation [CA]) may enhance/increase the amount of data associated with the video that is obtained by the communication device (where the amount of data may be expressed per unit of time). The implementation of carrier aggregation may result in fewer (if any) buffering delays at the communication device, and may result in a smoother (e.g., less choppy) playback of the video at the communication device. In this regard, the QoE associated with the use of the communication device may be enhanced as a result of the communication device utilizing CA. As an example, CA can include multiple frequency blocks or component carriers being assigned to a single user or single channel.

On the other hand, one or more of the communication devices206a-226amight not be configured to support carrier aggregation (such devices being referred to herein as non-CA devices). For example, and ignoring/excluding principles of a soft-handover (e.g., make-before-break) of a communication session that may be facilitated by a non-CA device, non-CA devices might only be able to connect to a service via a single cell/carrier at a given point in time. Thus, all other conditions being equal, non-CA devices might not able to leverage the benefits of increased throughput that may be obtained by their CA device counterparts. Note that the advantage of carrier aggregation may manifest itself only under light to medium loading conditions; under heavy load/network congestion, the scheduler weights may be automatically adjusted so as to counteract this advantage and ensure fairness across all UE's (CA or non-CA) belonging to each application class.

During periods of low network traffic (e.g., network traffic in an amount less than a threshold), the utilization of multiple carriers/cells by CA devices might not directly impact the QoE/QoS of non-CA devices, as there may be sufficient resources (e.g., carriers) available to the non-CA devices. However, during periods of high network traffic (e.g., network traffic in an amount greater than the threshold), the utilization of multiple carriers/cells by the CA devices may adversely impact the QoE/QoS of the non-CA devices. Accordingly, aspects of this disclosure are directed to determinations of how to allocate resources between CA and non-CA devices generally, and more specifically, how to allocate such resources in response to changing/dynamic network conditions.

Aspects of the system200amay be implemented in conjunction with an allocation of resources. To demonstrate, and referring toFIG. 2B, a system200bis shown that may be used to dimension/allocate resources (e.g., radio resources, communication bandwidth, control resources, etc.) associated with a communication network or system, such as the system200aofFIG. 2A. The system200bmay include a load-aware dimensioning engine204b, a forecasting engine208b, a QoS-aware dimensioning engine212b, and a CA-enhanced dimensioning engine216b. While the engines204b-216bare shown separately inFIG. 2B, a first of the engines (e.g., engine212b) may be combined with one or more other engines (e.g., engine216b) in some embodiments.

The load-aware dimensioning engine204bmay generate profiles for, e.g., each cell i of the network or system, among a load balanced group of cells indexed i=1, . . . , Ψ (e.g., the plurality of cells serving a single sector/face of a wireless site). The profiles, which may include or be based on various parameters (e.g., signals, interference, noise, etc.), may be specified in an uplink direction, a downlink direction, or both uplink and downlink directions. In some embodiments, one or more of the parameters may be combined in connection with a given profile. For example, in some embodiments the load-aware dimensioning engine204bmay generate a signal-to-interference-plus-noise (SINR) profile for a given cell. The SINR profile may be based at least in part on estimates/projections of one or more communication devices being located within the cell, estimates/projections of one or more communication sessions of the communication device(s) falling within a given SINR class/category, estimates/projections and/or measurements of throughput within the given SINR class/category, and/or estimates/projections and/or measurements of interference caused by other communication device in nearby (e.g., adjacent or neighboring) sectors and/or cells. Focusing on the downlink direction as an exemplary embodiment (uplink being analogous), by processing the SINR profiles in conjunction with the known spectrum bandwidth configurations, the load aware dimensioning engine may estimate a capacity Ci(in, e.g., Mbps) for each cell i=1, . . . , Ψ in a load balanced group of cells.

The forecasting engine208bmay generate forecasts of traffic in the network or system. The forecasts may be based on traffic projections at a given level of granularity. In some embodiments, the generation of the forecasts may take into consideration a type of traffic (e.g., voice and video), and elasticity in terms of data volume at different priority levels/classes.

The QoS-aware dimensioning engine212bmay be operative on the outputs of the load-aware dimensioning engine204band the forecasting engine208bto provide/generate dimensioned resource allocations. The resource allocations generated by the QoS-aware dimensioning engine212bmay be provided as input to the CA-enhanced dimensioning engine216b. The CA-enhanced dimensioning engine216bmay be operative on the dimensioned resource allocations received from the QoS-aware dimensioning engine212bto generate refined/enhanced dimensioned resource allocations. Aspects of the refinement/enhancement of the dimensioned resource allocations provided by the CA-enhanced dimensioning engine216bmay conform to aspects of this disclosure set forth below.

As described above, aspects of the disclosure may incorporate/segment traffic into multiple, different priority classes. In the examples that follow, it may be assumed that there are two guaranteed traffic classes (corresponding to an index of k=0 and k=1). For example, the guaranteed traffic classes may correspond to: (1) conversational voice (k=0), e.g., voice over internet protocol [VoIP], and (2) conversational video (k=1), e.g., live streaming. Still further, it may be assumed that there are four elastic data classes (corresponding to indices of k=2 through k=5). The elastic data classes each may correspond to/include any combination of buffered video, email, text (documents, chat), file transfers, peer-to-peer file sharing, progressive video, and interactive gaming. One skilled in the art would appreciate that these assumptions may be relaxed to provide for more or less guaranteed traffic classes and/or more or less elastic data classes. For example, in some embodiments there may be up to ‘M’ number of different classes, where each class may be associated with/assigned a respective index ‘k’ from k=0 through k=M−1. This disclosure concerns with network resource dimensioning for elastic traffic classes with distinct QOE targets, and supporting both carrier aggregated and non-aggregated UE devices. Traffic estimates corresponding to the two guaranteed traffic classes (k=0,1) furnished by the forecasting engine208bmay be subtracted from the capacity Ciof each cell i=1, . . . , Ψ in the load balancing group, to estimate the respective portion Diavailable to carry elastic data.

Given the elastic data capacity D in a given individual cell (estimated from the procedures as outlined above), and assuming that a priority class k incurs a resource utilization Qk(which equals the class k traffic volume Jkin, e.g., Mbps divided by the elastic data capacity D), the average communication device throughput Tkfor each of the M−2 elastic priority classes may be determined/calculated in accordance with the following system of equations (hereinafter referred to as the Fayolle equations or matrix formula):
xk×[1−Σj((Qj×wj)/(wj+wk))]−[Σj((Qj×wj×xj)/(wj+wk))]=1/D,

where the summation operator (Σj) is applied over all ‘j’ from j=2 to j=M−1, and there exists a distinct linear equation in the system, for each value of k=2, . . . , M−1 (guaranteed classes 0 and 1 excluded). The value wkis representative of a weight applied to the kthpriority class. The weights {wk} may be used to prioritize a first class (e.g., k=2) relative to the other classes (e.g., k=3, 4, 5, etc.). Once the values xkare known/computed, the throughputs (Tk) may be obtained as the inverse of those values, i.e., Tk=1/xk, k=2, . . . , M−1.

Aspects of this disclosure may utilize the principles set forth above to derive/obtain throughput values for CA and non-CA devices when CA and non-CA devices co-exist with one another (e.g., are simultaneously operative) in a given network or system. In particular, attributes of CA devices may be mathematically abstracted by treating as special/separate QoS/priority classes with dynamic priority weights. The relative weights/weightings among the different CA QoS classes may remain constant. The non-CA devices may utilize, or be assigned, static priority weights as part of the methodology. The CA weights may be determined in accordance with characteristics of the CA traffic, one or more scheduling algorithms, and/or load balancing policies among the different carriers. As described in further detail below, the CA weights may be initialized in proportion to the capacities of the different carriers in an aggregation group, and then updated/modified via an iterative process until steady-state values are obtained upon a condition of convergence.

As described herein, the CA devices may transmit data/traffic across multiple carriers. The throughput of the CA devices may be represented as the aggregated throughput over all of the carriers of the group of carriers utilized by the CA devices. When a proportionally-fair scheduler is employed for the CA devices in a given cell, the instantaneous dynamic scheduler priority weight of a CA device belonging to priority class k (wk) would be given by:
wk=gk×(Tk/T),

where gkdenotes the static component of priority accorded to class k (for both CA and non-CA), Tkdenotes the average throughput that the class k CA device is receiving at this cell, and T denotes the aggregate average throughput that this device is receiving from all cells that it is concurrently connected to. In contrast to the foregoing, each non-CA device belonging to class k has its scheduler priority statically set at the value wk=gk. Thus, in accordance with aspects of this disclosure, a scheduling priority for a CA device in relation to each individual carrier utilized by the CA device may effectively incur a penalty in (e.g., may be reduced by) an amount that is in accordance with (e.g., that is in proportion to) throughput contributions from the other carriers utilized by the CA device. Those skilled in the art will appreciate that while a CA device has relatively unhindered access under light loading conditions, to the full capacity of all cells it is concurrently connected to, the penalty part of its dynamic scheduler weight would effectively neutralize the carrier aggregation advantage under congestion, as may be desired. An iterative update to capture the steady-state values of this penalty component, and hence to accurately estimate the device throughputs of all traffic classes (belonging to non-CA as well as CA), so as to facilitate spectrum resource dimensioning, may form a principle/foundation for the algorithm described below.

In view of the foregoing, traffic from each CA device may have a characteristic of self-load balancing, which is to say that the traffic volume that is distributed to each carrier/cell in an aggregated group of cells (e.g., serving a wireless sector) may be proportional to the throughput that the device obtains from that carrier/cell. As an illustrative example, if there were only CA devices in a given network or system, the traffic distributed to the jthcell may be proportional to Dj/ΣlDl, where Djis the capacity of the jthcell/carrier. This would result in an equal physical resource block (PRB) utilization amongst the different carriers.

In many embodiments (e.g., in many networks or systems), there will be a mix of CA devices and non-CA devices present. In some embodiments, non-CA device traffic may be balanced based on certain criteria (e.g., certain overall performance criteria) after considering the impact of the CA traffic (potentially as part of a background task/methodology). To demonstrate, an example of a non-CA load balancing rule/policy would be to maintain equal resource utilization (e.g., equal PRB utilization) across all carrier bands in an aggregated group of carriers/cells. Other forms/types of rules/policies may be used in some embodiments.

Referring now toFIG. 2C, an illustrative embodiment of a method200cin accordance with various aspects described herein is shown. The method200cmay be implemented/executed as part of one or more algorithms to calculate throughput associated with CA and non-CA devices, which in turn may influence a distribution of traffic as described in further detail below. Blocks/operations (or one or more portions thereof) of the method200cmay be executed iteratively/repeatedly until a convergence is obtained/achieved in terms of the distribution of traffic for different priority classes of traffic. The blocks/operations of the method200cmay be executed, in whole or in part, in conjunction with one or more systems, apparatuses, devices, and/or components, such as for example the systems, apparatuses, devices, and components described herein.

In block204c, inputs to the algorithmic loop are fetched, comprising of (i) the elastic data capacity Diof each cell in the load balanced/carrier aggregated group, i=1, . . . , Ψ; and (ii) the traffic volume Jkand static priority weight gkfor each elastic traffic class k=2, . . . M−1. For purposes of illustration, and continuing the example set forth above where there are four elastic data classes (with indices k=2 through k=5), the static priority weights may be set as g2=8, g3=4, g4=2, and g5=1. The particular values (8, 4, 2, and 1) associated with the weights g2through g5in this example are arbitrary/representative/illustrative, and are not binding for purposes of a general execution of the method200c(e.g., a general execution of the block204c). Additionally, more or fewer than four elastic data classes may be used in some implementations of the method200c/the block204c. Treating CA devices and non-CA devices as distinct entities, would require a total of eight classes (4×2=8) in conjunction with the execution of the method200c. However, in the interests of simplified bookkeeping, only the basic four elastic data classes are explicitly shown, with the distinctions applicable to the respective CA and non-CA components brought out as needed.

In block208c, a critical set of parameters (for load balancing as well as dynamic weight assignment), namely throughput ratios {σki} is initialized. In the course of execution, these parameters may track ratios of CA device throughputs in particular cells to the total system throughputs (on a per-class basis). At the start of execution, in block208c, these values are uniformly initialized to ratios of the cell capacities to the total system capacity (sum of cell capacities), Ci/ΣjCj. Execution of block208cmay facilitate an initial balancing of the load/traffic in the network/system as between CA devices/traffic and non-CA devices/traffic.

In block212c, scheduler priority weights for the non-CA traffic categories may be initialized to the respective static priority weights (on a per-class basis) from block204c. Next, scheduler priority weights for the CA traffic categories may be initialized to the product of the respective static priority weights (on a per-class basis) and throughput ratios (on a per class per cell basis) from block208c. Finally, traffic volumes may be distributed to each cell in proportion to the respective values of throughput ratio (for non-CA as well as CA categories). Thus, in the example considered above, each cell i may have four CA weights corresponding to the CA variants of the four elastic data classes, and four non-CA weights corresponding to the non-CA variants of the four elastic data classes.

In block216c, the complete set of per-cell per-elastic class and per-CA/non-CA average device throughputs, {TkiCA, Tkinon-CA, k=2, . . . , M−1, i=1, . . . , Ψ}, may be calculated using the matrix equations (Fayolle equations) described earlier. For example, the throughput for a carrier/cell may be calculated in accordance with the Fayolle equation set forth above, utilizing the weights of block212cin the first instance and the weights of block236cdescribed below in subsequent instances. As part of block216c, and for the traffic that is attributable to the CA devices, the total system throughput Tkfor each class k may be determined/calculated by summing the individual throughput values {Tki} across the carriers that are included in the aggregated CA group. Finally, the set of throughput ratios {σki} may be updated as ratios of the computed per-cell CA device throughputs to the corresponding system aggregates (on a per-class, per-cell basis)

In block220c, traffic that is attributable to the CA devices may be redistributed to each carrier i based on the throughput ratios calculated in block216c. For example, in block220cthe redistribution of the CA traffic belonging to each class k to the ihcarrier may be in accordance with (e.g., may be in proportion to) the corresponding ratio of the computed CA throughput on that carrier (Tki) to the total class k CA throughput (Tk).

In block224c, traffic that is attributable to the non-CA devices may be redistributed in accordance with one or more load balancing rules/policies, such as for example an equal PRB utilization rule/policy as set forth above. In an exemplary embodiment based on the equal PRB utilization policy, the non-CA traffic splits per cell (to achieve equal PRB utilization) may be computed in terms of the corresponding CA throughput ratios computed in block216cas follows:
Jki=[(Ci/C)+(JkCA/Jknon-CA)((Ci/ΣjCj)−σk,i)],k=2, . . . ,M−1;i=1, . . . ,Ψ

In block228c, a determination may be made if the distribution of traffic has converged. Whether the distribution of traffic has converged may be based on a comparison (e.g., a statistical comparison) between variations in the distribution of the traffic components {JkiCA, Jkinon-CA} (for example, from one visit to block228cto the next—as implied, this requires memorization of values from the past) relative to one or more thresholds. The threshold(s) may be selected to ensure sufficient accuracy on the one hand, while at the same time avoiding unnecessary executions/repetitions of blocks of the method200con the other hand.

If, in block228c, it is determined that the traffic distribution has converged, the method200cmay end as shown in block232cafter outputting the computed device throughput values (which may in turn be compared against performance targets to aid in the resource/spectrum upgrade decision process). Otherwise, flow may proceed from block228cto block236c.

In block236c, the respective values of each of the CA scheduler weights (compare with block212c) for each cell/carrier i and traffic class k may be updated to be equal to the product of the respective static weight and the current value of the respective CA throughput ratio (the ratio σki=Tki/Tkas determined in block220c). Note that the static scheduler weights for the non-CA traffic components remain unchanged from their initial values set in block212c. From block236c, flow may proceed to block216c.

As described herein, aspects of the disclosure may be utilized to allocate resources. When a network is lightly loaded, CA devices may utilize as much/as many resources as they are able to in order to increase throughput and enhance QoE/QoS. Conversely, when the network is heavily loaded, the CA devices may be forced to forego utilizing resources in order to avoid depleting resources (thereby ensuring such resources are available for non-CA devices). Resource allocations as between CA devices and non-CA devices may adapt in response to changes in network conditions (e.g., changes in the amount of traffic or load in the network), by virtue of the dynamic penalization factor in the CA scheduler weights and the self-load-balancing property of CA traffic.

Aspects of this disclosure may apply/utilize (static portions of) scheduler weights that are representative of relative priorities/classes of traffic. As described herein, such weights may be used to allocate a greater share of resources to higher grades/classes of traffic.

Aspects of this disclosure are directed to apparatuses and methods for estimating throughputs associated with communication devices, such as user equipment and client devices. In particular, aspects of this disclosure include a calculation of throughput values for co-existing CA devices and non-CA devices in a system or network. As demonstrated herein, the throughput values may be utilized to allocate/distribute traffic between cells/carriers.

While some of the specific, example embodiments set forth herein provide for resource or traffic allocations/distributions on the basis of a device being a CA device or a non-CA device, the techniques and the methodology set forth herein may be applied more generally in accordance with any parameter/variable associated with a communication device and/or a network. For example, if a first cell has a first device located therein that is capable of processing a first amount of load (e.g., the first device has a first processing capability), and a second cell has a second device located therein that is capable of processing a second amount of load (e.g., the second device has a second processing capability), then the total load may be distributed to each of the cells/devices in proportion to their respective processing capabilities.

Aspects of this disclosure may facilitate a distribution or redistribution of traffic, such as for example CA traffic and/or non-CA traffic, in one or more communication networks or systems. Multiple distributions of such traffic may be distinguished from one another in accordance with an identifier, such as an index. Thus, distributions or redistributions may be referred to herein as a first distribution, a second distribution, a third distribution, a fourth distribution, etc.

Referring now toFIG. 3, a block diagram300is shown illustrating an example, non-limiting embodiment of a virtualized communication network in accordance with various aspects described herein. In particular a virtualized communication network is presented that can be used to implement some or all of the subsystems and functions of communication network100, the subsystems and functions of the systems200aand200c, and the functions of the method200cpresented inFIGS. 1 and 2A-2C. For example, virtualized communication network300can facilitate in whole or in part distributing carrier aggregated traffic amongst a plurality of cells of a network, initializing a first plurality of weights for each cell of the plurality of cells, wherein for each cell, each of the first plurality of weights is associated with a respective traffic class included in a plurality of traffic classes, calculating a throughput of each cell of the plurality of cells, calculating a total throughput of the plurality of cells in accordance with the calculating of the throughput of each cell, and redistributing the carrier aggregated traffic amongst the plurality of cells based on the throughput of each cell and the total throughput. Virtualized communication network300can facilitate in whole or in part calculating a throughput of each cell of a plurality of cells of a communication network, calculating a total throughput of the plurality of cells in accordance with the calculating of the throughput of each cell, and redistributing carrier aggregated traffic amongst the plurality of cells, wherein each cell obtains a respective portion of the carrier aggregated traffic as part of the redistributing in proportion to a ratio of the throughput of the cell relative to the total throughput. Virtualized communication network300can facilitate in whole or in part calculating a throughput of each cell of a plurality of cells of a communication network, calculating a total throughput of the plurality of cells, and distributing, in accordance with a first distribution, carrier aggregated traffic amongst the plurality of cells, wherein each cell obtains a respective portion of the carrier aggregated traffic as part of the first distribution in accordance with the throughput of the cell and the total throughput.

Turning now toFIG. 4, there is illustrated a block diagram of a computing environment in accordance with various aspects described herein. In order to provide additional context for various embodiments of the embodiments described herein,FIG. 4and the following discussion are intended to provide a brief, general description of a suitable computing environment400in which the various embodiments of the subject disclosure can be implemented. In particular, computing environment400can be used in the implementation of network elements150,152,154,156, access terminal112, base station or access point122, switching device132, media terminal142, and/or VNEs330,332,334, etc. Each of these devices can be implemented via computer-executable instructions that can run on one or more computers, and/or in combination with other program modules and/or as a combination of hardware and software. For example, computing environment400can facilitate in whole or in part distributing carrier aggregated traffic amongst a plurality of cells of a network, initializing a first plurality of weights for each cell of the plurality of cells, wherein for each cell, each of the first plurality of weights is associated with a respective traffic class included in a plurality of traffic classes, calculating a throughput of each cell of the plurality of cells, calculating a total throughput of the plurality of cells in accordance with the calculating of the throughput of each cell, and redistributing the carrier aggregated traffic amongst the plurality of cells based on the throughput of each cell and the total throughput. Computing environment400can facilitate in whole or in part calculating a throughput of each cell of a plurality of cells of a communication network, calculating a total throughput of the plurality of cells in accordance with the calculating of the throughput of each cell, and redistributing carrier aggregated traffic amongst the plurality of cells, wherein each cell obtains a respective portion of the carrier aggregated traffic as part of the redistributing in proportion to a ratio of the throughput of the cell relative to the total throughput. Computing environment400can facilitate in whole or in part calculating a throughput of each cell of a plurality of cells of a communication network, calculating a total throughput of the plurality of cells, and distributing, in accordance with a first distribution, carrier aggregated traffic amongst the plurality of cells, wherein each cell obtains a respective portion of the carrier aggregated traffic as part of the first distribution in accordance with the throughput of the cell and the total throughput.

Turning now toFIG. 5, an embodiment500of a mobile network platform510is shown that is an example of network elements150,152,154,156, and/or VNEs330,332,334, etc. For example, platform510can facilitate in whole or in part distributing carrier aggregated traffic amongst a plurality of cells of a network, initializing a first plurality of weights for each cell of the plurality of cells, wherein for each cell, each of the first plurality of weights is associated with a respective traffic class included in a plurality of traffic classes, calculating a throughput of each cell of the plurality of cells, calculating a total throughput of the plurality of cells in accordance with the calculating of the throughput of each cell, and redistributing the carrier aggregated traffic amongst the plurality of cells based on the throughput of each cell and the total throughput. Platform510can facilitate in whole or in part calculating a throughput of each cell of a plurality of cells of a communication network, calculating a total throughput of the plurality of cells in accordance with the calculating of the throughput of each cell, and redistributing carrier aggregated traffic amongst the plurality of cells, wherein each cell obtains a respective portion of the carrier aggregated traffic as part of the redistributing in proportion to a ratio of the throughput of the cell relative to the total throughput. Platform510can facilitate in whole or in part calculating a throughput of each cell of a plurality of cells of a communication network, calculating a total throughput of the plurality of cells, and distributing, in accordance with a first distribution, carrier aggregated traffic amongst the plurality of cells, wherein each cell obtains a respective portion of the carrier aggregated traffic as part of the first distribution in accordance with the throughput of the cell and the total throughput.

Turning now toFIG. 6, an illustrative embodiment of a communication device600is shown. The communication device600can serve as an illustrative embodiment of devices such as data terminals114, mobile devices124, vehicle126, display devices144or other client devices for communication via either communications network125. For example, computing device600can facilitate in whole or in part distributing carrier aggregated traffic amongst a plurality of cells of a network, initializing a first plurality of weights for each cell of the plurality of cells, wherein for each cell, each of the first plurality of weights is associated with a respective traffic class included in a plurality of traffic classes, calculating a throughput of each cell of the plurality of cells, calculating a total throughput of the plurality of cells in accordance with the calculating of the throughput of each cell, and redistributing the carrier aggregated traffic amongst the plurality of cells based on the throughput of each cell and the total throughput. Computing device600can facilitate in whole or in part calculating a throughput of each cell of a plurality of cells of a communication network, calculating a total throughput of the plurality of cells in accordance with the calculating of the throughput of each cell, and redistributing carrier aggregated traffic amongst the plurality of cells, wherein each cell obtains a respective portion of the carrier aggregated traffic as part of the redistributing in proportion to a ratio of the throughput of the cell relative to the total throughput. Computing device600can facilitate in whole or in part calculating a throughput of each cell of a plurality of cells of a communication network, calculating a total throughput of the plurality of cells, and distributing, in accordance with a first distribution, carrier aggregated traffic amongst the plurality of cells, wherein each cell obtains a respective portion of the carrier aggregated traffic as part of the first distribution in accordance with the throughput of the cell and the total throughput.