Model for optimizing management of sector carriers

Abstract

A method performed by an electronic device for assigning computing resources for managing sector carriers in a telecommunication network. The method comprises constructing a linear programming model based at least on a set of information and a plurality of models. The set of information includes at least a first information on a current assignment of the sector carriers to computing resources. The plurality of models include: a first model including a first binary decision variable on the current assignment and a second binary decision variable on a proposed assignment. The first model provides a component of an objective function of the linear programming model. The method comprises solving the LP model to obtain a solution. The solution includes a final assignment for the sector carriers to the computing resources. The method comprises generating an assignment plan for the sector carriers corresponding to the solution.

Description

MODEL FOR OPTIMIZING MANAGEMENT OF SECTOR CARRIERSTECHNICAL FIELD

[0001] Embodiments of the invention relate to the field of mobile networks; and more specifically, to techniques for assigning computing resources for managing sector carriers in a telecommunication network.BACKGROUND ART

[0002] Cellular telecommunication networks, sometimes referred to herein as “mobile networks,” are relatively large networks encompassing a large number of wireless devices to enable other wireless devices (sometimes referred to as “user equipment” (UE) or “mobile devices”) to connect wirelessly to the mobile network. The mobile network is also typically connected to one or more other networks, e.g., the Internet. The mobile network enables the wireless devices currently connected to the mobile network to communicate over the network(s) with other wireless devices. The mobile network is designed to allow the mobile devices, e.g., mobile phones, tablets, laptops, loT devices and similar devices, to shift connection points with the mobile network in a manner that maintains continuous connections for the applications of the mobile devices. Typically, the mobile devices connect to the mobile network via radio access network (RAN) base stations, sometimes referred to as “access points”, which provide connectivity to a number of mobile devices for a local area or “cell”. Managing and configuring the mobile network including the cells of the mobile network is an administrative challenge as each cell can have different geographic and / or technological characteristics.

[0003] In modern implementations of mobile networks, such as 5G, 6G, or beyond, a number of different resources such as radio resources and computing resources, e.g., compute, storage, transport, are provisioned to provide communication connections and services to a number of mobile devices. Achieving suitable performance of the mobile network, such as service availability and service assurance, may require the real-time prioritization of the different provisioned resources. The prioritization may be challenging, as a finite amount of resources are balanced with a number of competing and sometimes contradicting objectives while still meeting requirements on resiliency, mobility, server utilization, migration and / or energy efficiency requirements of the mobile network. For example, in a cloud-based RAN, resources are provisioned for virtualized distributed units (DUs), centralized units providing user plane functionality (CU-UP), centralized units providing control plane functionality (CU-CP), and the routing of enhanced Common Public Radio Interface (eCPRI) traffic to the virtualized DUs in Hub sites. The cloud-based RAN may have contradicting objectives, such as a high mobilityrequirement that incentivizes hosting multiple virtualized DU instances on a single server, and a resiliency requirement that incentivizes hosting the multiple virtualized DU instances on different servers.

[0004] For implementations of mobile networks in 5G and beyond, efficient and robust end-to- end resource provisioning that involves radio, compute, storage, and transport, it is a key challenge to ensure service availability. Additionally, real-time prioritization of the computing resources to achieve a high level of performance and resilience across all domains is a must for service assurance.

[0005] There are no existing solutions that apply a LP modeling approach toward sector carrier assignment and re-assignment in telecommunication. For example, in US8027260(B2) a mixed- integer programming model for minimizing leased access network costs is presented and is achieved by applying optimal network configuration mapping produced by a constructed MIP model. US20200357293(Al) presents an integer programming-based method and apparatus for outputting information toward flights-airport gates mapping optimization. US20220037883(Al) discloses a power distribution network reliability index calculation method based on mixed- integer LP model, and provides a power distribution network reliability index calculation method based on mixed-integer linear programming.SUMMARY

[0006] According to a first aspect, there is provided a method performed by an electronic device for assigning computing resources for managing sector carriers in a telecommunication network. The method comprises constructing a linear programming model based at least on a set of information and a plurality of models. The set of information includes at least a first information on a current assignment of the sector carriers to computing resources. The plurality of models include: a first model including a first binary decision variable on the current assignment and a second binary decision variable on a proposed assignment. The first model provides a component of an objective function of the model. The method comprises solving the LP model to obtain a solution. The solution includes a final assignment for the sector carriers to the computing resources. The method comprises generating an assignment plan for the sector carriers corresponding to the solution.

[0007] According to a second aspect, there is provided an electronic device for assigning computing resources for managing sector carriers in a telecommunication network. The electronic device comprises a memory and a processor, the memory comprising instructions which when executed by the processor, cause the electronic device to construct a linear programming model based at least on a set of information and a plurality of models. The set of information includes atleast a first information on a current assignment of the sector carriers to computing resources. The plurality of models include: a first model including a first binary decision variable on the current assignment and a second binary decision variable on a proposed assignment. The first model provides a component of an objective function of the linear programming model. The memory comprising instructions which when executed by the processor, cause the electronic device to solve the LP model to obtain a solution. The solution includes a final assignment for the sector carriers to the computing resources. The memory comprising instructions which when executed by the processor, cause the electronic device to generate an assignment plan for the sector carriers corresponding to the solution.

[0008] According to a third aspect, there is provided a computer program comprising instructions which, when executed on an electronic device, cause the electronic device to carry out a method according to the first aspect.

[0009] According to a fourth aspect, there is provided a computer program product comprising a computer readable storage means on which the computer program according to the third aspect is stored.BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The invention may best be understood by referring to the following description and accompanying drawings that are used to illustrate embodiments of the invention. In the drawings:

[0011] Figure 1 illustrates a block diagram depicting different scenarios for managing migration of one or more sector carriers, according to one or more embodiments.

[0012] Figure 2 illustrates a flow diagram depicting generating an assignment plan for sector carriers based on constructing and solving a linear programming model, according to one or more embodiments.

[0013] Figure 3 illustrates a method performed by an electronic device for assigning computing resources for managing sector carriers in a telecommunication network, according to one or more embodiments.

[0014] Figure 4 illustrates exemplary hierarchy of computing resources including virtualized resources at one or more levels of virtualization according to one or more embodiments.

[0015] Figure 5 illustrates a communication system for assigning computing resources for managing sector carriers in a telecommunication network, according to one or more embodiments.

[0016] Figure 6 illustrates an electronic device for assigning computing resources for managing sector carriers in a telecommunication network, according to one or more embodiments.DETAILED DESCRIPTION

[0017] The present disclosure relates to sector carrier optimization for Cloud RAN through migration. The aspects of runtime and reassurance for events that can be received from the RAN domain are considered. As examples, an event may be: 1) situational changes where some of the sector carriers are removed from the system; 2) date / time-based changes in the amount of traffic, e.g., rush hours vs normal hours, weekends / holidays vs business days, etc. In these cases, migration of existing sector carriers may be done to find their optimal or near-optimal assignments or re-assignment to computing resources, in a way such that the server utilization is improved, e.g., maximized, and / or requirements between sector carriers are satisfied. This is done with an aim of balancing the number of migration steps, e.g., utilizing minimum migration steps, with the additional overhead associated with the migration. A linear programming (LP) model for sector carrier optimization is created that considers one or more of: the current assignments, capacity of the resources, number of migration steps, resiliency, and mobility. The LP model may be solved to run global optimizations for tens of sector carrier demands. The LP model may be run in an iterative manner to re-optimize the assignments to achieve the maximum possible performance gain in the system. Further, at least some aspects of the present disclosure enable maximizing server utilization by shutting down some computing resources and concentrating sector carriers assignments in as minimum as possible computing resources.

[0018] The following description describes methods and apparatus for assigning computing resources for managing sector carriers in a telecommunication network, provided by one or more electronic devices. In the following description, control structures, gate level circuits and full software instruction sequences have not been shown in detail in order not to obscure the invention. Those of ordinary skill in the art, with the included descriptions, will be able to implement appropriate functionality without undue experimentation.

[0019] References in the specification to “one embodiment,” “an embodiment,” “an example embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.

[0020] Bracketed text and blocks with dashed borders, e.g., large dashes, small dashes, dot-dash, and dots, may be used herein to illustrate optional operations that add additional features to embodiments of the invention. However, such notation should not be taken to mean that these arethe only options or optional operations, and / or that blocks with solid borders are not optional in certain embodiments of the invention.

[0021] The operations in the flow diagrams will be described with reference to the exemplary embodiments of the other figures. However, it should be understood that the operations of the flow diagrams can be performed by embodiments of the invention other than those discussed with reference to the other figures, and the embodiments of the invention discussed with reference to these other figures can perform operations different than those discussed with reference to the flow diagrams.

[0022] As used herein, an integer variable may refer to a variable that may take only integer values. An integer decision variable is an integer variable used to model decision-making processes wherein the decision choice may be whole numbers.

[0023] As used herein, a binary decision variable may refer to a variable whose only possible values are 0 and 1. A binary decision variable may be used to represent yes / no decisions. A binary decision variable may be a specific type of integer decision variable that can only take two values: 0 and 1.

[0024] As used herein, a pod may refer to smallest deployable unit of computing that may be created and managed in a container orchestration system (COS), such as Kubernetes, Openshift, Nomad (by Hasicorp), Docker Swarm, etc. A pod may include one or more virtual containers with shared storage and network resources, and a specification for how to run the virtual container(s). A pod’s content may be co-located, co-scheduled and run in a shared context.

[0025] In various embodiments described herein, an LP model may be constructed. The LP model may consider a set of information, for e.g., information on a current assignment of the sector carriers to computing resources, and a plurality of models each including a number of different constraints that are competing with and sometimes contradicting each other.

[0026] The LP model may further consider several requirements or constraints, such as resiliency, mobility, server utilization, or maximum number of migrations allowed, in relation to the sector carriers and their assignment to computing resources. These requirements or constraints may be modelled into a plurality of models that may include a migration model representing requirements on re-assignments of sector carriers to computing resources starting from current assignments, a mobility model representing mobility requirements between different sector carriers, a resiliency model representing resiliency requirements between different sector carriers, a computing resource reduction model representing the number of computing resources to be reduced, a capacity model representing processing capacity of a physical server or a pod and a current assignment model representing current assignment of sector carriers to computing resources. Some of the models may impose contradictory requirements, for example: migrationof two different sector carriers executing in two different pods to a single pod i.e., a new assignment, may result in better mobility but lower resiliency than the current assignment of the sector carriers executing in two different pods. That is, resiliency and mobility may be contemplated as contradictory requirements.

[0027] The LP model may then be solved to obtain a solution. The solution includes an assignment of the sector carriers to the computing resources. An assignment plan based on the solution may then be generated for deploying in the telecommunication network.

[0028] Figure 1 illustrates an electronic device 110 for assigning computing resources for managing a plurality of sector carriers of a telecommunication network, according to one or more embodiments.

[0029] Figure 1 illustrates the electronic device 110 including a sector carrier assignment manager 111. The sector carrier assignment manager 111 includes a set of information and a plurality of models. The set of information includes a first information 104-1 on a current assignment of the sector carriers to computing resources. The set of information may optionally include a second information 104-2 on mobility requirements and / or resiliency requirements for sector carriers. The set of information may optionally include a third information 104-3 describing the characteristics of the computing resources. The first model 105-1 may, for example, be the migration model as described previously. For instance, in some embodiments the plurality of models includes a second model 105-2 where the second model is at least one of: the resiliency model 105-2a, the mobility model 105-2b. In some embodiments, the plurality of models further includes a third model, wherein the third model is the computing resource reduction model 105- 3. In some embodiments, the plurality of models includes the capacity model 105-4 and / or the current assignment model 105-5. In some embodiments, the plurality of models include all of the above described models.

[0030] The sector carrier assignment manager 111 utilizes the set of information and the plurality of models to construct a LP model. The solution obtained by solving the LP model may be used to generate an assignment plan for assigning computing resources to the sector carriers, as will be described later in the application in relation to Figure 2 and Figure 3.

[0031] Figure 1 further illustrates four scenarios identified by the reference sign A, B, C and D, depicting the assignment of computing resources for managing sector carriers by the sector carrier assignment manager 111. It will be appreciated that the four scenarios are merely examples, and that different embodiments may support more, less, and / or different scenarios or combinations thereof. Each of the depicted scenarios include at least one physical server, for e.g., physical server 1 101-1, one or more virtual containers or pods comprised in the physical server, e.g., pod 102-1and pod 102-2, and one or more sector carriers (SCs) assigned to the virtual containers or pods, e.g., SCI 103-1, SC2, 103-2.

[0032] Referring to scenario A of Figure 1 , there is illustrated a scenario of migration of a SC from one pod / virtual container to another pod / virtual container within the same physical server. The sector carrier SCI 103-1 has a current assignment to a pod 102-1 in a physical server 1 101 -1. The sector carrier assignment manager 111, based on the set of information and the plurality of models, proposes a new assignment such that the sector carrier SCI 103-1 is migrated from a pod 102-1 to a pod 102-2, wherein the pod 102-2 already has an existing assignment of a sector carrier SC2-103 in the same physical server 1 101-1. The new assignment of the SCs 103-1, 103-2 to the single pod 102-2, may be carried out to improve, for example, mobility among the two SCs 103-1, 103-2. Further, as result of this migration, it will be possible to shut down, as indicated in the Figure 1 by ‘X’, the pod 102-1 and have only one pod i.e., the pod 102-2 running. The new assignment, may thus, be provided by the sector carrier assignment manager 111 to improve the overall performance, e.g., resource utilization, in the telecommunication network 510 by providing optimized assignment plans for the sector carriers. It may be noted that the new assignment in scenario A may lead to a decrease in resiliency compared to the previous assignment because the two SCs are now assigned to a single pod instead of two different pods and thus more susceptible to failures. On the other hand, the new assignment in scenario A may lead to an increase in mobility compared to the previous assignment since both the SCs are assigned in the same pod. Further, as described later herein, certain embodiments support an LP model configured to have a hard constraint for the mobility model 105 -2b and a soft constraint for the resiliency model 105-2a, thereby resulting in the assignment plan of scenario A.

[0033] Now referring to scenario B of Figure 1 , there is illustrated a scenario of migration of a SC from one physical server to another physical server. The sector carrier SCI 103-1 has a current assignment to a pod 102-1 in a physical server 101-1. The sector carrier assignment manager 111, based on the set of information and the plurality of models, proposes a new assignment such that the sector carrier SCI 103-1 is migrated from a pod 102-1 in the physical server 101-1 to a pod 101-2 in a different physical server 101-2, wherein the pod 102-2 already has an existing assignment of a sector carrier SC2-103. That is, in scenario B a new assignment is made by migrating the sector carrier SCI 103-1 from one physical server to a different physical server. The new assignment of the SCs 103-1, 103-2 to the single pod 102-2 in the same physical server 101-2, may be carried out to improve, for example, mobility among the two SCs 103-1, 103-2. Further, as result of this migration, it will be possible to shut down, indicated in the Figure 1 by ‘X’, the physical server 101-1 and have only one physical server running i.e., the physical server 101-2 running. The new assignment, may thus, be provided by the sector carrier assignment manager111 to improve the overall performance, e.g., resource utilization, in the telecommunication network 510 by providing optimized assignment plans for the sector carriers. It may be noted that the new assignment in scenario B may lead to a decrease in resiliency compared to the previous assignment because the two SCs are now assigned to a single physical server instead of two different physical servers and thus more susceptible to failures. Further, as described later herein, certain embodiments support an LP model configured to have a hard constraint for the mobility model 105-2b and a soft constraint for the resiliency model 105-2a, thereby resulting in the assignment plan of scenario B.

[0034] Now referring to scenario C of Figure 1 , there is illustrated a scenario of migration of a SC from an existing pod / virtual container to a newly created pod / virtual container within the same physical server. The sector carriers SCI 103-1, SC2 103-2 have a current assignment to a pod 102-3 in a physical server 101-1. The sector carrier assignment manager 111, based on the set of information and the plurality of models, proposes a new assignment such that the sector carrier SCI 103-1 is migrated from the pod 102-3 in the physical server 101-1 to a pod 102-1. The pod 102-1 is newly created or spawned to facilitate the assignment of the sector carrier SCI 103-1 to the pod 102-1. The assignment of the SCs 103-1, 103-2 to the pods 102-1, 102-3 respectively in the same physical server 101-2, may be carried out to improve, for example, resiliency among the two SCs 103-1, 103-2 in the telecommunication network 510. The new assignment, may thus, be provided by the sector carrier assignment manager 111 to improve the overall performance, e.g., resiliency, in the telecommunication network 510 by providing optimized assignment plans for the sector carriers. It may be noted that the new assignment in scenario C may lead to a decrease in mobility between the SCs 103-1, 103-2 compared to the previous assignment because the two SCs are now assigned to two different pods 102-1, 102-3. On the other hand, the new assignment in scenario C may lead to an increase in resiliency between the SCs 103-1, 103-2 compared to the previous assignment. Further, as described later herein, certain embodiments support an LP model configured to have a soft constraint for the mobility model 105 -2b and a hard constraint for the resiliency model 105-2a, thereby resulting in the assignment plan of scenario C.

[0035] Now referring to scenario D of Figure 1 , there is illustrated a scenario of migration of sector carriers from two different pods to a single pod in the same physical server. The sector carrier SCI 103-1 has a current assignment to a pod 102-1 in a physical server 101-1, and a sector carrier SC2 103-2 has a current assignment to a pod 102-2 in a physical server 101-1. The sector carrier assignment manager 111, based on the set of information and the plurality of models, proposes a new assignment such that both the sector carriers SCI 103-1, SC2 103-2 are migrated from their respective pods to a single pod 102-3 in the same physical server. The pod 102-3 is newly created or spawned to facilitate the assignment of the sector carriers 103-1, 103-2 to thepod 102-3. As a result of the new assignment, the pods 102-1, 102-2 may now be shut down, indicated in the Figure 1 by ‘X’. The new assignment may be carried out to improve, for example, mobility among the two SCs 103-1, 103-2 in the telecommunication network 510 and reduce pod resource utilization. The new assignment, may thus, be provided by the sector carrier assignment manager 111 to improve the overall performance, e.g., resource utilization, in the telecommunication network 510 by providing optimized assignment plans for the sector carriers. It may be noted that the new assignment in scenario D may lead to a decrease in resiliency between the SCs 103-1, 103-2 compared to the previous assignment because the two SCs are now assigned to the same pod 102-3. Further, as described later herein, certain embodiments support an LP model configured to have a hard constraint for the mobility model 105-2b and a soft constraint for the resiliency model 105-2a and the capacity model 105-4, thereby resulting in the assignment plan of scenario D.

[0036] In the scenarios described above, depending on which models are included, the sector carrier assignment manager 111 may take into consideration the overall requirements from the different models and generate an assignment plan that may result in an overall improvement in the performance in the telecommunication network 510, even though certain assignments may have a higher penalty associated with certain models.

[0037] It may be noted that the sector carrier assignment manager 111 may consider a combination of the different scenarios presented above and generate the assignment plan. In some embodiments, the procedure for generating the assignment plan includes optimizing a solution of the LP model constructed using the set of information and the plurality of models, wherein the solution takes into account the different assignment scenarios presented above, and generates an optimized assignment plan for the sector carriers corresponding to the solution.

[0038] In one or more embodiments described herein, the sector carrier assignment or reassignment problem may be modeled as an offline optimization problem using linear programming. More specifically, the assignment of multiple levels of computing resources, e.g., including virtualized resources at one or more levels of virtualization, may be modeled as a hierarchical bin packing problem. In an exemplary hierarchical bin packing problem, sector carriers of the mobile network are assigned to radio access network (RAN) network functions such as virtualized DUs, virtualized CU-UP and virtualized CU-CP, and the RAN network functions are assigned to virtual containers or pods (one example of virtualization units) of physical servers (one example of physical computing units).

[0039] Figure 2 illustrates a flow diagram for sector carrier assignment manager 111 depicting generating an assignment plan for assigning sector carriers to computing resources based onconstructing and solving a LP model, according to one or more embodiments. Figure 2 is split into 2 portions of the flow diagram: a top portion and a bottom portion.

[0040] In the top portion, a model constructor 210 constructs a LP model 201 based on a set of information and a plurality of models, also referred to as component models or sub-LP models. As mentioned above, the set of information includes a first information 104-1 on a current assignment of the sector carriers to computing resources, and optionally, includes a second information 104-2 on mobility requirements and / or resiliency requirements for sector carriers, and a third information 104-3 describing the characteristics of the computing resources. Further, as stated earlier, the plurality of models includes the migration model 105-1 and one or more other models, such as the resiliency model 105-2a, the mobility model 105-2b, the computing resource reduction model 105-3, the capacity model 105-4, and the current assignment model 105-5. The capacity model 105-4 may further comprise a container capacity model 105-4b and a sever capacity model 105 -4a.

[0041] One or more of the plurality of models may be based on one or more of the set of information. For example, the migration model 105-1 may be constructed using the first information 104-1 and optionally the second information 104-2. The resiliency model 105-2a, the mobility model 105-2b, and the computing resource reduction model 105-3 may be constructed using the first information. The current assignment model may be constructed using the first information 104-1. The capacity model 105-4 may be constructed using the third information 104- 3. Further, the model constructor 210 may receive input 200 from different devices in the telecommunication network 510. The input 200 may, for example, relate to physical system characteristics, or available computing resources such as server or pod resources in the telecommunication network 510.

[0042] Each of the models 105-1, 105-2a, 105-2b, 105-3, 105-4, 105-5 provide a component of the objective function of the LP model 201. Each component of the objective function may be based on one or more constraints associated with the model, one or more decision variables, e.g., integer decision variables and / or binary decision variables, associated with the model, and a penalty term associated with the model. In one or more embodiments, the constraints may, for example, be modelled as zero or more “hard” constraints that are required to be satisfied at all times, or one or more “soft” constraints which are required to be satisfied as much as possible. In one or more embodiments, if the soft constraint(s) is not satisfied, one or more penalty terms are provided to the objective function of the LP model. In some other embodiments, if the hard constraint(s) is not satisfied, a solution of the LP model 201 is not provided and the current assignment is maintained without migrating the SCs.

[0043] The objective function of the LP model 201 is obtained by summing the component provided by each of the models. Further details on the components provided by each of the models will be explained later in relation to Figure 3. The objective function of the LP model 201 may be modeled as a minimization problem.

[0044] A solver 202 solves the LP model 201 based on the objective function to obtain an assignment of the SCs to computing resources. The solver 202 may, for example, solve using an iterative hierarchical clustering algorithm. The solver 202 may solve iteratively until a defined stopping criteria 203 is met. While in some embodiments the stopping criteria 203 is a dynamic stopping criteria based on a predefined gain value, that is the solver 202 stops solving iteratively once the gain from a new assignment of sector carriers to computing resources meets the predefined gain value, alternative embodiments may use other stopping criteria 203, e.g., if the possible gain from the assignments is larger than a threshold, etc.

[0045] A solution 204 is then obtained as output. The solution 204 includes a final assignment for the sector carriers to the computing resources. The solution 204 is sent to an assignment plan generator 205 that generates an assignment plan for the sector carriers corresponding to the solution 204. The generated SC assignment / migration plan is sent to an entity to perform a deployment operation that includes deploying the sector carriers on the computing resources based on the optimal solution.

[0046] Now referring to the bottom portion of the flow diagram of Figure 2, there is depicted an optional procedure for providing an indication to the model constructor 210 to either use or not use the computing resource reduction model 105-3 for constructing the LP model 201. This component of the LP model 201 is to take into consideration the possibility of reducing the number of physical servers used in the solution 204, and thus allowing for the shutting down of one or more physical servers. Thus, an advantage of this procedure is that it may enable maximizing physical server utilization in the telecommunication network 510 by optimizing the assignment of sector carriers in as minimum as possible physical servers. This was illustrated in, for example, the scenario B of Figure 1.

[0047] In further detail, the procedure starts with constructing an LP model 221 that is based on the resiliency model 105-2a and the capacity model 105-4. The models 105-2a, 105-4 may be the same ones as used in the model constructor 210. The LP model 221 may be constructed by a model constructor 220. The models 105-2a, 105-4 each provide a component of the objective function of the LP model 221. The component of the objective function may be based on one or more constraints associated with the model, one or more decision variables, e.g., integer decision variables and / or binary decision variables, associated with the model, and a penalty term associated with the model. The constraint may be a constraint on a proposed assignment denotedby xcpG {0,1} that indicates whether a sector carrier SC is assigned to pod p and physical server s i.e., a new / proposed assignment.

[0048] The objective function of the LP model 221 is obtained by summing the component provided by each of the models. Further details on the components provided by each of the models will be explained later in relation to Figure 3. The objective function of the LP model 221 may be modeled as a minimization problem.

[0049] A solver 222 solves the LP model 201 based on the objective function to obtain an assignment of the SCs to computing resources.

[0050] An estimator 223 estimates the minimum number of required physical servers. By doing this, the estimator 223 may estimate the minimum number of physical servers required to satisfy all resiliency requirements. As depicted by block 224, if the minimum number of proposed physical servers is greater than or equal to the current number of physical servers in the current SC assignment, then the computing resource reduction model 105-3 is removed from the model constructor 210. Otherwise, the computing resource reduction model 105-3 is maintained.

[0051] It will be appreciated that the bottom portion of the flow diagram of Figure 2 is optional and the top portion of the flow diagram of Figure 2 may, as such, be executed without requiring the execution of the bottom portion. However, in some embodiments where the bottom portion of the flow is executed, the bottom portion is then executed prior to the execution of the top portion of the flow diagram of the Figure 2 and / or in conjunction with the execution of the model constructor 210. Further, the bottom portion may not consider information on current assignment while constructing the LP model, rather it may consider a possible deployment assuming there is nothing previously deployed. Furthermore, the resulting solution 204 may use more than the minimum number of physical servers determined by the estimator 223 depending on the other components of the LP model 201 including if achieving the goal of minimum number of servers would require too many migrations according to the migration models 105- l's component and / or the stopping criteria 203.

[0052] Now referring to Figure 3, there is depicted a flowchart of a method 300 performed by an electronic device 110 for assigning computing resources for managing sector carriers in a telecommunication network 510. The method 300 includes a number of steps some of which are optional. The method 300 comprises constructing 301 a LP model 201 based at least on the first information 104-1, the first model 105-1, and the second model 105-2. The LP model 201 may, for example, be constructed according to procedures described in Figure 2. The set of information may be provided in any suitable form. For example, the information may be provided to the electronic device 110 as included in a sector carrier assignment request. The set of information may be obtained from the other devices or network functions in the telecommunication network510. In some embodiments, at least some of the set of information are pre-configured in the electronic device 110. The set of information includes at least a first information 104-1 on a current assignment of the sector carriers to computing resources.

[0053] An example is now provided to further describe the set of information. Let SCI, SC2, SC3, and SC4 be the different sector carriers. Let pod 0 and pod 1 be the two pods to which one or more sector carriers are assigned. Let physical server 1 and physical server 2 be the two physical servers to which one or more sector carriers are assigned. Consider an assignment scenario wherein: SCI is mapped to physical server 2 and pod 0; SC2 is mapped to physical server 1 pod 1; SC3 is mapped to physical server 2 pod 1; SC4 mapped to physical server 2 pod 1. Based on this example assignment scenario, the first information may indicate the current assignment of the sector carriers, that is, SCI is mapped to physical server 2 and pod 0; SC2 is mapped to physical server 1 pod 1 ; SC3 is mapped to physical server 2 pod 1 ; SC4 mapped to physical server 2 pod 1.

[0054] The second information may indicate mobility and resiliency requirements between the SCs. For example: SCI and SC2 have high resiliency and high mobility requirements, SC2 and SC3 have high mobility and low resiliency requirement, SCO and SC4 have medium mobility and high resiliency requirement.

[0055] Further, the third information may indicate characteristics of computing resources. The characteristics may include server capacity either in terms of CPU or number of SCs that a physical server may support. As an example here, physical server 1 may host up to 4 SCs. The characteristics may include pod capacity either in terms of CPU or number of SCs it may support. As an example here, each pod may host up to 4 SCs.

[0056] Further, the plurality of models include a first model. The first model provides a component of an objective function of the LP model 201. The component of the objective function may be based on one or more constraints associated with the first model, one or more decision variables, e.g., integer decision variables and / or binary decision variables, associated with the first model, and a penalty term associated with the first model.

[0057] In an embodiment, the first model is the migration model 105-1. A description of the LP model 201 construction based on the migration model 105-1 is provided herein. The migration model aims to minimize the number of physical server or pod reassignments for SCs starting from their current assignment. Thus, if a new assignment for a SC is different from a current assignment, then a high penalty term is provided with the component of the objective function. On the other hand, if they are the same assignment, meaning there is no migration, then a smaller penalty term compared to the previous case is provided to the component of the objective function. This is further expressed mathematically below.

[0058] The first model includes a first binary decision variable on the current assignment and a second binary decision variable on a proposed assignment. As an example, the first binary decision variable on the current assignment is denoted by ycpG {0,1} that indicates the current assignment of a SC to a pod p and a physical server s i.e., current assignment / pre-reassignment. The second binary decision variable on the proposed assignment is denoted by xcpG {0,1} that indicates whether a sector carrier SC is assigned to a pod p and a physical server s i.e., a new / proposed assignment.

[0059] Let SCnbe the nth sector carrier. Considering a SCn, for each physical server there is introduced an integer decision variable reassignsi.e., a component of the objective function.obj+ T eas signs(3) wherein obj is the objective function of the LP model 201 to which a component of the objective function from each of the plurality of models are added. preassignsis apenalty term provided to each component of the objective function.

[0060] The purpose of the above equations is to reduce the number of physical server migrations considering the overhead associated with each migration. If a given SC is migrated to a new physical server, then a penalty value of preassignsmay be provided twice to the component of the objective function. If the SC is kept or assigned on the same physical server, then the penalty value Preassignsmay be provided only once to the objective function. Further, different penalty values may be assigned depending on whether the migration of SC is from one pod to another pod, for example, as shown in scenarios A, C and D of Figure 1, or whether the migration is from one physical server to another physical server for example, as shown in scenario B of Figure 1. For example, the migration of SCs between physical servers may be twice as penalizing as the migration of SCs between the pods on the same physical server. Further, preassignsmaY be assigned different values according to the priorities of the requirements, for example, according to a soft constraint requirement. The soft constraint may be used to minimize the number of migrations, wherein for each migration a penalty term may be added. These mechanisms allow for flexibly configuring the model during model construction.

[0061] Beside minimizing the number of physical server migrations, the model construction based on the migration model 105-1 may also include a hard constraint to limit the number of SC migrations that are allowed to happen. For this, one may calculate the number of times the integer decision variable renibsbsitymi,i is added to the ob Jjective function, ■ e.g ©., ■ renuososi tym I, c » • «- renu.is>is>iym 1,1$The numbermigrationsrepresents the number of SC migrations allowed in the system. To further explain the equation above, for example, if no migration has happened, then the reassignis added only once to the renssim, for each SC. Hence, the sum of renssim, and number^ will beequal. As an example, assume there are 4 SCs, the penalty of SC migration has a value 1, and the number of allowed migrations is 2, then:

[0062] It may be noted that hard constraint may be used to limit the number of migrations, and thus, not allowing for migrations more than a pre-defined value. In the equation (4), the penalty term is introduced for normalizing the equation to be able to correctly determine the number of migrations. The penalty term is not provided to the objective function, in case of hard constraint.

[0063] Similarly, the number of pod migrations may be limited. In some embodiments, a pod migration is considered only when a SC did not migrate from a previously assigned physical server to a new physical server. Considering a SCn, for each server for each pod, let an integer decision variable be reassignp, i.e., a component of the objective function. Then,obi+= renssinn. (8)

[0064] Now referring back to the method 300 of Figure 3, the method comprises solving 302 the LP model 201 to obtain a solution 204 for the LP model 201. The LP model 201 constructed according to one or more procedures described above, may be solved. The LP model 201 may be solved according to one or more procedures described above in relation to Figure 2. The solver 202 of Figure 2 may, for example, solve the LP model 201. The solver 203 may receive the LP model 201 as input and generate a mapping of SCs to computing resources. The solver 203 may then obtain a solution for migrating a SC to i) a new computing resource instance, e.g., vDU, on the same physical server, ii) a new physical server, or keeps a SC on its current computing resource, e.g., vDU, and / or the physical server. The solution 204 may, thus, include a final assignment for the sector carriers to the computing resources.

[0065] In one or more embodiments, the solving 302 comprises solving 303, iteratively, the linear programing model until the solution is obtained. During the iterations, a number of intermediate assignments for the sector carriers to the computing resources may be obtained. The solution 204 includes an optimized solution of the one or more intermediate solutions.

[0066] In one or more further embodiments, the solving 302 further comprises stopping 304 the solving of the LP model 201 if the gain from optimizing reaches or crosses a pre-defined threshold. The stopping 304 may, for example, be based on the stopping criteria 203 described above in relation to Figure 2. The stopping criteria allows minimizing the execution time and increasing the problem size.

[0067] The method 300 further comprises generating 305 an assignment plan for the sector carriers corresponding to the solution 204. The assignment plan may, for example, include a mapping of which sector carriers are assigned to which computing resources. The assignment plan may, for example, be generated by the assignment plan generator 205 of Figure 2 according to the procedures described therein. The generated assignment plan may further be sent to a deployment service.

[0068] In one or more embodiments, generating 306 the assignment plan comprises deploying, in the telecommunication network 510, the assignment plan for the sector carriers corresponding to the solution 204.

[0069] The method 300 may further include some optional steps resulting in further optional embodiments as will be described below.Resiliency model

[0070] In some of the embodiments, one of the plurality of models may, for example, be the resiliency model 105-2a. In one or more embodiments, the set of information includes a second information 104-2 on resiliency requirements for the sector carriers. The set of information may further include a third information 104-3 describing the characteristics of the computing resources.

[0071] The resiliency model 105-2a provides a component of an objective function of the LP model 201. The component of the objective function may be based on one or more constraints associated with the resiliency model 105-2a, one or more decision variables, e.g., integer decision variables and / or binary decision variables, associated with the resiliency model 105-2a, and a penalty term associated with the resiliency model 105-2a. A brief description of the LP model 201 construction based on the resiliency model 105-2a according to some embodiments is provided next.

[0072] The resiliency model 105-2a may include three levels of resiliency. (1) High resiliency which indicates that two SCs with high resiliency requirements between them are placed on different physical servers; (2) medium resiliency which indicates that two SCs with medium resiliency requirements between them are placed on different pods / virtual containers (for example, as shown in scenario C of Figure 1); and (3) low resiliency which indicates that two SCs with low resiliency requirements between them are placed on the same (for example, as shown in scenario D of Figure 1) or different computing resources. In some embodiments, the resiliency model 105-2a may include both soft and hard constraints. Violating high-resiliency requirements modeled as a soft constraint may be accounted for by a penalty term.

[0073] In an embodiment, the resiliency model 105-2a includes a soft constraint for sector carriers with high-resiliency requirements wherein the assignment of the SCs is on different computing resources. However, if the SCs are proposed to be assigned on the same computing resources, meaning the high-resiliency requirement is not satisfied, then a penalty term is provided to the component of the objective function. If they are on different computing resources, meaning the resiliency requirement is satisfied, then no penalty or a smaller penalty compared to the previous case is provided to the objective function. This may be referred to as a group penalty method and is mathematically expressed as follows.

[0074] Let us assume there are 2 SCs with high resiliency requirements (SCn& SCm), let an integer decision variable be reqserverRi.e., a component of the objective function, andG {0,1} indicates whether a sector carrier SC is assigned to POD p and server s (new assignment / migration. Then,obj+= reqserverR* pR (11) wherein obj is the objective function of the LP model 201 to which a component of the objective function from each of the plurality of models are added. pR is a penalty term provided to the component of the objective function.

[0075] The aim is to assign the two SCs to different computing resources. If they are on the same computing resource, then a negative penalty value of (— pR) will be provided to the component of the objective function. If they are on different computing resources, meaning the resiliency requirement is satisfied, then twice the negative penalty value i.e., 2 * (— pR) is provided to the objective function, indicating a smaller penalty. pR may be assigned different values according to the priority of the requirement.

[0076] In another embodiment, the resiliency model 105-2a includes a hard constraint for sector carriers S , SCj with high-resiliency requirements, wherein the SCs S , SCj that are resiliency constrained should be assigned on different servers. Then, for any server i and pod j, the resiliency model 105-2a may be based on the following equation:Mobility model

[0077] In some embodiments, one of plurality of models may, for example, be the mobility model 105-2b. In one or more embodiments, the set of information includes a second information 104-2 on mobility requirements for the sector carriers. The set of information may further include a third information 104-3 describing the characteristics of the computing resources.

[0078] The mobility model 105-2b provides a component of an objective function of the LP model 201. The component of the objective function may be based on one or more constraints associated with the mobility model 105-2b, one or more decision variables, e.g., integer decision variables and / or binary decision variables, associated with the mobility model 105-2b, and a penalty term associated with the mobility model 105-2b. A brief description of the LP model 201 construction based on the mobility model 105-2b according to some embodiments is provided next.

[0079] The mobility model 105-2b may include three levels of mobility: (1) high mobility indicating that that two SCs with high-mobility requirements should share the same pod, e.g., same DU instance, for example, as shown in scenario A of Figure 1, (2) Medium-mobility indicating that SCs with medium-mobility requirements should be placed on the same physical server, not necessarily sharing the same pod, for example, as shown in scenario C of Figure 1 , and (3) low mobility indicating that the SCs may either share or not share the same physical server / pod. The Medium and high-mobility requirements violations may be penalized by a penalty value. The mobility model 105 -2b may further include a soft constraint.

[0080] In an embodiment, the_mobility model 105-2b includes a soft constraint for sector carriers with medium-mobility or high-mobility requirements, wherein the SCs are to be assigned on the same server. Following the same group penalty method mentioned earlier, if both the SCs are not assigned on the same server, meaning that the requirement is not satisfied, then a higher penalty term is provided to the objective function. If they are on the same physical server, meaning that the requirement is satisfied, then a smaller penalty term compared to the previous case is provided to the objective function. This is expressed mathematically below.

[0081] Let us assume there are two SCs SCn, SCmwith high or medium mobility requirements. For each physical server s, let an integer decision variable be reqserverMi.e., a component of the objective function.G {0,1} indicates whether a sector carrier SC is assigned to POD p and server s i.e., a new assignment / migration. Then,obj+ e(JServerM (15)wherein obj is the objective function of the LP model 201 to which a component of the objective function from each of the plurality of models are added. pMsis a penalty term provided to the component of the objective function.

[0082] The aim is to assign the two SCs to the same server. If they are not on the same server, meaning that the mobility requirement is not satisfied, then the penalty value of pM_s is provided twice to the component of the objective function. If they are on the same server, meaning the mobility requirement is satisfied, then pM_s is provided once to the component of the objective function. pM_s may be assigned different values according to the priority of the requirement.

[0083] In an embodiment, the_mobility model 105-2b includes a soft constraint for sector carriers with high-mobility requirements wherein the SCs are to be assigned on the same pod after assigning them to the same physical server. If the SCs are not assigned on the same pod, meaning that the high mobility requirement is not satisfied, then a high penalty term is added to the component of the objective function. If the SCs are assigned on the same pod, meaning that the high mobility requirement is satisfied, then a smaller penalty value compared to the previous case is provided to the component of the objective function. This is expressed mathematically below.

[0084] Let us assume there are two SCs SCn, SCmwith high mobility requirements. For each server s and each pod p on server s, let an integer decision variable be reqpodMi.e., a component of the objective function, then:obj+= reqpodM(18) wherein obj is the objective function of the LP model 201 to which a component of the objective function from each of the plurality of models are added. pMpis a penalty term provided to the component of the objective function.

[0085] This aims to assign the two SCs to the same pod on the same physical server. If the SCs are assigned on the same pod and the same physical server, meaning that the high mobility requirement is satisfied, then the penalty pMpis added to the objective function. If they are assigned on different PODs, meaning the high mobility requirement is not satisfied, then the penalty pMpis added twice to the component of the objective function. pMpmay be assigned different values according to the priority of the requirement. It may be noted that when two SCs with high mobility requirements are not assigned on the same physical server, then it may not be unnecessary to check whether they are assigned to the same pod.Computing resource reduction model

[0086] In some embodiments, one of the plurality of models is the computing resource reduction model 105-3. The computing resource reduction model 105-3 provides a component of an objective function of the LP model 201. The component of the objective function may be based on one or more constraints associated with the computing resource reduction model 105-3, one or more decision variables, e.g., integer decision variables and / or binary decision variables, associated with the computing resource reduction model 105-3, and a penalty term associated with the computing resource reduction model 105-3. In some embodiments, the LP model 201 is constructed based on the computing resource reduction model 105-3 if the number of the computing resources in the proposed assignment is lesser than the number of computing resources in the current assignment. A brief description of the LP model 201 construction based on the computing resource reduction model 105-3 according to some embodiments is provided herein.

[0087] The computing resource reduction model 105-3 may include a soft constraint. The aim is to minimize the number of physical servers used and maximize the server utilization. For each server in the physical system description, let an integer decision variable be serverusedsi.e., a component of the objective function, x^cpG {0,1} indicates whether a sector carrier SC is assigned to POD p and server s i.e., a new assignment / migration. Then,serveruseds> 0 (20) obj+ SeweVuseds* P server (21)For each server used, a penalty term of pserveris added to the component of the objective function.Capacity model

[0088] In some embodiments, one of the plurality of models is a capacity model 105-4 representing processing capacity of a physical server and / or a pod. The capacity model 105-4 may further comprise a server capacity model 105-4a and / or a pod capacity model 105-4b.

[0089] The server capacity model 105-4a describes the capacities of the servers, e.g., the number of pods they can host, their central processing unit (CPU) capacity, etc. The aim is to not overload the servers. The SCs may be assigned to physical servers up to the limits of the capacity defined in the server capacity model 105-4a. Similarly, pods may be started in the physical server up to the limits of the capacity defined in the server capacity model 105-4a.The server capacity model may be modeled either in terms of CPU capacity or number of SCs it can support. For instance, in terms of CPU, the server capacity model may be modeled as:

[0090] The pod capacity model 105-4b may, similarly, describe the pod capacity limit including the maximum number of SCs that may be assigned to a single pod or the maximum CPU capacity of a pod. SCs may be assigned to pods up to the maximum capacity of the pods. The pods may not be over-loaded by assigning SCs to the pods, when the maximum capacity limit is reached. The pod capacity model may be modeled either in terms of CPU capacity or number of SCs it may support. For instance, in terms of CPU, the pod capacity model may be modeled as:Xvscnescsxspn■ required_cpuSCn< max_CPUpYp G pods-, Vs E servers (23)

[0091] The above two constraints in the capacity model 105-4 may be modeled as hard constraints.Current assignment model

[0092] In one or more embodiments, the plurality of models includes a current assignment model 105-5 representing the current assignment of SCs to computing resources. The constraint current assignment y^cpE {0,1} indicates a current assignment of a SC to a pod p and a physical server s i.e., a current assignment / pre-reassignment. Where there are already existing assignments of SCs to physical resources and virtual containers / pods, the LP model 201 is constructed with an initial state reflecting those assignments. Thus, the assignments before the re-optimization may be considered as the current assignment.

[0093] In some embodiment, the LP model 201 may be constructed such that each SC is assigned to only one pod and one physical server. That is,

[0094] In this way, using the methods described above in relation to Figures 1-3, a solution may be achieved that provides an assignment plan and / or reduces the number of servers that are required to support the sector carriers of the telecommunication network 510. In some cases, this can result in a lower cost implementation of the telecommunication network 510, using fewer servers and / or less expensive servers, e.g., offering fewer computing resources. A reduced number of servers also results in a reduced energy consumption of the telecommunication network 510, which may also reduce operating costs. Use of the LP model 201 may require fewer computing resources, e.g., CPU cycles, memory, to produce more optimal solutions when compared to existing approaches such as mixed-integer linear programming models, which may further reduce the energy consumption of the telecommunication network 510. Use of the LP model 201 may allow more optimal solutions to be produced more quickly than existing approaches, making the LP model 201 more suitable for the dynamic capabilities of the telecommunication network 510.

[0095] Referring now to Figure 4, which illustrates an exemplary hierarchy of computing resources including virtualized resources at one or more levels of virtualization, according to one or more embodiments. The features shown in the Figure 4 may be used in conjunction with other embodiments, e.g., representing the computing resources provided by any of the methods of Figures 1-3.

[0096] As discussed above, the assignment of multiple levels of computing resources, e.g., including virtualized resources at one or more levels of virtualization, may be modeled as a hierarchical bin packing problem, where sector carriers of the telecommunication network 510 are assigned physical resources and / or virtual resources provided by the electronic device or any other device of the telecommunication network 510. In the Figure 4, the hierarchy includes a hub 401- 1, a plurality of m physical servers 402-1, 402-2, . . ., 402-m that are each connected with the hub 401-1, and a plurality of virtual containers / pods 403-1, 403-2, ..., 403-m implemented using various ones of the plurality of the physical servers 402-1, 402-2, . . ., 402-m.

[0097] In some embodiments (not shown in figure), the hierarchy further includes another hub401-2 that is arranged at a same level at the hub 401-1, and a second plurality of n servers that are each connected with the hub 401-2, and a second plurality of pods implemented on various ones of the second plurality of servers. The hierarchy further includes a master hub 400-1 (not shown in the Figure) that is arranged at a higher level than the hubs 401-1, 401-2 and that connects to the hubs 401-1, 401-2. In some cases, the hub 400-1 may provide a supervisory functionality to the hubs 401-1, 401-2, e.g., coordinating and / or controlling operation.

[0098] In this way, the virtualized resources provided by the electronic device may be provided by the virtual containers / pods. In some embodiments (not shown in Figure 4), the virtualized resources provided by the electronic device may be provided by virtual machines (VMs) and virtual containers / pods, wherein the VMs form an intermediate virtualization level between a physical server and one or more pods implemented on the physical server. In some embodiments, all of the pods 403-1, ..., 403-m are implemented directly on the respective servers 402-1, ...,402-m, i.e., without an intermediate virtualization level. Such a hierarchy may be represented using different forms of variables in the LP model 201. For example, the LP model 201 may include X [i, j] = 1 to indicate that a sector carrier j is directly hosted on a physical server i.

[0099] Figure 5 shows an example of a communication system 500 in accordance with some embodiments. The electronic device 110 may be part of the telecommunication network 510 as illustrated in Figure 5. The person of ordinary skill in the art will appreciate that the telecommunication network 510 may include numerous additional electronic devices, functions, and components that would be involved in the operation of the telecommunication network 510.The telecommunication network 510 can implement any communication technology such as 3G, 4G, 5G, 6G, e.g., as defined by 3GPP, technologies or similar technologies.

[0100] The telecommunication network 510 comprises a plurality of edge servers (not shown in the Figure 5). Each of the edge servers may be implemented using any type or combination of electronic device(s) that provide computing resources at, or in combination with, access points to the telecommunication network 510 such as a respective RAN base station, also referred to as “base stations”, of the telecommunication network 510. The edge servers, the base stations, and / or other electronic devices, functions, and components of the RAN can enable wireless connections with a number of mobile devices or UEs 101A, 101B, 101C, 101D that use the services of the telecommunication network 510.

[0101] Each of the RAN base stations, e.g., network nodes 510A, 510B, operates to provide a respective one or more sector carriers corresponding to one or more spectrum bands. For example, assuming that telecommunication network 510 is a 5G network, the RAN base stations may be operated to provide sector carriers corresponding to one or more “low” bands, e.g., frequencies less than 1 gigahertz (GHz), one or more “mid” bands, e.g., frequencies between 1-2.6 GHz and / or 3.5-6 GHz, and / or one or more “high” bands, e.g., frequencies between 24-40 GHz. The configuration of the RAN base stations, e.g., specifying the number and / or spectrum bands of the sector carriers, may be determined to provide a desired service availability and / or performance for the mobile device(s). The person of ordinary skill will understand the RAN base stations 205 may provide any other suitable numbers of sector carriers, which may correspond to any different spectrum bands.

[0102] In the example of Figure 5, the communication system 500 includes a telecommunication network 510 that includes an access network 520, such as a radio access network (RAN), and a core network 530, which includes one or more electronic device 110. The electronic device 110 includes the sector carrier assignment manager 111.

[0103] The access network 520 includes one or more access network nodes, such as network nodes 510A and 510B (one or more of which may be generally referred to as network nodes 511), or any other similar 3rdGeneration Partnership Project (3GPP) access nodes or non-3GPP access points. Moreover, as will be appreciated by those of skill in the art, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunication network 510 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network 510 that supports an ORAN specification, e.g., a specification published by the O-RAN Alliance, or any similar organization,and may operate alone or together with other nodes to implement one or more functionalities of any node in the telecommunication network 510, including one or more network nodes 511 and / or electronic device 110.

[0104] Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O-CU- CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller, e.g., near-real time or non- real time, hosting software or software plug-ins, such as a near-real time control application, e.g., xApp, or a non-real time control application, e.g., rApp, or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an Al, Fl, Wl, El, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment described further below in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an O-2 interface defined by the O-RAN Alliance or comparable technologies. The network nodes 511 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 101A, 101B, 101C, and 101D (one or more of which may be generally referred to as UEs 501) to the core network 530 over one or more wireless connections.

[0105] Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 500 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system 500 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0106] The UEs 501 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes 511 and other communication devices. Similarly, the network nodes 511 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 501 and / or with other network nodes or equipment in the telecommunication network 510 toenable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network 510.

[0107] In the depicted example, the core network 530 connects the network nodes 511 to one or more hosts, such as host 540. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 530 includes one more core network nodes, e.g., electronic device 110, that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the electronic device 110. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0108] The host 540 may be under the ownership or control of a service provider other than an operator or provider of the access network 520 and / or the telecommunication network 510, and may be operated by the service provider or on behalf of the service provider. The host 540 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0109] As a whole, the communication system 500 of Figure 5 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard, e.g., 6G; wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.

[0110] In some examples, the telecommunication network 510 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 510 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 510. For example, the telecommunications network 510 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC) / Massive loT services to yet further UEs.

[0111] In some examples, the UEs 501 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 520 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 520. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e., being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0112] In the example, the hub 514 communicates with the access network 520 to facilitate indirect communication between one or more UEs, e.g., UE 101C and / or 101D, and network nodes, e.g., network node 510B. In some examples, the hub 514 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 514 may be a broadband router enabling access to the core network 530 for the UEs. As another example, the hub 514 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 511, or by executable code, script, process, or other instructions in the hub 514. As another example, the hub 514 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 514 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 514 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 514 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 514 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0113] The hub 514 may have a constant / persistent or intermittent connection to the network node 510B. The hub 514 may also allow for a different communication scheme and / or schedule between the hub 514 and UEs, e.g., UE 101C and / or 101D, and between the hub 514 and the corenetwork 530. In other examples, the hub 514 is connected to the core network 530 and / or one or more UEs via a wired connection. Moreover, the hub 514 may be configured to connect to an M2M service provider over the access network 520 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 511 while still connected via the hub 514 via a wired or wireless connection. In some embodiments, the hub 514 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 51 OB. In other embodiments, the hub 514 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 51 OB, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0114] Figure 6 shows an electronic device 110 in accordance with some embodiments. The electronic device 110 may comprise a memory 604 and processing circuitry 602. The memory 604 may comprise software instruction(s) which when executed by the processing circuitry cause the electronic device 110 to perform one or more methods / steps described above in relation to any of Figure 1, Figure 2, Figure 3, Figure 4 and Figure 5. The processing circuitry 602 may include one or more processors. As used herein, electronic device refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of electronic devices include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)), O-RAN nodes or components of an O-RAN node, e.g., O-RU, O-DU, O-CU. The electronic device 110 stores and transmits, internally and / or with other electronic devices over a network, code, which is composed of software instructions and which is sometimes referred to as computer program code or a computer program, and / or data using machine-readable media also called computer- readable media, such as machine-readable storage media (e.g., magnetic disks, optical disks, solid state drives, read only memory (ROM), flash memory devices, phase change memory) and machine -readable transmission media (also called a carrier) (e.g., electrical, optical, radio, acoustical or other form of propagated signals - such as carrier waves, infrared signals). Thus, the electronic device 110, e.g., a computer, includes hardware and software, such as a set of one or more processors each having one or more processor cores, e.g., wherein a processor is a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application specific integrated circuit, field programmable gate array, other electronic circuitry, a combination of one or more of the preceding, coupled to one or more machine-readable storage media to store code for execution on the set of processors and / or to store data. For instance, an electronic device may include non-volatile memory containing the code since the non-volatilememory can persist code / data even when the electronic device is turned off when power is removed, and while the electronic device is turned on that part of the code that is to be executed by the processor(s) of that electronic device is typically copied from the slower non-volatile memory into volatile memory, e.g., dynamic random access memory (DRAM), static random access memory (SRAM), of that electronic device. Typical electronic devices also include a set of one or more physical network interface(s) (NI(s)) to establish network connections, to transmit and / or receive code and / or data using propagating signals, with other electronic devices. For example, the set of physical Nis or the set of physical NI(s) in combination with the set of processors executing code, may perform any formatting, coding, or translating to allow the electronic device to send and receive data whether over a wired and / or a wireless connection. In some embodiments, a physical NI may comprise radio circuitry capable of receiving data from other electronic devices over a wireless connection and / or sending data out to other devices via a wireless connection. This radio circuitry may include transmitter(s), receiver(s), and / or transceiver(s) suitable for radiofrequency communication. The radio circuitry may convert digital data into a radio signal having the appropriate parameters, e.g., frequency, timing, channel, bandwidth, etc. The radio signal may then be transmitted via antennas to the appropriate recipient(s). In some embodiments, the set of physical NI(s) may comprise network interface controller(s) (NICs), also known as a network interface card, network adapter, or local area network (LAN) adapter. The NIC(s) may facilitate in connecting the electronic device to other electronic devices allowing them to communicate via wire through plugging in a cable to a physical port connected to a NIC. One or more parts of an embodiment of the invention may be implemented using different combinations of software, firmware, and / or hardware.

[0115] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. The electronic device may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units, e.g., in an O-RAN access node, and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0116] Other examples of electronic devices include multiple transmission point (multi-TRP) 5G access nodes, multi- standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiverstations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0117] In one or more embodiments, the electronic device 110 includes a processing circuitry 602, a memory 604, a communication interface 606, and a power source 608. The electronic device 110 may be composed of multiple physically separate components, e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc., which may each have their own respective components. In certain scenarios in which the electronic device 110 comprises multiple separate components, e.g., BTS and BSC components, one or more of the separate components may be shared among several electronic devices. For example, a single RNC may control multiple NodeB s. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate electronic device. In some embodiments, the electronic device 110 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated, e.g., separate memory 604 for different RATs, and some components may be reused, e.g., a same antenna 610 may be shared by different RATs. The electronic device 110 may also include multiple sets of the various illustrated components for different wireless technologies integrated into electronic device 110, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within electronic device 110.

[0118] The processing circuitry 602 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other electronic device 110 components, such as the memory 604, to provide electronic device 110 functionality.

[0119] In some embodiments, the processing circuitry 602 includes a system on a chip (SOC). In some embodiments, the processing circuitry 602 includes one or more of radio frequency (RF) transceiver circuitry 612 and baseband processing circuitry 614. In some embodiments, the radio frequency (RF) transceiver circuitry 612 and the baseband processing circuitry 614 may be on separate chips or sets of chips, boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 612 and baseband processing circuitry 614 may be on the same chip or set of chips, boards, or units.

[0120] The memory 604 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 602. The memory 604 may store any suitable instructions, data, or information, including a computer program 605, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 602 and utilized by the electronic device 110. The memory 604 may be used to store any calculations made by the processing circuitry 602 and / or any data received via the communication interface 606. In some embodiments, the processing circuitry 602 and memory 604 is integrated.

[0121] The communication interface 606 is used in wired or wireless communication of signaling and / or data between an electronic device, access network, and / or UE. As illustrated, the communication interface 606 comprises port(s) / terminal(s) 616 to send and receive data, for example to and from a network over a wired connection. The communication interface 606 also includes radio front-end circuitry 618 that may be coupled to, or in certain embodiments a part of, the antenna 610. Radio front-end circuitry 618 comprises filters 620 and amplifiers 622. The radio front-end circuitry 618 may be connected to an antenna 610 and processing circuitry 602. The radio front-end circuitry may be configured to condition signals communicated between antenna 610 and processing circuitry 602. The radio front-end circuitry 618 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 618 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 620 and / or amplifiers 622. The radio signal may then be transmitted via the antenna 610. Similarly, when receiving data, the antenna 610 may collect radio signals which are then converted into digital data by the radio front-end circuitry 618. The digital data may be passed to the processing circuitry 602. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0122] In certain alternative embodiments, the electronic device 110 does not include separate radio front-end circuitry 618, instead, the processing circuitry 602 includes radio front-end circuitry and is connected to the antenna 610. Similarly, in some embodiments, all or some of the RF transceiver circuitry 612 is part of the communication interface 606. In still other embodiments, the communication interface 606 includes one or more ports or terminals 616, theradio front-end circuitry 618, and the RF transceiver circuitry 612, as part of a radio unit (not shown), and the communication interface 606 communicates with the baseband processing circuitry 614, which is part of a digital unit (not shown).

[0123] The antenna 610 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 610 may be coupled to the radio front-end circuitry 618 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 610 is separate from the electronic device 110 and connectable to the electronic device 110 through an interface or port.

[0124] The antenna 610, communication interface 606, and / or the processing circuitry 602 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the electronic device. Any information, data and / or signals may be received from a UE, another electronic device and / or any other network equipment. Similarly, the antenna 610, the communication interface 606, and / or the processing circuitry 602 may be configured to perform any transmitting operations described herein as being performed by the electronic device. Any information, data and / or signals may be transmitted to a UE, another electronic device and / or any other network equipment.

[0125] The power source 608 provides power to the various components of electronic device 110 in a form suitable for the respective components, e.g., at a voltage and current level needed for each respective component. The power source 608 may further comprise, or be coupled to, power management circuitry to supply the components of the electronic device 110 with power for performing the functionality described herein. For example, the electronic device 110 may be connectable to an external power source, e.g., the power grid, an electricity outlet, via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 608. As a further example, the power source 608 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0126] Embodiments of the electronic device 110 may include additional components beyond those shown in Figure 6 for providing certain aspects of the electronic device’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the electronic device 110 may include user interface equipment to allow input of information into the electronic device 110 and to allow output of information from the electronic device 110. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the electronic device 110.

[0127] While the invention has been described in terms of several embodiments, those skilled in the art will recognize that the invention is not limited to the embodiments described, can be practiced with modification and alteration within the spirit and scope of the appended claims. The description is thus to be regarded as illustrative instead of limiting.

Claims

CLAIMSWhat is claimed is:

1. A method (300) performed by an electronic device (110) for assigning computing resources (101-1, 101-2, 102-1, 102-2, 102-3) for managing sector carriers (103-1, 103-2, 103-3) in a telecommunication network (510), the method comprising: constructing (301) a linear programming model (201) based at least on a set of information (104-1, 104-2, 104-3) and a plurality of models (105-1, 105-2a, 105- 2b, 105-3, 105-4, 105-5), wherein the set of information includes at least a first information (104-1) on a current assignment of the sector carriers to computing resources, and wherein the plurality of models include: a first model (105-1) including a first binary decision variable on the current assignment and a second binary decision variable on a proposed assignment, wherein the first model provides a component of an objective function of the linear programming model, solving (302) the linear programming model to obtain a solution (204) for the linear programming model, wherein the solution includes a final assignment for the sector carriers to the computing resources; and generating (305) an assignment plan for the sector carriers corresponding to the solution.

2. The method of claim 1, wherein the set of information includes a second information (104-2) on mobility requirements and / or resiliency requirements for sector carriers.

3. The method of one or more claims 1 to 2, wherein the set of information includes a third information (104-3) describing the characteristics of the computing resources.

4. The method of one or more claims 2 to 3, wherein the plurality of models includes a second model (105-2a, 105-2b, 105-4, 105-5) based on the second information and / or the third information, wherein the second model provides a component of the objective function of the linear programming model.

5. The method of one or more claims 1 to 4, wherein the plurality of models includes a third model (105-3) representing the number of computing resources to be reduced, wherein the linear programming model is constructed based on the third model if the number of the computing resources in the proposed assignment is lesser than the number of computingresources in the current assignment, and wherein the third model provides a component of the objective function of the linear programming model.

6. The method of one or more claims 1 to 5, wherein the first model (105-1) includes a first penalty term that is applied if the proposed assignment is different from the current assignment.

7. The method of claim 6, wherein the proposed assignment is different from the current assignment if the sector carriers are assigned to different computing resources than the computing resources to which the sector carriers are currently assigned.

8. The method of claim 7, wherein the different computing resources includes different virtual computing resources on the same physical computing resource, or different physical computing resources.

9. The method of one or more claims 1 to 5, wherein the first model (105-1) includes a second penalty term that is applied if the proposed assignment is same as the current assignment.

10. The method of one or more claims 1-9, wherein the solving comprises solving (303), iteratively, the linear programing model until the solution is obtained, wherein the solution is an optimized solution of one or more intermediate solutions.

11. The method of claim 10, comprising stopping (304) the solving of the linear programming model if the gain from optimizing reaches or crosses a pre-defined threshold.

12. The method of one or more claims 1-11, wherein generating (306) the assignment plan comprises: deploying, in the communication network, the assignment plan for the sector carriers corresponding to the solution.

13. The method of one or more claims 1 to 12, wherein the sector carriers comprises: one or more first sector carriers corresponding to a first spectrum band, one or more second sector carriers corresponding to a second spectrum band, and one or more third sector carriers corresponding to a third spectrum band.

14. The method of claim 13, wherein the first spectrum band is a higher frequency band than the second spectrum band and the second spectrum band is higher frequency band than the third spectrum band.

15. The method of one or more claims 1 to 14, wherein the computing resource includes a physical server and / or a virtual container.

16. An electronic device (110) for assigning computing resources (101-1, 101-2, 102-1, 102- 2, 102-3) for managing sector carriers (103-1, 103-2, 103-3) in a telecommunication network (510), the electronic device comprising a memory (604, 701) and a processor (602, 703), the memory comprising instructions which when executed by the processor, cause the electronic device to: construct a linear programming model (201) based at least on a set of information (104- 1, 104-2, 104-3) and a plurality of models (105-1, 105-2a, 105-2b, 105-3, 105-4, 105-5), wherein the set of information includes at least a first information on a current assignment of the sector carriers to computing resources, and wherein the plurality of models include: a first model (105-1) including a first binary decision variable on the current assignment and a second binary decision variable on a proposed assignment, wherein the first model provides a component of an objective function of the linear programming model, solve the linear programming model to obtain a solution (204) for the linear programming model, wherein the solution includes a final assignment for the sector carriers to the computing resources; and generate an assignment plan for the sector carriers corresponding to the solution.

17. The electronic device according to claim 16, the memory comprising instructions which when executed by the processor, cause the electronic device to perform a method according to one or more claims 2 to 15.

18. A computer program (605, 702) comprising instructions which, when executed on an electronic device (110), cause the electronic device to carry out a method according to one or more of claims 1 to 15.

19. A computer program product comprising a computer readable storage means on which the computer program (605, 702) according to claim 18 is stored.

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