Patent Publication Number: US-10784963-B2

Title: Multi-layer system capacity planning

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of, and claims priority to, U.S. patent application Ser. No. 16/015,838, filed Jun. 22, 2018, entitled “Multi-Layer System Capacity Planning,” the entire contents of which are hereby incorporated herein by reference. 
    
    
     BACKGROUND 
     A packet layer of the network may include internet protocol (IP) links connected among IP devices such as router ports. The IP links may be routed over a path in the optical layer and use reconfigurable optical add-drop multiplexers (ROADMs) and transponders at the endpoints, and optical signal regenerators (or repeaters) in the middle of the path when the path is longer than the optical reach. A transponder is an intermediary between the IP and the optical layers of the network and is used to perform electrical-to-optical conversion and optical-to-electrical conversion. A connected combination of a IP port and a transponder is defined as a “Tail”. IP ports, optical transponders, and optical regenerators are typically associated with a certain bandwidth unit such as 40 Gbps, 100 Gbps, 200 Gbps, or 400 Gbps. If there are N traffic endpoints and K Quality of Service (QoS) classes then the traffic matrix consists of K*N*(N−1) individual traffic units, all of which may change over time. This disclosure is directed to addressing issues in the existing technology. 
     SUMMARY 
     Disclosed herein are techniques that may address repeated joint global optimization (e.g., whenever network condition changes) while running a multi-layer network. These network condition changes may be based on traffic matrix changes, scheduled outages (e.g., maintenance activity such as software upgrades) or unscheduled outages (e.g., caused by fiber cuts or failure of IP or optical devices). A software-defined network multi-layer controller (SDN-MLC) may communicate with multiple layers of a telecommunication network. The SDN-MLC may have an optimization algorithm that helps manage, in near real-time, the multiple layers of the telecommunication network. Joint multi-layer global optimization may be used to respond to network condition changes caused by traffic matrix changes, scheduled outages, or unscheduled outages. Near real-time optimization makes the best use of available capacity installed in the network. However, over time the installed capacity may not be sufficient to meet the quality of service requirements. Multilayer capacity planning provides an optimal (e.g., minimum) estimate of needs for IP capacity or optical capacity in order to ensure that a network has enough resources for some specified period of time in the future (e.g., days, weeks, or months). 
     In an example, an apparatus (e.g., software-defined network controller) may include a processor and a memory coupled with the processor that effectuates operations. The operations may include: obtaining multiple layer information associated with multiple layers of a telecommunications network, the multiple layer information comprising optical layer information and router layer information; based on the multiple layer information, forecasting operation of the telecommunications network for a plurality of network conditions; based on the forecasted operations of the telecommunication network for the plurality of network conditions, providing a capacity plan option for the telecommunications network. The configuration change of the component of the router layer may include a routing path of traffic in a routing table. The configuration change (e.g., capacity action) may be addition of a router card, optical regenerator, or transponder. 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Furthermore, the claimed subject matter is not limited to limitations that solve any or all disadvantages noted in any part of this disclosure. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Reference will now be made to the accompanying drawings, which are not necessarily drawn to scale. 
         FIG. 1A  illustrates an exemplary system for managing multi-layer self-optimization. 
         FIG. 1B  illustrates  FIG. 1A  in further detail. 
         FIG. 2  illustrates an exemplary method for managing multi-layer system self-optimization. 
         FIG. 3  illustrates an exemplary method for capacity planning or managing multi-layer system self-optimization. 
         FIG. 4  illustrates an exemplary method for capacity planning or managing multi-layer system self-optimization. 
         FIG. 5  illustrates a schematic of an exemplary network device. 
         FIG. 6  illustrates an exemplary communication system that provides wireless telecommunication services over wireless communication networks. 
         FIG. 7  is a representation of an exemplary network. 
     
    
    
     DETAILED DESCRIPTION 
     Conventional approaches to network optimization and planning may assume that the mapping between an IP link and the set of optical transponders and regenerators needed underneath is fixed and if any component fails, the entire IP link fails and the non-failed components of the IP link are rendered unusable. Also, conventional approaches may assume consideration of traffic routing over the IP layer and the optical layer separately. Conventionally, optical layer optimization (e.g., the choice of IP links and their mapping over the optical layer) may rarely be done (e.g., once) and when optical layer optimization is done it is usually with a consideration that the IP layer traffic should only be routed over these once-determined set of IP links. Again, due to lack of joint global optimization of IP and optical layers, conventional approaches use significantly more IP resources and optical resources. 
     Disclosed herein are techniques that may address repeated joint global optimization (whenever network condition changes) while operating a multi-layer network. These network condition changes may be based on traffic changes (e.g., spike in traffic to an internet resource because of an emergency, holiday, or media event (e.g., promotion a associated with a website), scheduled outages (e.g., maintenance activity such as software upgrades), or unscheduled outages (e.g., caused by fiber cuts or failure of IP or optical devices). For capacity planning purposes there may be repeated joint global multi-layer optimization for computing required IP/Optical resources (IP ports, optical transponders, or optical regenerators) to cover traffic matrix and outage scenarios over a specified period of time in the future (e.g., days, weeks, or months), while satisfying engineering rules (e.g., percentage of traffic of various types that should be carried plus certain latency constraints that should be met). 
       FIG. 1A  illustrates an exemplary system for multi-layer self-optimization. Software-defined network multi-layer controller  112  (SDN-MLC  112 ) may communicate with multiple layers of system  100  (e.g., a telecommunication network). SDN-MLC  112  may have an optimization algorithm that helps manage multiple layers of the network. The multiple layers may include optical layer  150 , router layer  130  (which may also be a switch layer), and multi-protocol label switching (MPLS) tunneling layer  120 . As generally shown in  FIG. 1A  (and with more detail in  FIG. 1B ), there may be multiple sites, which include one or more components that help build one or more physical or logical connections. For example, in  FIG. 1B , for site  101  it may include router  131 , tail  161 , ROADM  151 , or regenerator  171  of  FIG. 1B ).  FIG. 1A  illustrates connections between multiple sites as may be seen at each level. Sites include site  101  through site  109 . Some sites (e.g., site  107 ) may have optical equipment (ROADM  157 ), but may not have routing equipment. 
       FIG. 1B  illustrates  FIG. 1A  in further detail. Optical layer  150  may include multiple components, such as reconfigurable optical add-drop multiplexer (ROADMs) (e.g., ROADM  151 -ROADM  159 ), tails (e.g., tail  161 -tail  168 ), and regenerators (e.g., regenerator  171 -regenerator  178 ). A ROADM is a form of optical add-drop multiplexer that adds the ability to remotely switch traffic from a wavelength-division multiplexing (WDM) system at the wavelength layer. A tail is a connection between an internet protocol (IP) port (e.g., port of router  131 ) and a transponder port (e.g., port of transponder  181 ). In optical fiber communications, a transponder is the element that sends and receives the optical signal from a fiber. A transponder is typically characterized by its data rate and the maximum distance the signal can travel. An optical communications regenerator may be used in a fiber-optic communications system to regenerate an optical signal. Such regenerators may be used to extend the reach of optical communications links by overcoming loss due to attenuation of the optical fiber. Some regenerators may also correct for distortion of the optical signal by converting it to an electrical signal, processing that electrical signal, and then retransmitting an optical signal. Router layer  130  may include routers (e.g., router  131 -router  136 ) or switches (not shown). MPLS layer  120  may have several tunnels logically connected via the routers in router layer  130 . 
       FIG. 2  illustrates an exemplary method for multi-layer capacity planning. In an exemplary scenario, as shown in  FIG. 1A  and  FIG. 1B , there may be a system  100  with an optical layer  150 , router layer  130 , and a MPLS layer  120 . At step  191 , information about each layer may be obtained, which may be over days, weeks, months, or more. This information may be before, during, or subsequent to an outage or other event of system  100  and may be used for forecasting. The information may assist in understanding activity patterns for system  100 . For example, activity patterns (which may include scheduled or unscheduled outages) may include the frequency of network link outages (and flow of traffic activity thereafter), dates and times of significant traffic load on system  100 , minimum or maximum average (or median) traffic load on system  100  during a period, traffic matrix changes, trouble tickets (outages may be experienced by users, but not detected through normal techniques), or estimated time of repair on a layer (which may be based on similar errors, alarms, or diagnosed issues), among other things. Information may be gathered on each layer. For MPLS layer  120 , the information may include the MPLS interface state, reserved bandwidth, or label switching paths, among other things. For router layer  130 , there may be information that includes input bytes, output bytes, input packets, output packets, input errors, input drops, input framing errors, output errors, output drops, usual traffic load on affected link, types of traffic on affected link (e.g., defined QoS, video, voice, TCP, UDP, source address, etc.), or routing information, among other things. The router layer information may be obtained from one or more routers. For optical layer  150 , the information may include location of optical equipment (e.g., transponder  181 , regen  171 , ROADM  157 , or tails), errors from the optical equipment, length of optical paths, or outages of the optical equipment, among other things. The optical layer information may be obtained from one more ROADMs. 
     With continued reference to  FIG. 2 , based on multi-layer information of step  192  (e.g., optical layer and router layer information), there is a determination whether a change short-term or intermediate-term change in configuration of existing capacity of the optical layer or router layer may resolve undesirable network conditions alerted to by the information (e.g., information of step  191 ). A resolution may be based on a predetermined period and predetermined engineering (or the like) thresholds. The period would usually range from days to weeks or months. The predetermined engineering thresholds may include service level agreements (SLAs) for transport for much of the information of step  191 , such as errors, traffic loads, or types of traffic, among other things. For example, in this scenario, the load along an optical path site  101 - 107 - 106 - 104  (i.e., path  11 ) may have reached a threshold (e.g., 80 percent) during a period (e.g., 30 minute time frame), which may cause errors or latency. A first selected option may be for ROADMs along the path (e.g., ROADM  151 , ROADM  157 , ROADM  156 , and ROADM  154 ) to add one more wavelengths carrying data channels to increase the capacity along path  11 . This may be preferred over changing a routing path at routing layer  130 , because there may be a tendency for the routing protocol to send traffic through router path site  105 - 104 , which actual goes over optical path site  106 - 104  and doesn&#39;t help resolve the congestion. Or alternatively, it may be selected because SDN-MLC  112  may constantly have to adjust the routing metrics, which may cause high utilization (e.g., eventually slow response) of SDN-MLC  112 , or it may be selected because routing changes would have a service fall outside its SLA. 
     A second selected option (at a different time with different weighted info) may be for just the router layer  120  to be changed. Routes to some or all the traffic may be weighted to go through one or more routers (e.g., router  132 ) of site  102 , because optical path site  102 - 103 - 109 - 104  does not go through optical path site  106 - 104 . An additional consideration that may have led for this second selection option may be that the ROADMs could not (e.g., no more channels available) or should not increase its wavelength based on the information as disclosed in step  191 . A third selected option (at yet a different time with different weighted info) may decide to do a combination of router layer  130  and optical layer  150  solutions (e.g., configuration changes) in order to reduce the traffic to an acceptable threshold (e.g., 30 percent). For instance, traffic for a couple of heavy users along the path may be redirected and stay within SLAs, while an increase in ROADM may be enough to accommodate the traffic load based on information of other users. SDN-MLC  112  may be used to determine the change needed in this step  192 . 
     With reference to step  192 , it may have sub-divided steps for capacity planning. For example, there may be a determination of somewhat immediate (e.g., near real-time) changes to configurations that may be communicated to router layer  130  or optical layer  150  devices. There may also be a consideration of the different parts within system  100  (e.g., see  FIG. 3 ) that may not be automatically or immediately changed, but would provide capacity (for example) that would address issues determined by analyzing the information of step  191  or the like. As shown, SDN-MLC  112  may obtain data from different sources, such as tail database  114 , that allows SDN-MLC  112  to know the current usage of router layer  130  components or optical layer  150  components. If a regenerator, ROADM, tail, or the like are not in use (e.g., because of re-provisioned customer service) or can be added to system  100  to extend the period to be within the predetermined engineering thresholds, then SDN-MLC may automatically order the component or provide an alert (e.g., via a display) regarding the component to be authorized and ordered by a user. 
     With continued reference to step  192 , as disclosed herein, optimal estimate may consider the lowest capital cost but yet the estimation may satisfy both no-fail scenario and a specified set of failure scenarios and under each such scenario it should satisfy a specified set of engineering rule constraints (e.g., percentage of traffic of each type to be carried and latency constraints). Again, due to lack of joint global optimization of IP and optical layers, conventional approaches used significantly more IP resources and optical resources. 
     At step  193 , SDN-MLC  112  may provide instructions based on the determination of step  192 . For example, SDN-MLC  112  may communicate with routers, ROADMs, tails, ordering system  115 , ROADM SDN controller  110 , or the like to execute the determination of step  192 . ROADM SDN controller  110  may be an intermediate device that may directly communicate with optical layer  150  devices. Ordering system  115  may be used to order one or more devices for future use (e.g., spare). There may be an anticipated need for the spare based on the information of step  191 . At this step, SDN-MLC  112  may provide one or more capacity planning options for system  100 . The capacity planning options may be adding/removing/repurposing/stocking spares of one or more components, such as router, router ports, tails, ROADMs, or other router layer, MPLS layer, or optical layer components. 
     SDN-MLC  112  may manage the multiple layers of system  100  in a closed loop and heuristic manner. In a first example, this management may allow for dynamic mapping between a router layer and an optical layer by using colorless or directionless open ROADMs and reusing non-failed router layer or optical layer components of a failed link. In a second example, this management may allow for the use of spare tails (connection between a router port and optical transponder port) and spare optical regenerators. In a third example, this management by SDN-MLC  112  may allow router layer devices to be physical or virtual and software and hardware to be aggregated (e.g., traditional routers) or dis-aggregated (e.g., whitebox switches). 
     Based on the network condition (e.g., traffic matrix or outages) changes, the mapping between IP (e.g., router) and optical layers may be changed to more efficiently carry traffic under the changed network condition. Joint optimization of IP and optical layers whenever the network condition changes may be done by using algorithms based on integer linear programming or heuristics. 
       FIG. 3  illustrates an exemplary method for capacity planning in multi-layer systems as disclosed herein. At step  201 , initially system  100  may include unconnected sets of tails and regenerators. Each such tail and optical regenerator may be classified as spare tails and spare optical regenerators. For a given network condition, only a selected subset of spare tail pairs may be connected to form an IP link that carries the required traffic (e.g., less than 70 percent load) under the required latency constraints (e.g., 5 ms). Depending on the length (e.g., in miles) of a specific IP link, it may require spare optical regenerators as well. At step  202 , a joint multi-layer global optimization may be done to choose the right set of spare tail pairs (plus optical regenerators, if needed) along with the proper IP layer routing. The joint optimization should satisfy engineering rule constraints (e.g., percentage of traffic of each type to be carried and latency constraints), use realizable routing (e.g., shortest path routing, constrained shortest path routing, multi-commodity flow routing with the restriction of equal splitting of traffic units, etc.), or optimize some other desirable quantities (e.g., maximizing unused spare tails and regenerators, minimizing the maximum traffic on a link, etc.). At step  203 , SDN-MLC  112  may provide instructions for IP devices (e.g., physical or virtual routers) or optical layer devices to be turned up or down depending on network conditions of system  100 . 
     With continued reference to  FIG. 3 , at step  204 , there may be a detected change in network condition that may result in a change in the traffic matrix and a certain set of tails and regenerators to fail, or alternatively some previously failed tails and regenerators may become operational. At step  205 , taking account of the traffic and failure conditions, a new joint multi-layer global optimization may be performed. The new joint multi-layer global optimization may result in some previously established IP links to be taken down and some new IP links to be added and thus also reflecting the dynamic nature and control of the network topology. The joint global optimization problem may be formulated as an exact integer linear programming problem, but if the exact algorithm is time consuming then a fast heuristic may be used. At step  206 , SDN-MLC  112  may provide instructions for IP devices (e.g., physical or virtual) or optical layer devices to be turned up or down depending on network conditions of system  100 . Software or hardware may remain aggregated or may be disaggregated. 
       FIG. 4  illustrates another exemplary method for capacity planning for multi-layer systems as disclosed herein. The main capacity units used to optimize over are the tails and regenerators where a tail includes an IP port and a transponder. Let C 1  and C 2  represent the costs of a tail and a regenerator respectively, and N 1  and N 2  represent the numbers of tails and regenerators needed in the final solution. The quantity (C 1 *N 1 +C 2 *N 2 ) is optimized over the possible network change scenarios. At step  211 , the number of tails are set to zero. Initially the network may only include zero tails and zero regenerators. At step  212 , for one given network change condition, minimum set of tails and regenerators may be selected that can be connected appropriately to form IP links that can carry the required traffic under the required engineering rule constraints. At step  213 , the analysis of significant number of network change conditions may be repeated. This selection may be based on analysis of many different traffic scenarios and different failure scenarios. Preferably, all the different combination are analyzed. For example, repeated may be similar to the following: 1). Peak traffic on day 1; 2) Peak traffic on day 2; 3) Day 1, but some of the links/routers have failed—with same traffic load for day 1, etc. For any given network change condition, tails and regenerators used for previous network change conditions may be considered for reuse. The joint global optimization problem over the several network change scenarios can be formulated as an exact integer linear programming problem, but if the exact algorithm is time consuming then a fast heuristic may be used. At step  214 , SDN-MLC  112  implements or provides options (e.g., displays) for implementing one or more capacity plans. The capacity planning actions may be adding/removing/repurposing/stocking spares of one or more components, such as router, router ports, tails, ROADMs, or other router layer, MPLS layer, or optical layer components. 
     The disclosed capacity planning approach may anticipate all failure and traffic surge scenarios over the next few weeks or months and for each scenario may consider a joint router layer or optical layer global optimization to minimize the total cost of tails, regenerators, or other devices. The disclosed methods, systems, and apparatuses may provide an optimal network that results in network capex savings in the range of 15-30% or more. Table 1 helps illustrate an example with the components of cost that may include router IP port, optical transponder, or optical regenerator. For this example, note that Tail=Packet Port+Transponder; traffic Numbers are provided for exemplary purpose only; Cost assumptions are: 1) optical regenerator cost ˜1.5*Transponder cost; and 2) router port cost ˜2.5*Transponder cost. The normalized cost is in units of transponder cost for this example. As shown, there is potential for ˜34% CAPEX cost saving with joint global optimization. 
     
       
         
           
               
               
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                   
                 # of 100 
                 # of 100 
                 Normalized 
               
               
                 Scenario 
                 GE Tails 
                 GE Regens 
                 Cost 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                 PMO 
                 1153 
                 226 
                 2105 
               
               
                 +Fast Provisioning 
                 1048 
                 105 
                 1913 (−9%)   
               
               
                 +Joint Optimization 
                 939 
                 91 
                 1711 (−18.5%) 
               
               
                 on Existing Links 
               
               
                 +Joint Global 
                 740 
                 130 
                 1392 (−34%)   
               
               
                 Optimization (add 
               
               
                 links anywhere) 
               
               
                   
               
            
           
         
       
     
     Although a router layer, optical layer, and MPLS tunneling layer are discussed, it is contemplated that the MPLS tunneling layer be some other tunneling layer or not present at all. Also, it is contemplated that the optical layer may be another physical layer other than optical. As discussed herein, the router layer, may be a switching layer or the like. It is contemplated herein that the term information as considered herein may be information on any layer (e.g., layer  130  or layer  150 ). Activity patterns as disclosed herein may be considered “information” which is used in step  192 . Whether an outage is scheduled or unscheduled is other information that may be used for determining capacity planning. With reference to estimated time for repair, it is contemplated that sometimes it may take less time to implement a router layer solution rather than an optical layer solution (or vice versa). Although time may be a significant factor, SDN-MLC  112  may consider a predetermined weight of the information in order to derive a weighted determination (e.g., step  192  or step  202 ). The disclosed techniques may be used to help change the amount of capacity units (e.g., devices and connection configurations) in different layers of the network so that near real-time optimization has sufficient installed resources to meet the quality of service requirements of the network. 
     Disclosed herein is capacity planning as an option on the SDN controller. There may also be standalone capacity planning system that uses the same data and potentially the same core optimization algorithm. Again, disclosed is capacity planning which may be over a set future time period (that can be days, weeks or months) which basically identifies how much additional resources would be needed over that set future time period in addition to what is already in the defined telecommunications network. Using observation of past traffic patterns plus forecasting of traffic growth, identified herein may be a set of traffic matrix conditions and failure conditions that can be realized over the set future time period. Each traffic matrix condition and failure condition may be simulated one at a time and using joint multi-layer optimization for that condition that identifies how much additional resources (e.g., Tails+Regens) on top of current resources may be needed to satisfy each traffic matrix condition and failure condition. There may be a final determination of the minimal amount of additional resources needed to satisfy every traffic matrix condition and failure condition. 
       FIG. 5  is a block diagram of network device  300  that may be connected to or comprise a component of system  100  of  FIG. 1A . Network device  300  may comprise hardware or a combination of hardware and software. The functionality to facilitate telecommunications via a telecommunications network may reside in one or combination of network devices  300 . Network device  300  depicted in  FIG. 5  may represent or perform functionality of an appropriate network device  300 , or combination of network devices  300 , such as, for example, a component or various components of a cellular broadcast system wireless network, a processor, a server, a gateway, a node, a mobile switching center (MSC), a short message service center (SMSC), an automatic location function server (ALFS), a gateway mobile location center (GMLC), a radio access network (RAN), a serving mobile location center (SMLC), or the like, or any appropriate combination thereof. It is emphasized that the block diagram depicted in  FIG. 5  is exemplary and not intended to imply a limitation to a specific implementation or configuration. Thus, network device  300  may be implemented in a single device or multiple devices (e.g., single server or multiple servers, single gateway or multiple gateways, single controller or multiple controllers). Multiple network entities may be distributed or centrally located. Multiple network entities may communicate wirelessly, via hard wire, or any appropriate combination thereof. 
     Network device  300  may comprise a processor  302  and a memory  304  coupled to processor  302 . Memory  304  may contain executable instructions that, when executed by processor  302 , cause processor  302  to effectuate operations associated with mapping wireless signal strength. As evident from the description herein, network device  300  is not to be construed as software per se. 
     In addition to processor  302  and memory  304 , network device  300  may include an input/output system  306 . Processor  302 , memory  304 , and input/output system  306  may be coupled together (coupling not shown in  FIG. 5 ) to allow communications between them. Each portion of network device  300  may comprise circuitry for performing functions associated with each respective portion. Thus, each portion may comprise hardware, or a combination of hardware and software. Accordingly, each portion of network device  300  is not to be construed as software per se. Input/output system  306  may be capable of receiving or providing information from or to a communications device or other network entities configured for telecommunications. For example input/output system  306  may include a wireless communications (e.g., 3G/4G/GPS) card or wired communications (e.g. optical lines) card. Input/output system  306  may be capable of receiving or sending video information, audio information, control information, image information, data, or any combination thereof. Input/output system  306  may be capable of transferring information with network device  300 . In various configurations, input/output system  306  may receive or provide information via any appropriate means, such as, for example, optical means (e.g., infrared), electromagnetic means (e.g., RF, Wi-Fi, Bluetooth®, ZigBee®), acoustic means (e.g., speaker, microphone, ultrasonic receiver, ultrasonic transmitter), or a combination thereof. 
     Input/output system  306  of network device  300  also may contain a communication connection  308  that allows network device  300  to communicate with other devices, network entities, or the like. Communication connection  308  may comprise communication media. Communication media typically embody computer-readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, or wireless media such as acoustic, RF, infrared, or other wireless media. The term computer-readable media as used herein includes both storage media and communication media. Input/output system  306  also may include an input device  310  such as keyboard, mouse, pen, voice input device, or touch input device. Input/output system  306  may also include an output device  312 , such as a display, speakers, or a printer. 
     Processor  302  may be capable of performing functions associated with telecommunications, such as functions for processing broadcast messages, as described herein. For example, processor  302  may be capable of, in conjunction with any other portion of network device  300 , determining a type of broadcast message and acting according to the broadcast message type or content, as described herein. 
     Memory  304  of network device  300  may comprise a storage medium having a concrete, tangible, physical structure. As is known, a signal does not have a concrete, tangible, physical structure. Memory  304 , as well as any computer-readable storage medium described herein, is not to be construed as a signal. Memory  304 , as well as any computer-readable storage medium described herein, is not to be construed as a transient signal. Memory  304 , as well as any computer-readable storage medium described herein, is not to be construed as a propagating signal. Memory  304 , as well as any computer-readable storage medium described herein, is to be construed as an article of manufacture. 
     Memory  304  may store any information utilized in conjunction with telecommunications. Depending upon the exact configuration or type of processor, memory  304  may include a volatile storage  314  (such as some types of RAM), a nonvolatile storage  316  (such as ROM, flash memory), or a combination thereof. Memory  304  may include additional storage (e.g., a removable storage  318  or a non-removable storage  320 ) including, for example, tape, flash memory, smart cards, CD-ROM, DVD, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, USB-compatible memory, or any other medium that can be used to store information and that can be accessed by network device  300 . Memory  304  may comprise executable instructions that, when executed by processor  302 , cause processor  302  to effectuate operations to map signal strengths in an area of interest. 
       FIG. 6  depicts an exemplary diagrammatic representation of a machine in the form of a computer system  500  within which a set of instructions, when executed, may cause the machine to perform any one or more of the methods described above. One or more instances of the machine can operate, for example, as processor  302 , router  131 , and other devices of  FIG. 1A ,  FIG. 1B , and  FIG. 7 . In some embodiments, the machine may be connected (e.g., using a network  502 ) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client user machine in a server-client user network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. 
     The machine may comprise a server computer, a client user computer, a personal computer (PC), a tablet, a smart phone, a laptop computer, a desktop computer, a control system, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. It will be understood that a communication device of the subject disclosure includes broadly any electronic device that provides voice, video or data communication. Further, while a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methods discussed herein. 
     Computer system  500  may include a processor (or controller)  504  (e.g., a central processing unit (CPU)), a graphics processing unit (GPU, or both), a main memory  506  and a static memory  508 , which communicate with each other via a bus  510 . The computer system  500  may further include a display unit  512  (e.g., a liquid crystal display (LCD), a flat panel, or a solid state display). Computer system  500  may include an input device  514  (e.g., a keyboard), a cursor control device  516  (e.g., a mouse), a disk drive unit  518 , a signal generation device  520  (e.g., a speaker or remote control) and a network interface device  522 . In distributed environments, the embodiments described in the subject disclosure can be adapted to utilize multiple display units  512  controlled by two or more computer systems  500 . In this configuration, presentations described by the subject disclosure may in part be shown in a first of display units  512 , while the remaining portion is presented in a second of display units  512 . 
     The disk drive unit  518  may include a tangible computer-readable storage medium  524  on which is stored one or more sets of instructions (e.g., software  526 ) embodying any one or more of the methods or functions described herein, including those methods illustrated above. Instructions  526  may also reside, completely or at least partially, within main memory  506 , static memory  508 , or within processor  504  during execution thereof by the computer system  500 . Main memory  506  and processor  504  also may constitute tangible computer-readable storage media. 
       FIG. 7  is a representation of an exemplary network  600  (e.g., cloud). Network  600  (e.g., system  100 ) may comprise an SDN—that is, network  600  may include one or more virtualized functions implemented on general purpose hardware, such as in lieu of having dedicated hardware for every network function. That is, general purpose hardware of network  600  may be configured to run virtual network elements to support communication services, such as mobility services, including consumer services and enterprise services. These services may be provided or measured in sessions. 
     A virtual network functions (VNFs)  602  may be able to support a limited number of sessions. Each VNF  602  may have a VNF type that indicates its functionality or role. For example,  FIG. 7  illustrates a gateway VNF  602   a  and a policy and charging rules function (PCRF) VNF  602   b . Additionally or alternatively, VNFs  602  may include other types of VNFs. Each VNF  602  may use one or more virtual machines (VMs)  604  to operate. Each VM  604  may have a VM type that indicates its functionality or role. For example,  FIG. 7  illustrates a management control module (MCM) VM  604   a , an advanced services module (ASM) VM  604   b , and a DEP VM  604   c . Additionally or alternatively, VMs  604  may include other types of VMs. Each VM  604  may consume various network resources from a hardware platform  606 , such as a resource  608 , a virtual central processing unit (vCPU)  608   a , memory  608   b , or a network interface card (NIC)  608   c . Additionally or alternatively, hardware platform  606  may include other types of resources  608 . 
     While  FIG. 7  illustrates resources  608  as collectively contained in hardware platform  606 , the configuration of hardware platform  606  may isolate, for example, certain memory  608   c  from other memory  608   c.    
     As described herein, a telecommunications system wherein management and control utilizing a software defined network (SDN) and a simple IP are based, at least in part, on user equipment, may provide a wireless management and control framework that enables common wireless management and control, such as mobility management, radio resource management, QoS, load balancing, etc., across many technologies; decoupling the mobility control from data planes to let them evolve and scale independently; reducing network state maintained in the network based on user equipment types to reduce network cost and allow massive scale; shortening cycle time and improving network upgradability; flexibility in creating end-to-end services based on types of user equipment and applications, thus improve customer experience; or improving user equipment power efficiency and battery life-especially for simple M2M devices—through enhanced wireless management. 
     Crossing or meeting a threshold as discussed herein, which may trigger the determining step  192 , may be described as surpassing a number that is prescribed in order to determine when some action is triggered. For example, a threshold may be crossed if the number of keepalives from a device is below a certain amount (e.g., 3) within a timeframe (e.g., 10 minutes) and therefore an alert may be triggered. In another example, a threshold may be crossed if the number of errors is above a certain amount (e.g., 100) within a certain time frame (e.g., 1 minute) and therefore an alert may be triggered. 
     While examples of a telecommunications system in which multi-layer self-optimization may be processed and managed have been described in connection with various computing devices/processors, the underlying concepts may be applied to any computing device, processor, or system capable of facilitating a telecommunications system. The various techniques described herein may be implemented in connection with hardware or software or, where appropriate, with a combination of both. Thus, the methods and devices may take the form of program code (i.e., instructions) embodied in concrete, tangible, storage media having a concrete, tangible, physical structure. Examples of tangible storage media include floppy diskettes, CD-ROMs, DVDs, hard drives, or any other tangible machine-readable storage medium (computer-readable storage medium). Thus, a computer-readable storage medium is not a signal. A computer-readable storage medium is not a transient signal. Further, a computer-readable storage medium is not a propagating signal. A computer-readable storage medium as described herein is an article of manufacture. When the program code is loaded into and executed by a machine, such as a computer, the machine becomes an device for telecommunications. In the case of program code execution on programmable computers, the computing device will generally include a processor, a storage medium readable by the processor (including volatile or nonvolatile memory or storage elements), at least one input device, and at least one output device. The program(s) can be implemented in assembly or machine language, if desired. The language can be a compiled or interpreted language, and may be combined with hardware implementations. 
     The methods and devices associated with a telecommunications system as described herein also may be practiced via communications embodied in the form of program code that is transmitted over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via any other form of transmission, wherein, when the program code is received and loaded into and executed by a machine, such as an EPROM, a gate array, a programmable logic device (PLD), a client computer, or the like, the machine becomes an device for implementing telecommunications as described herein. When implemented on a general-purpose processor, the program code combines with the processor to provide a unique device that operates to invoke the functionality of a telecommunications system. 
     While a telecommunications system has been described in connection with the various examples of the various figures, it is to be understood that other similar implementations may be used or modifications and additions may be made to the described examples of a telecommunications system without deviating therefrom. For example, one skilled in the art will recognize that a telecommunications system as described in the instant application may apply to any environment, whether wired or wireless, and may be applied to any number of such devices connected via a communications network and interacting across the network. Therefore, a telecommunications system as described herein should not be limited to any single example, but rather should be construed in breadth and scope in accordance with the appended claims. 
     In describing preferred methods, systems, or apparatuses (e.g., devices) of the subject matter of the present disclosure—multi-layer capacity planning—as illustrated in the Figures, specific terminology is employed for the sake of clarity. The claimed subject matter, however, is not intended to be limited to the specific terminology so selected, and it is to be understood that each specific element includes all technical equivalents that operate in a similar manner to accomplish a similar purpose. In addition, the use of the word “or” is generally used inclusively unless otherwise provided herein. Real-time as discussed herein refers to operations that usually occur in seconds, but not more than a minute. As disclosed herein, near real-time events usually occur within minutes. A traffic matrix may represent the load from each ingress point to each egress point in an IP network. Although networks are engineered to tolerate some variation in the traffic matrix, large changes may lead to congested links and poor performance. Configuration change of a component as disclosed herein may include a software change or a hardware change (e.g., replace or remove). 
     This written description uses examples to enable any person skilled in the art to practice the claimed invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art (e.g., skipping steps, combining steps, or adding steps between exemplary methods disclosed herein). Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal languages of the claims.