Patent Publication Number: US-11665062-B2

Title: Geographic service classification and routing

Description:
PRIORITY APPLICATION 
     This patent application is a continuation of U.S. application Ser. No. 16/328,140, filed Feb. 25, 2019, which is a U.S. National Stage Application under 35 U.S.C. 371 from International Application No. PCT/US2016/054803, filed Sep. 30, 2016, published as WO2018/063332 on Apr. 5, 2018, the entirety of which are hereby incorporated by reference herein. 
    
    
     TECHNICAL FIELD 
     The subject matter disclosed herein generally relates to methods, systems, and programs for network management, and more particularly, to methods, systems, and computer programs for managing resources to deliver a network service in a distributed configuration. 
     BACKGROUND 
     The proliferation of wireless-enabled devices has enabled an always-connected paradigm. For example, users are now commonly equipped with at least one mobile communication device (e.g., a smart phone, a table computer, etc.) capable of providing wireless data access in virtually any location. Mobile communications support a variety of data-driven applications, such as messaging, financial accounts access, online shopping, navigation, gaming and entertainment, encyclopedic services, news services, rating services, etc. 
     In addition, another class of wireless devices is emerging to couple systems that may not have traditionally comprised communication features to a network for purposes of monitoring, reporting, alarming, controlling, etc. For example, heating/ventilating/air conditioning (HVAC) systems, security systems, appliances, utility meters, parking meters, automobiles, etc., may be coupled to the Internet to provide access to users, operators, service organizations, etc. These “Internet-of-Things” (IoT) devices will provide unprecedented access and control to users. 
     Supporting the resulting increased wireless traffic may be problematic for the underlying network, such as by having to support increased signaling. Data-signaling traffic storms are currently happening, and it is expected to become worse as 5G and IoT data on the network grow exponentially. This data storm is triggering stresses and service outages on core elements such as policy servers, gateway servers, charging and revenue generating services, and subscriber databases. Network core nodes are struggling to maintain service levels and response times during these signaling storms. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various ones of the appended drawings merely illustrate example embodiments of the present disclosure and cannot be considered as limiting its scope. 
         FIG.  1    illustrates an example embodiment of a system to monitor and control data flow in a network. 
         FIG.  2    illustrates an example configuration of a protocol stack usable in accordance with at least one embodiment of the present disclosure. 
         FIG.  3    is a block flow diagram illustrating an example embodiment of messaging pathways used to classify and route data service requests. 
         FIG.  4    is a timing diagram illustrating an embodiment of a process for scaling out a processing resource. 
         FIG.  5 A  is a table for storing service data, according to some example embodiments. 
         FIG.  5 B  is a table for storing geographic service routing agent (GSRA) data, according to some example embodiments. 
         FIG.  6 A  is a table for storing service pool data for a service, according to some example embodiments. 
         FIG.  6 B  is a table for storing service capacity data per location, according to some example embodiments. 
         FIG.  7    is an architecture for an orchestrator, according to some example embodiments. 
         FIG.  8    is a flowchart of a method for managing service resources at an orchestrator, according to some example embodiments. 
         FIG.  9    is a flowchart of a method for managing resources to deliver network services, according to some example embodiments. 
         FIG.  10    is a flowchart of a method for operations executed by the Geographic service routing agents (GSRA), according to some example embodiments. 
         FIG.  11    is a flowchart of a method for managing resources to deliver a network service in a distributed configuration, according to some example embodiments. 
         FIG.  12    is a block diagram illustrating an example of a machine upon which one or more example embodiments may be implemented. 
     
    
    
     DETAILED DESCRIPTION 
     Example methods, systems, and computer programs are directed to managing resources to deliver a network service in a distributed configuration. Examples merely typify possible variations. Unless explicitly stated otherwise, components and functions are optional and may be combined or subdivided, and operations may vary in sequence or be combined or subdivided. In the following description, for purposes of explanation, numerous specific details are set forth to provide a thorough understanding of example embodiments. It will be evident to one skilled in the art, however, that the present subject matter may be practiced without these specific details. 
     Embodiments provide ways to manage service traffic better by allocating resources closer to the edge of the network, where client devices are sending service requests. Geographic service routing agents (GSRA) are distributed throughout the network to configure service handler pools at specific locations, based on captured service traffic information data. The GSRA cooperate with an orchestrator to understand service traffic levels and determine how to improve service delivery. The service traffic is measured by analyzing service traffic at the higher levels of the ISO stack (levels 4-7), as well as understanding traffic at the lower levels (levels 1-3). 
     In some example embodiments, the traffic is routed to particular geographic service handlers, based on the originating location of the traffic, in order to lower the load on the network and the response times to clients. For example, in a big city, it is possible to dimension traffic to handlers in different parts of the city based on the geographic location of the originating traffic. 
     In another example, services are classified and the resources assigned to each service according to the service classification. For example, some IoT services may be classified as low priority and given less resources than other services, and the lower-priority services may be assigned service resources if other higher-priority services (e.g., for-pay services) do not consume all available resources. This way, if there is a surge in traffic, the network is able to manage the traffic according to service priority and desired quality-of-service (QOS) goals. 
     Legacy core nodes implement service handling in a generic model, where all traffic gets routed to one location with an independent monolithic service handling node. In some example embodiments presented below, service handling is broken down based on geographic location and service, and service traffic is routed as needed to the desired locations. The geographic location can be based on cell site, geographic coordinates, state, nation, etc. Further, the geographic location may be based on LTE&#39;s tracking area (for a group of cells) or the cell identifier for a specific cell, or in GSM&#39;s tracking area, or in a similar concept applied to 5G. 
     In an example embodiment, a tracking area is defined for identifying, at a macro level, a location in the network. For example, the tracking area may be defined as a 2 km 2  region in a city, which typically may cover an area comprised of several cells. In addition, the geographic location may be based on the “cell id” representing the area of coverage for the network or for a single cell. Some of the example services may include Internet traffic, Internet voice calls, email, Facebook™, weather, video, etc., and the services requests are detected and routed based on geographic location to location-specific service handling functions to guarantee service handling and latency response times This avoids traffic interfering with other traffic from other geographic areas. New services may be added dynamically without interruption, and the service pools and locations can be adjusted dynamically without service interruption. 
     In an embodiment, a method is provided. The method includes an operation for identifying resources for delivering a network service, the resources being classified by geographic area from a plurality of geographic areas. Further, the method includes operations for selecting service agents required to configure the identified resources, each service agent to manage service pools for delivering the network service across at least one geographic area, the service agents being selected to provide configurability for the service pools to deliver the network service for the identified resources. The method further includes an operation for sending respective configuration rules to each selected service agent. The configuration rules are configured to establish service pools for delivering the network service across the plurality of geographic areas. Further, the method includes operations for collecting service traffic information from the service agents, adjusting the resources for delivering the network service based on the collected service traffic information, and for sending updated respective configuration rules to the service agents based on the adjustment. 
       FIG.  1    illustrates an example embodiment of a system  100  to monitor and control data flow in a network. The present disclosure may reference components, systems, methodologies, etc., associated with certain technologies such as, for example, the 3GPP Long Term Evolution (LTE) or LTE-Advanced (LTE-A)-based wireless network standards, such as LTE Advanced Pro (LTE-A Pro), and related specifications that may be associated with the Diameter Protocol. These specifications may include, for example, 3GPP TS 36.300, V11.2.0, “Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Overall description; Stage 2 (Release 11)”. Specific technologies are referenced to provide a readily comprehensible perspective from which to understand the embodiments disclosed herein, but are not intended to limit possible implementations to employing only these technologies. 
     In general, the system  100  may be network-configured to support a wireless front end of a cellular network as well as other wired or wireless front ends for accessing the network. In an embodiment, the core network may be an LTE evolved packet core (EPC) network. While an EPC network implementation is utilized in this disclosure to describe the various embodiments, other types of networks may be employed. For example, in addition to existing fourth generation (4G) wireless networks such as LTE networks, the systems, methodologies, etc. disclosed herein may likewise be implemented in a system including entities performing a wireless core network function role in emerging fifth generation (5G) networks. The system  100  may comprise, for example, a network of devices that may be configured to perform different functions such as those shown in  FIG.  1   . For example, computing devices such as servers (e.g., in a blade or rack configuration) may be coupled by a wired network (e.g., Ethernet) to work individually or collaboratively to perform the disclosed functionality. It is possible for one server to perform more than one function, for multiple servers to work together to perform a single function, etc. Moreover, interfaces may include hardware and/or software to allow one function to provide data to another. Consistent with the present disclosure, the Rx, Sh, Gx, Gy and Gz interfaces may be based on the Diameter protocol. Diameter may provide interfaces for authentication, authorization, traffic monitoring, device configuration, and accounting in system  100 . 
     Base station (BS)  104  corresponds to a “cell” in a wireless cellular communication system (e.g., LTE). Wireless cellular systems may typically comprise a plurality of cells arranged in close proximity that cover large geographic areas to provide uninterrupted wireless service to service subscribers. However, one user equipment (UE)  102  and one BS  104  are illustrated in  FIG.  1    for the sake of clarity. In an example of operation, the UE  102  may communicate wirelessly with the BS  104  when operating within the cell (e.g., when within the transmission range of BS  104 ). The UE  102  may comprise, for example, at least one wireless-enabled apparatus such as a mobile communication device, which could be a cellular handset or some other type of smartphone, a netbook, a notebook, a laptop, a palmtop, a wearable device such as a wristwatch, a head-mounted display, a device or system that does not operate primarily as a data processor but is equipped with at least basic data processing and communication resources such as an Internet-of-Things (IoT) device, a typically stationary computing device such as a desktop computer, server, a smart television or other “smart” device, a set-top box, etc. 
     The BS  104  interacts with a mobility management engine (MME)  106  or a serving gateway (SGW)  108  to support the wireless activity of the UE  102 . The MME  106  may generally control the BS  104 , and in that capacity, may manage bearer establishment, idle mode and handover operations for the UE  102 . This may include choosing at least one SGW  108  when the UE  102  initiates operation in the cell of BS  104 . The SGW  108  may route data packets from the UE  102  to other locations and may store the context (e.g., parameters, device state, etc.) of the UE  102  during handover to another cell, or may replicate user traffic in instances of lawful interception. An application function (AF)  110  may offer or control applications executing within system  100  that may depend on policy and charging control. An example of such an application is the Proxy-Call Session Control Function (P-CSCF). The AF  110  may provide quality of service (QoS) control and charging functionality for these applications. The AF  110  may be coupled to Policy and Charging Rules Function (PCRF)  112  via the Rx interface (e.g., a Diameter QoS/Policy Interface such as defined in the ETSI TS 129 214 V7.4.0 specification). The PCRF  112  may comprise at least a database  114  including provisions for policy control decision and flow-based charging control functionalities in system  100 . The PCRF  112  may control service data flow detection, gating, QoS, flow-based charging, etc. 
     As illustrated in the system  100 , the PCRF  112  may be coupled to a Packet Data Network (PDN) Gateway (collectively, “PGW”)  116  via the Gx Interface (e.g., a Diameter QoS/Policy Interface such as defined in the ETSI TS 129 212 V7.4.0 specification). The PGW  116  may provide a gateway allowing the UE  102  to access external packet data networks (e.g., Internet). The PCRF  112  may also be coupled to a Home Subscriber Function (HSS)-Subscriber Profile Repository (SPR)  118  via the Sh Interface (e.g., a Subscriber Profile interface such as defined in the 3GPP TS 29.329 V9.2.0 specification). The HSS-SPR  118  may include at least a database  120  to store user and subscription-related data. For example, the HSS may authenticate subscribers (e.g., utilizing an International Mobile Subscriber Identity (IMSI) alone or combined with other public or private user data as a key), while the SPR may maintain subscriber policies and profiles for the QoS management. 
     The charging system  122  in the system  100  may comprise an Offline Charging System (OFCS)  124  including at least the database  126  and an Online Charging System (OCS)  128  including the database  130 . The PGW  116  may be coupled to the OFCS  124  via the Gz interface (e.g., an offline charging interface such as defined in the 3GPP TS 29.209 V680 specification) and to OCS  128  via the Gy interface (e.g., an online charging interface such as defined in the 3GPP TS 32.299 VD40 specification). The OFCS  124  may collect resource usage information in database  126  concurrently with the usage of a resource (e.g., streaming media to the UE  102 ), and may then generate files for billing the user for the access at a later time. The OFCS  124  may not affect real-time access to the resource. The OCS  128  may allow a service provider to charge the user in real time based on resource usage. The OCS  128  may, for example, provide credit management and grant credit based on time, traffic volume or chargeable events. In addition, an Internet Protocol (IP) Multimedia System (e.g., collectively, “IMS”)  132  may also be coupled to the OCS  128  through the Gy interface. The IMS  132  may utilize an IP-protocol-based communication to send multimedia data (e.g., text, audio, video, etc.) to the UE  102 . 
     When the UE  102  attempts to download data (e.g., in response to running an application, streaming media, etc.), the charging system  122  may receive a request to determine whether the user of the UE  102  is a subscriber, the limitations of the subscription, whether there is available credit or the user should be billed for the access, etc. Charging system  122  may be stressed when a large number of new access requests are generated due to, for example, many users attempting to download data to UEs  102  at the same time (e.g., during an emergency, a special event such as a holiday, election, sporting event, concert, etc.), poorly designed services, policy-triggered re-rating on all open sessions, etc. This may cause a “storm” of reconnection requests to charging system  122 . 
     Consistent with the present disclosure, some or all of system  100  may be composed of a cloud computing architecture coupled to a fog mesh network. The cloud computing architecture may include at least one computing device (e.g., server) coupled to a network (e.g., the Internet). For example, the at least one cloud server may be capable of providing data processing and/or data storage services to a variety of remotely-situated data consumers. The cloud computing system may be in communication with a mesh network of devices (e.g., a “fog”) operating at the edge of the cloud. In an embodiment, the fog devices may include at least IoT devices. 
     The fog may be considered to be a massively interconnected network wherein the devices are in communication with each other (e.g., via radio links). This interaction may be performed using, for example, the open interconnect consortium (OIC) standard specification 1.0 released by the Open Connectivity Foundation™ (OCF) on Dec. 23, 2015. The OIC standard may allow for device-to-device discovery and the establishment of communications for interconnects. Other interconnection protocols that may be usable include, for example, the optimized link state routing (OLSR) Protocol, the better approach to mobile ad-hoc networking (B.A.T.M.A.N.), etc. 
     Communications within the fog may be passed along the most convenient path between any device to reach gateways providing access to the cloud. In these networks, the number of interconnections provide substantial redundancy, allowing communications to be maintained, even with the loss some of devices. Further, the mesh network architecture may allow limited devices (e.g., very low power, located at a distance from infrastructure) to be used, as the range to connect to another device in the fog may be less than the range to connect to the gateways. 
       FIG.  2    illustrates an example configuration of a protocol stack usable in accordance with embodiments of the present disclosure. In some example embodiments, Diameter is a protocol used to exchange information in the Core resources in LTE/4G/5G—between gateways, policy servers, charging systems, databases and application servers—but any other protocol for exchanging network and service information may be utilized. 
     Diameter is an authentication, authorization, and accounting protocol that includes application programming interfaces (APIs) for access to information and configuration. Diameter manages the exchange of information, such as authentication, authorization, accounting, policy and charging control and mobility. 
     An example Diameter protocol stack  200  illustrates how the Diameter protocol may be utilized consistently with the present disclosure. Physical layer  202  may comprise the physical network (e.g., Ethernet) via which devices such as UE  102  may interact in system  100 . IP protocol layer  204  may format the packets that will be transmitted via the physical layer  202  as IP packets. Stream control transmission protocol (SCTP) layer  206  is a transport-layer protocol that provides features such as connection-oriented data stream support, reliability, flow control and multiplexing. Diameter base protocol layer  208  provides, for example, capabilities-exchange-request/capabilities-exchange-answer (CER/CEA) message capabilities for use in supporting Diameter applications. 
     Example Diameter applications are illustrated in the top level of  FIG.  2   . In some example embodiments, a Diameter services management application  210 , executing on a computing device, is used to implement real-time service-resource control for a variety of end user services such as network access, session initiation protocol (SIP) services, messaging services, video services, social media access, download services, etc. In an example embodiment, the GSRA is embodied as a new Diameter Agent or extension, but other embodiments may utilize other types of GSRA devices to manage services (e.g., to configure network devices and collect network and service information). 
     The Diameter mobile IPv4 application  212  may allow a Diameter server to authenticate, authorize, and collect accounting information for Mobile IPv4 services rendered to a mobile node. Diameter session initiation protocol (SIP)  214  is a service that allows a Diameter client to request the authentication of users and authorization of SIP resources usage from a Diameter server. Diameter credit control application  216  may provide a general solution to real-time cost and credit-control. 
     Diameter services may be utilized to obtain a wide variety of service-related information, such as geographic location of clients, traffic statistics for the service, traffic statistics by geographic location, statistics by service server or server pool, etc. This information is used, in some example embodiments, for allocation service resources by geography and by network location. 
       FIG.  3    is a block flow diagram illustrating an example embodiment of messaging pathways used to classify and route data service requests. Some current solutions for load balancing on the network are based on measuring traffic at level 2 or level 3, but this information is insufficient to identify in detail how clients are accessing the server or what the quality is of the service provided. The present embodiments look at traffic data at higher levels (4-7) to better distribute the allocation of service servers or handlers. For example, by looking at the location of the client instead of just looking at the IP address or VLAN address, it is possible to reduce the latency for accessing the service and to quickly adjust configuration during congestion. 
     The orchestrator  312  is the entity that manages the allocation of service resources throughout the network, or throughout a domain within the network. The domain may be defined by any of a plurality of criteria selected from a group consisting of geographic area, national borders, state borders, county borders, regional borders, continental borders, internet service provider (ISP) network, one or more services, or server provider. In other example embodiments different criteria for allocating the domain to the orchestrator  312  are also possible. 
     The GSRAs  308 A,  308 B, and  308 M (collectively designated as  308 ) are computer-implemented agents for setting up service pools to implement the services and for collecting network traffic information. As used herein, network traffic information may include statistics related to traffic in the network, the statistics being obtained for any one of the seven layers of the ISO model. Therefore, the network traffic information may include IP traffic statistics or service-delivery statistics (e.g., number of active streams for a particular service). Further, service traffic information includes statistics regarding service delivery for a particular service. 
     The GSRAs  308  are distributed throughout the network to configure service handler pools at specific locations, based on captured service traffic information data. The GSRAs  308  are able to route traffic based on message type, geographic location, and the service type. The GSRAs  308  are also able to load balance the service traffic across multiple geographic service handlers. In some example embodiments, the GSRAs  308  are executed within a computer device, which is generally referred to as processor A  304 A, processor B  304 B, and processor M  304 M (collectively, processor  304 ). 
     The GSRAs  308  and the orchestrator  312  cooperate to implement service-routing policies, and management operations may be performed by either the orchestrator  312 , by the GSRAs  308 , or both. Some example embodiments are presented regarding the division of responsibilities between the orchestrator  312  and the GSRAs  308 , but other embodiments may perform different functions at different components. For example, in one implementation, the orchestrator  312  may be implemented within a GSRA  308 . This allows addressing the needs of low-latency services where the scale up decisions can be made locally. 
     Service handler pools  310 A,  310 B, and  310 C (collectively, service handler pools  310 ) include one or more servers configured to implement a network service, and perform service-related operations, such as responding to service requests. Each server in the pool may be implemented as one of several types of computer devices, such as a dedicated server, a shared server, a virtual server, a virtual machine, a process within a server, etc. 
     In some embodiments, GSRAs  308  are configured by orchestrator  312  to identify activated service handler pools  310  and their associated applications. In some embodiments, orchestrator  312  also provides GSRAs  308  with instructions on how to communicate with the activated service handler pools  310 , for example by providing an Internet Protocol (IP) address of the service handler circuit. In an example embodiment, GSRA  308 A configures processor A  304 A to listen for data service requests received for a particular application, and also programs processor A  304 A with the appropriate service handler pool  310 A,  310 B, or  310 C for routing the requests. 
     In some embodiments, processor  304  is implemented as a hardware circuit. In some example embodiments, processor  304  is implemented in hardware within an intelligent network interface card (NIC) programmed with routing rules. In other embodiments, the processor  304  is implemented as a software routine, consisting of machine-executable instructions to be executed on a processor. In some embodiments, the processor  304  is implemented as a virtual machine. 
     Incoming traffic  302  is received by the processors  304 , such as processor A  304 A. In particular, incoming traffic  302  is received by the service routing (SR) per message/location/service  306 A, which provides routing on a per message, per location, and per service basis. As illustrated, SR  306 A, having been programmed for selecting a service handler pool  310  to route requests, forwards the received data service requests to one of the service handler pools  310 . In some embodiments, after processing the data service request, the service handler circuit that received the request provides a response to the data service request to processor A  304 A. 
     As illustrated, the service handler pools  310  include a set of multiple processing servers or handlers that provide service functionality for one or more network services. In some embodiments, at least one of the processing pools  310  is stateless, meaning that routing can be done at a message level and data service requests can go to any one of the processing pools in the available pool of processing pools. In other embodiments, one or more of the processing pools of service handler pools  310  is stateful, meaning that when a session starts, all subsequent requests have to go to the same service handler instance. 
     In an example embodiment, the orchestrator  312  configures GSRA  308 A according to a default configuration. The GSRA  308 A in turn programs processor A  304 A to listen for data service requests associated with an application, and to identify which service handler pool  310  is to be used for routing incoming data service requests. 
     Processor A  304 A classifies incoming data service requests according to their associated application or service. Having been programmed to identify which service handler pool  310  is associated with each service application, processor A  304 A routes a data service request to the appropriate service handler pool  310  (e.g., service 1 handler pool  310 C). After processing the data service request, the service handler pool  310 C provides a response to processor A  304 A. In some embodiments, the data service request does not warrant a response. 
     In some example embodiments, a copy of the incoming traffic  302  is routed to each processor  304 . In alternate embodiments, incoming traffic  302  is routed to multiple separate network interfaces, one for each of processor A  304 A, processor B  304 B, and processor M  304 M. 
     In an example embodiment, GSRA  308 A programs processor A  304 A to listen for Transmission Control Packets (TCP) and Stream Control Transmission Protocol (SCTP) packets arriving on port  3868 , which has been defined in RFC 6733 as the port running the Diameter protocol for both TCP and SCTP. In some embodiments, the diameter protocol is defined by at least RFC 6733. Embodiments disclosed herein are not to be limited to any particular protocol. The GSRA  308 A may program processor A  304 A to listen for packets defined by additional protocols, such as transport protocols defined with respect to 5G networks. 
     In some embodiments, the GSRA  308 A programs processor A  304 A to listen for UDP packets arriving on port  1813 , which is dedicated to Radius protocol-based traffic, which is defined by at least RFC 2866. 
     The GSRA  308  in some embodiments is implemented as a hardware circuit. In other embodiments, GSRA  308  is implemented as a software routine consisting of computer-executable instructions that, when executed by a processor or processor core, perform at least one embodiment disclosed herein. In alternate embodiments, the GSRA  308  is implemented as a virtual machine. 
     The service handler pools  310 , in some embodiments, are comprised of hardware circuits. In other embodiments, service handler pools  310  are implemented as software routines consisting of computer-executable instructions that, when executed, perform at least one embodiment disclosed herein. In alternate embodiments, the service handler pools  310  are virtual machines. 
     The orchestrator  312 , in some example embodiments, is implemented as a hardware circuit. In other embodiments, the orchestrator  312  is implemented as a software routine consisting of computer-executable instructions that, when executed by a processor or processor core, perform at least one embodiment disclosed herein. In alternate embodiments, the orchestrator  312  is implemented as a virtual machine. 
     In some example embodiments, the GSRAs  308  are configured to identify traffic above level 4 into the Diameter protocol messages. This allows the identification of Diameter traffic, specific message types, geographic location information, and the type of service (e.g., rating group parameter). The rating group and the location group parameters identify the service and location in the diameter message. The configuration of these routing flow rules on a NIC, or at the virtual-switch level, distributes the service-specific traffic to geographic-specific service handlers. 
     In some example embodiments, the orchestrator  312  identifies the resources for delivering a network service based on the current service traffic, where the resources are classified by geographic area (e.g., locations A, B, and C) from a plurality of geographic areas. The orchestrator  312  then determines which service pools  310  are to be configured to assign the identified resources for delivering the service based on the geographic areas. For example, some areas may have a large amount of service traffic, so service handler pools  310  may be added, while other areas having low service traffic may require a decrease in the current number of service handlers or leave the current configuration unchanged. 
     The orchestrator  312  also checks the availability of resources in the different locations and identities the resources based on the traffic and availability. In some cases, the orchestrator may request a migration of resources from a geographic location to another, such as by moving virtual machines two different servers in different locations. The orchestrator  312  selects the GSRAs  308  that must be accessed to be able to configure any new changes to the resource pool. Therefore, the service agents are selected to provide configurability for the service pools to deliver the network service for the identified resources. The orchestrator then sends respective configuration rules to each selected service agent, the configuration rules being configured to establish the service pools for delivering the network service across the plurality of geographic areas. 
     Service traffic information is collected periodically by the GSRAs  308  and shared with orchestrator  312 . The orchestrator  312 , or the GSRAs  308 , then adjust the resources for delivering the network service based on the collected service traffic information, and updated configuration rules are sent to the service agents based on the adjustment. 
     The orchestrator  312  sends, in some example embodiments, geographic service routing configuration to the GSRA  308 . In an example embodiment, the geographic service routing configuration includes one or more of a message identifier used to discriminate between the start of a session and any other message in the session, a service classifier that includes the service request identifier (ID), a location classifier that identifies the location of the request, a traffic sliced in milliseconds that identifies the time period where service requests are counted and reported, and a service pool that includes the address of the pool of geographic service handlers to handle requests. 
       FIG.  4    is a timing diagram illustrating an embodiment of a process for scaling out a processing resource. The example embodiment illustrated in  FIG.  4    shows the deployment and configuration of contracted services per location over diameter CCR messages. Location and services are identified, routed, and load balanced. In an example embodiment, the time slice for reporting statistics is 100 ms, but any other time slices are possible, such as in the range from 10 ms to 10 seconds or more. 
     The GSRA  308  polls the location service statistics counter. The flow identities the case where the initial configuration is deployed for known location and services. The monitoring information is relayed back to the orchestrator  312 , which identifies that requests for location C Service 1 has increased, so the orchestrator  312  scales the service handlers for service 1 at this location and reconfigures the service pool to include these new handlers to serve the increased traffic. 
     A timeline below GSRA  308  illustrates the activity of the GSRA  308 , which generates flow rules  404  transmitted to service routing  306 , which configures the service handler pools  310 . The GSRA  308  also communicates with the orchestrator  312  to report service statistics (at operations  408  and  412 ) and to receive routing configuration (at operations  406  and  410 ). 
     In some embodiments, when maximum scaling has been reached (e.g. no more servers are available to deploy new service handlers), the policy configured in the service routing  306  will differentiate based on a set of criteria, such as determining which services are granted access to the service handlers. For example, services that generate revenue may be given higher priority than non-revenue-generating services. Also, services that require low latency may be given a high priority to avoid lengthy re-try mechanisms. In some embodiments, the services associated with each of the diameter request packets are discovered by reading the attribute-value pairs (AVPs) in the messages. Differentiating on a per-service basis allows per-service control and a way of implementing network slicing, for example in 5G networks. 
     The service routing policy may be configured to restrict non-critical, lower priority workloads such as M2M/IoT devices during periods of high load on the network, allowing the network to prioritize critical services. In some embodiments, the policy may be re-configured dynamically based on load. Allowing the policy to be configured on a per-load or per-service basis may also reduce priority for requests from rogue or misbehaving devices, thus reducing their chances of causing high loads on the network. 
     The GSRA  308  configures two rules after time  402 : rule  414  to route to service A instructions for service pool 1, and rule  416  to route to service B instructions for service pool 2. At time  426 , a report is sent to the orchestrator  312  showing that service A traffic has increased (operation  408 ), and the associated service pool is scaled out (operation  410 ) to include extra service handlers. In particular, the service stats reported by the GSRA  308  at operation  408  informed the orchestrator  312  that service pool A needed to be scaled out. Accordingly, after time  426 , routing configuration arrives from the orchestrator to configure service pool 1 (operation  410 ), which provides service A, so the service is scaled out in operation  420 . The routing configuration also shows that a new route to service pool 3, providing service C, is configured at operation  422 . In some embodiments, by time  428 , the traffic is routed and load balanced across service pools. 
     The GSRA  308  is able, in real time, to route and load balance service traffic to specific geographic service handlers. For example, during data storms, the transaction rates on geographic service-specific traffic are reported, and this information may be used to launch new geographic-specific service handlers to react to the data storm in that area, thus guaranteeing response times. 
     In some example embodiments, in a virtualized world, it is possible to move service handlers closer to the edge of the network in order to reduce latency for responding to client requests. If the client demand then moves to a different location (e.g., at different times of the day), then the service resources can be moved to a different location in order to follow the client demand. Geographic location thus becomes a parameter for determining where the active handlers are situated. 
       FIG.  5 A  is a table for storing service data, according to some example embodiments. The service table  502  is for storing service-related data, and in an example embodiment, the service table  502  includes fields for a service identifier, a service priority, a maximum latency for the service, a cost model, a revenue model, a list of GSRAs  308 , and a service pool table identifier. 
     The service identifier is a unique identifier assigned to each service, and it is used to link the information to other tables. The service priority identifies the service delivery priority for the different services, where services with higher priority will be allocated services with a higher priority than services with lower priority. 
     The maximum latency is the value of the desired maximum latency for responses submitted by clients to the service servers (e.g., 900 ms). The cost model and the revenue model identify a cost for delivering the service and a revenue for delivering the service. For example, a cost model may include that the service delivery provider has to pay fees or royalties to a third party, and the revenue model may identify if the service is delivered for free or if the user pays for accessing the service. 
     The list of GSRAs  308  identifies a set of GSRAs  308  configured for delivering the service (as illustrated in  FIG.  3   ). For example, service S1 may utilize GSRAs G7 and G15, while service S2 may utilize GSRAs G1, G2, G3, and G5. The GSRA identifiers are linked to GSRA table  504  described below with reference to  FIG.  5 B . The service pool table identifier identifies the service pool table identifier that stores information for the corresponding service pool, as illustrated below with reference to  FIG.  6 A . 
     One example of how service resources are allocated under changing conditions is when allocating resources for a public event with a large attendance, such as a rock concert or a football match. If all the traffic from the rock concert is routed to the same place as the traffic from the rest of the city, there may be a reduction in service for both the people at the concert and the people in the city. In an example embodiment, the location of the concert is identified and then resources are scaled up to serve the temporary large number of people at this one location. This way, service quality is not impacted for the people in the city that are not attending the concert. 
     If the network has a large number of service servers, the embodiments presented herein allow the service provider to allocate servers based on geographic location and service demand. In some example embodiments, resources may be allocated based on their ability to generate revenue, where some revenue-generating services are given higher priority and more resources in order to improve the revenue stream, as compared to services that do not provide revenue. 
       FIG.  5 B  is a table for storing geographic service routing agent (GSRA  308 ) data, according to some example embodiments. The GSRA table  504  stores GSRA-related data, and in an example embodiment, the GSRA table  504  includes a GSRA identifier, a network address, a set of geographic locations managed by the GSRA, services managed by the GSRA, and service pools managed by the GSRA. 
     The GSRA identifier is a unique identifier for each GSRA  308 , and is linked to a field in the service table  502 . The network address includes information for accessing the GSRA, such as an IP address and a port number, but other types of identifier might be used to access the service. The “locations managed” field includes a set of the locations (which may be empty) managed by the corresponding GSRA  308 , and the “pools managed” field includes a set (which may be empty) of the service handler pools configured by the GSRA  308 . 
     In an example embodiment, the geographic service request classification is provided with routing and load-balancing for online charging systems. The online charging systems host revenue-generating services that actively monitor users&#39; sessions in real time (e.g., granting quota with configurable times for use). 
     Instead of monolithic nodes that do not differentiate between location or different service-type requests, geographic service handlers are utilized. Instead of having all service requests served from the same location with traffic traversing the same path and all services having the same response time, the geographic specific service handlers have custom functionality, guaranteed response times, location isolation, and may be geographically orchestrated and scaled independently. 
     The GSRA  308  is configured by the orchestrator  312  at geographic and service levels in order to route and load-balance traffic to one or more geographic service handler pools, while providing feedback for monitoring and orchestration to allow for geographic service handling scaling. Further, launching of new geographic-specific charging services can be isolated and tested independently by the instantiation of the service handlers and the addition of new routing rules via the GSRA  308 . 
       FIG.  6 A  is a table  602  for storing service pool data for a service, according to some example embodiments.  FIG.  6 A  illustrates a service pool table  602  associated with a particular service (e.g., service S1 in service table  502 ) that identifies the resources in use for the service. The service pool table  602  includes entries for a location identifier (e.g., location A in  FIG.  3   ), a set of GSRAs  308  allocated for managing this service, the current number of handlers (e.g., servers) allocated to this service and their respective location, a maximum number of handlers, a planned capacity, and the current number of streams. 
     The planned capacity identifies the estimated number of users accessing the service at the respective location, and the current number of streams identifies the current number of streams (e.g., users) accessing the service at the respective location. 
     For example, a pool of servers is configured to handle 1000 calls per second, and to report every second on the number of calls being handled. If the pool of servers (e.g., via the GSRA  308 ) reports that there 500 calls per second being handled, the orchestrator  312  may reduce the number of servers in that pool, as the pool is right now over-served. On the other hand, if a report is received with 1500 calls per second, the orchestrator  312  may increase the number of servers in that pool. This helps prevent service outages and an undesirable increase in response latency. Further, if the service provider is paid according to the number of servers in use, the service provider may optimize the number of servers in use to reduce expenses. 
       FIG.  6 B  is a table for storing service capacity data per location, according to some example embodiments. The service capacity table  604  includes fields for a service identifier, a pool capacity value, a number of planned streams supported, a current number of streams, and a current measured service latency. 
     The service identifier is linked to the service table  502 . The pool capacity indicates the maximum number of handlers reserved for that service in that location. The planned number of streams supported identifies the maximum planned number of clients supported at a particular time. The current number of streams indicates how many streams are accessing the service at that location at the present time, and the current latency identifies an average latency response time for the handlers in the current allocation. 
     Multiple services may be supported at a given location, such as access to Facebook®, messaging applications, a computer on a car, a computer on a refrigerator, or some other IoT services, such as a computer on a vending machine. At a moment in time, the orchestrator  312  may decide to scale down some of the services in order to scale out some revenue-generating services. For example, services generated at mobile phones may produce higher revenues than services for communicating to a vending machine. This way, the network traffic is configured appropriately based on service priority. 
     Further, the service resources may be allocated based on the time of day. If, in a certain geography, a large number of calls are made between 8 AM and 9 AM, the services may be configured accordingly to compensate for the spike in demand at that time. Other services, like services related to vending machines, may be throttled to a later time when there is less traffic on the network. 
     Further, the service handlers may be configured in order to reduce the round-trip time from the client to the server, by allocating service handlers closer to the clients at the edge of the network. Thus, the services may be load balanced across services and across locations. 
     It is noted that the embodiments illustrated in  FIGS.  5 A- 5 B and  6 A- 6 B  are examples and do not describe every possible embodiment. Other embodiments may utilize different fields, or combine tables, or distribute the table content across a plurality of devices, or utilize fewer or additional tables, etc. The embodiments illustrated in  FIGS.  5 A- 5 B and  6 A- 6 B  should therefore not be interpreted to be exclusive or limiting, but rather illustrative. 
       FIG.  7    is an architecture for an orchestrator  312 , according to some example embodiments. In an example embodiment, the orchestrator  312  includes a service planning module  702 , a GSRA manager  710 , a service allocation module  704 , a traffic data collection module  712 , a diameter services management application  210 , a user interface  714 , and a plurality of databases, which include the service capacity table  604 , the service table  502 , the service pool table  602 , and the GSRA table  504 . 
     The service planning module  702  determines how the service resources are allocated, including determining which service handlers are allocated by geography. The GSRA manager  710  manages the allocation of GSRAs  308 , which includes instantiating the GSRAs  308  in the respective processors and sending the configuration parameters to the GSRA  308 . 
     The service allocation module  704  works with the service planning module  702  and the GSRA manager  710 , at least, to determine how service resources are allocated dynamically. The traffic data collection module  712  collects traffic data from the network (e.g., the GSRAs  308 ), and this data is made available to the other modules in the orchestrator  312 . 
     The diameter services management application  210  provides support for the diameter protocol and the respective APIs. The user interface  714  provides user-interface abilities for the system administrator to configure the orchestrator  312  and to obtain data, such as live traffic data or current allocation of resources. 
     The cooperation between the orchestrator  312  and the GSRAs  308  allows for geographic service and traffic isolation, improved performance, and better response times as traffic is segregated, per location and per service, and routed to specific service handlers, as well as the detection of geographic service hot spots (e.g., a sports event) to trigger deployment of more service handlers for this geographic area. 
     It is noted that the embodiments illustrated in  FIG.  7    are examples and do not describe every possible embodiment. Other embodiments may utilize different modules, fewer modules, or combine the functionality of several modules, etc. The embodiments illustrated in  FIG.  7    should therefore not be interpreted to be exclusive or limiting, but rather illustrative. 
       FIG.  8    is a flowchart of a method  800  for managing service resources at an orchestrator, according to some example embodiments. While the various operations in the flowcharts of  FIGS.  8 - 11    are presented and described sequentially, one of ordinary skill will appreciate that some or all of the operations may be executed in a different order, be combined or omitted, or be executed in parallel. 
     At operation  802 , the orchestrator  312  identifies the service needs by geography. The service needs may be identified by a system administrator, or by analyzing traffic patterns (current and historical), or a combination of both. The service needs include parameters related to the demand on the service, such as overall user demand, or demand by region, etc. 
     From operation  802 , the method  800  flows to operation  804  for identifying the GSRA  308  based on the service needs. From operation  804 , the method  800  flows to operation  816 , which is performed for each GSRA  308 . 
     At operation  806 , the routing configuration is calculated. The routing configuration determines how service traffic is routed by geography, and by service pool. In operation  808 , the routing configuration is sent to the appropriate GSRA or GSRAs  308 . 
     At operation  810 , the orchestrator  312  receives traffic and service data periodically from the set of GSRAs  308 . From operation  810 , the method  800  flows to operation  812 , where a check is made to determine if the current configuration is to be adjusted based on the traffic and service data. If the configuration is to be adjusted, the method  800  flows to operation  814 , and if the configuration data is not to be adjusted, the method  800  flows back to operation  810 . 
     At operation  814 , the routing configuration is recalculated based on the received service and the traffic data. From operation  814 , the method  800  flows back to operation  802 . 
       FIG.  9    is a flowchart of a method  814  for managing resources to deliver network services, according to some example embodiments. At operation  902 , the current resource pool being utilized is identified, and at operation  904  the current services being delivered, as well as the geographic scope of the service handler pools, are identified. 
     From operation  904 , the method  814  flows to operation  906  to determine service priority parameters and cost-of-delivery parameters. For example, the service priority parameters may include a desired server latency (or network latency) for responding to clients and the cost-of-delivery parameters may determine the cost per active service server. At operation  908 , a determination is made on the service delivery needs at each location-based on the load (e.g., number of active streaming clients). 
     From operation  908 , the method  814  flows to operation  920 , which is performed for all services and all geographic locations. At operation  910 , a check is made to determine if too many resources are in use. For example, this determination may be made when the number of clients per server goes below a predetermined threshold (e.g., 100 clients per server). 
     If there are too many resources in use, the method  814  flows to operation  912 , and if there are not too many resources in use, the method  814  flows to operation  914 . At operation  912 , allocated resources that are not needed are released. At operation  914 , a calculation is made to add up all the resources needed to support all services, and based on this calculation, a check is made at operation  916  to determine if there is service congestion (e.g., the number of clients per server is above a predetermined threshold at one or more geographic locations). 
     If there is congestion, the method  814  flows to operation  918 , and if there is no congestion, the method  814  flows back to operation  902 . At operation  918 , the available resources are allocated by location based on the service priority and the current demand. 
       FIG.  10    is a flowchart of a method  1000  for operations executed by the GSRA  308 , according to some example embodiments. At operation  1002 , the GSRA  308  identifies the resources available for location. From operation  1002 , the method  1000  flows to operation  1004  for receiving configuration data from the orchestrator  312 . 
     From operation  1004 , the method  1000  flows to operation  1006 , where the service handler pools are configured based on the received configuration from the orchestrator  312 . From operation  1006 , the method  1000  flows to operation  1008 , where service and traffic information is gathered by the GSRA  308 . In an example embodiment, the Diameter protocol is utilized to gather the service and traffic information, but other example embodiments may utilize other protocols, such as the Radius protocol. 
     From operation  1008 , the method  1000  flows to operation  1010  for periodically reporting service and traffic information to the orchestrator  312 . From operation  1010 , the method  1000  flows back to operation  1002 . 
       FIG.  11    is a flowchart of a method  1100  for managing resources to deliver a network service in a distributed configuration, according to some example embodiments. At operation  1102 , resources are identified for delivering a network service, the resources being classified by geographic area from a plurality of geographic areas. 
     From operation  1102 , the method  1100  flows to operation  1104 , where the service agents to configure the identified resources are determined. At operation  1106 , the orchestrator  312  sends respective configuration rules to each determined service agent. The configuration rules are configured to establish service pools for delivering the network service across the plurality of geographic areas. 
     From operation  1106 , the method  1100  flows to operation  1108  for collecting service traffic information from the service agents. At operation  1110 , the resources for delivering the network service are adjusted based on the collected service traffic information, and at operation  1112 , updated respective configuration rules are sent to each determined service agent based on the adjusting at operation  1110 . 
       FIG.  12    is a block diagram illustrating an example of a machine  1200  upon which one or more example embodiments may be implemented. In alternative embodiments, the machine  1200  may operate as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine  1200  may operate in the capacity of a server machine, a client machine, or both in server-client network environments. In an example, the machine  1200  may act as a peer machine in peer-to-peer (P2P) (or other distributed) network environment. The machine  1200  may be a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only 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 methodologies discussed herein, such as cloud computing, software as a service (SaaS), or other computer cluster configurations. 
     Examples, as described herein, may include, or may operate by, logic or a number of components, or mechanisms. Circuitry is a collection of circuits implemented in tangible entities that include hardware (e.g., simple circuits, gates, logic, etc.). Circuitry membership may be flexible over time and underlying hardware variability. Circuitries include members that may, alone or in combination, perform specified operations when operating. In an example, hardware of the circuitry may be immutably designed to carry out a specific operation (e.g., hardwired). In an example, the hardware of the circuitry may include variably connected physical components (e.g., execution units, transistors, simple circuits, etc.) including a computer-readable medium physically modified (e.g., magnetically, electrically, moveable placement of invariant massed particles, etc.) to encode instructions of the specific operation. In connecting the physical components, the underlying electrical properties of a hardware constituent are changed, for example, from an insulator to a conductor or vice versa. The instructions enable embedded hardware (e.g., the execution units or a loading mechanism) to create members of the circuitry in hardware via the variable connections to carry out portions of the specific operation when in operation. Accordingly, the computer-readable medium is communicatively coupled to the other components of the circuitry when the device is operating. In an example, any of the physical components may be used in more than one member of more than one circuitry. For example, under operation, execution units may be used in a first circuit of a first circuitry at one point in time and reused by a second circuit in the first circuitry, or by a third circuit in a second circuitry, at a different time. 
     Machine (e.g., computer system)  1200  may include a hardware processor  1202  (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory  1204  and a static memory  1206 , some or all of which may communicate with each other via an interlink (e.g., bus)  1208 . The machine  1200  may further include a display device  1210 , an alphanumeric input device  1212  (e.g., a keyboard), and a user interface (UI) navigation device  1214  (e.g., a mouse). In an example, the display device  1210 , input device  1212  and UI navigation device  1214  may be a touch screen display. The machine  1200  may additionally include a mass storage device (e.g., drive unit)  1216 , a signal generation device  1218  (e.g., a speaker), a network interface device  1220 , and one or more sensors  1221 , such as a global positioning system (GPS) sensor, compass, accelerometer, or other sensor. The machine  1200  may include an output controller  1228 , such as a serial (e.g., universal serial bus (USB), parallel, or other wired or wireless (e.g., infrared (IR), near field communication (NFC), etc.) connection to communicate or control one or more peripheral devices (e.g., a printer, card reader, etc.). 
     The storage device  1216  may include a machine-readable medium  1222  on which is stored one or more sets of data structures or instructions  1224  (e.g., software) embodying or utilized by any one or more of the techniques or functions described herein. The instructions  1224  may also reside, completely or at least partially, within the main memory  1204 , within static memory  1206 , or within the hardware processor  1202  during execution thereof by the machine  1200 . In an example, one or any combination of the hardware processor  1202 , the main memory  1204 , the static memory  1206 , or the storage device  1216  may constitute machine-readable media. 
     While the machine-readable medium  1222  is illustrated as a single medium, the term “machine-readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) configured to store the one or more instructions  1224 . 
     The term “machine-readable medium” may include any medium that is capable of storing, encoding, or carrying instructions  1224  for execution by the machine  1200  and that cause the machine  1200  to perform any one or more of the techniques of the present disclosure, or that is capable of storing, encoding or carrying data structures used by or associated with such instructions  1224 . Non-limiting machine-readable medium examples may include solid-state memories, and optical and magnetic media. In an example, a massed machine-readable medium comprises a machine-readable medium  1222  with a plurality of particles having invariant (e.g., rest) mass. Accordingly, massed machine-readable media are not transitory propagating signals. Specific examples of massed machine-readable media may include: non-volatile memory, such as semiconductor memory devices (e.g., Electrically Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM)) and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. 
     The instructions  1224  may further be transmitted or received over a communications network  1226  using a transmission medium via the network interface device  1220  utilizing any one of a number of transfer protocols (e.g., frame relay, internet protocol (IP), transmission control protocol (TCP), user datagram protocol (UDP), hypertext transfer protocol (HTTP), etc.). Example communication networks may include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), mobile telephone networks (e.g., cellular networks), Plain Old Telephone (POTS) networks, and wireless data networks (e.g., Institute of Electrical and Electronics Engineers (IEEE) 802.11 family of standards known as Wi-Fi®, IEEE 802.16 family of standards known as WiMax®), IEEE 802.15.4 family of standards, peer-to-peer (P2P) networks, among others. In an example, the network interface device  1220  may include one or more physical jacks (e.g., Ethernet, coaxial, or phone jacks) or one or more antennas to connect to the communications network  1226 . In an example, the network interface device  1220  may include a plurality of antennas to wirelessly communicate using at least one of single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) techniques. The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions  1224  for execution by the machine  1200 , and includes digital or analog communications signals or other intangible medium to facilitate communication of such software. 
     Additional Notes &amp; Examples 
     Example 1 is a system comprising: a geographic service routing agent (GSRA) to: identify resources for delivering a network service, the resources being classified by geographic area from a plurality of geographic areas; select service agents to configure the identified resources, each service agent to manage service pools for delivering the network service across at least one geographic area, the service agents being selected to provide configurability for the service pools to deliver the network service for the identified resources; send respective configuration rules to each selected service agent, the configuration rules configured to establish the service pools for delivering the network service across the plurality of geographic areas; collect service traffic information from the service agents; adjust the resources for delivering the network service based on the collected service traffic information; and send updated respective configuration rules to the service agents based on the adjustment. 
     In Example 2, the subject matter of Example 1 optionally includes wherein to identify the resources the GSRA is further to: identify a number of clients of the network service by each of the geographic areas. 
     In Example 3, the subject matter of any one or more of Examples 1-2 optionally include wherein to identify the resources the GSRA is further to: determine a number of service handlers by geography. 
     In Example 4, the subject matter of any one or more of Examples 1-3 optionally include wherein to identify the resources the GSRA is further to: allocate service handlers to locations at an edge of a network for lower latency based on client demand for the network service. 
     In Example 5, the subject matter of Example 4 optionally includes wherein to select the service agents the GSRA is further to: determine an amount of clients accessing the network service by geographic area; and select a service handler based on the amount of clients accessing the service. 
     In Example 6, the subject matter of Example 5 optionally includes wherein at least one service agent configures the selected service handlers to deliver the network service. 
     In Example 7, the subject matter of any one or more of Examples 1-6 optionally include wherein each service pool includes at least one handler executing on a server device or in a processor configured within a network switching device. 
     In Example 8, the subject matter of any one or more of Examples 1-7 optionally include wherein to collect the service traffic information the GSRA is further to: utilize a diamond application programming interface to collect service traffic statistics. 
     In Example 9, the subject matter of any one or more of Examples 1-8 optionally include wherein to adjust the resources the OSRA is further to: reduce resources allocated at a geographic location when demand decreases at the geographic location. 
     In Example 10, the subject matter of any one or more of Examples 1-9 optionally include wherein to adjust the resources the GSRA is further to: determine that more resources are needed to support a quality of service metric; identify whether additional resources are available; and assign additional resources when the additional resources are available. 
     In Example 11, the subject matter of Example 10 optionally includes wherein additional resources are not available, wherein the GSRA is further to: identify another network service with a lower priority and allocated resources; and reallocate resources from the another network service to the network service. 
     In Example 12, the subject matter of any one or more of Examples 1-11 optionally include wherein a first configuration rule comprises a number of service handlers to be allocated at a first geographic area. 
     In Example 13, the subject matter of any one or more of Examples 1-12 optionally include wherein a second configuration rule includes a policy for collecting service metrics and for reporting the collected service metrics. 
     Example 14 is a method comprising: identifying resources for delivering a network service, the resources being classified by geographic area from a plurality of geographic areas; selecting service agents to configure the identified resources, each service agent to manage service pools for delivering the network service across at least one geographic area, the service agents being selected to provide configurability for the service pools to deliver the network service for the identified resources; sending respective configuration rules to each selected service agent, the configuration rules configured to establish the service pools for delivering the network service across the plurality of geographic areas; collecting service traffic information from the service agents; adjusting the resources for delivering the network service based on the collected service traffic information; and sending updated respective configuration rules to the service agents based on the adjusting. 
     In Example 15, the subject matter of Example 14 optionally includes wherein the identifying resources further comprises: identifying a number of clients of the network service by each of the geographic areas. 
     In Example 16, the subject matter of any one or more of Examples 14-15 optionally include wherein the identifying resources further comprises: determining a number of service handlers by geography. 
     In Example 17, the subject matter of any one or more of Examples 14-16 optionally include wherein the identifying resources further comprises: allocating service handlers to locations at an edge of a network for lower latency based on client demand for the network service. 
     In Example 18, the subject matter of Example 17 optionally includes wherein the selecting of service agents further comprises: determining an amount of clients accessing the network service by geographic area; and selecting a service handler based on the amount of clients accessing the service. 
     In Example 19, the subject matter of Example 18 optionally includes wherein at least one service agent configures the selected service handlers to deliver the network service. 
     In Example 20, the subject matter of any one or more of Examples 14-19 optionally include wherein each service pool includes at least one handler executing on a server device or in a processor configured within a network switching device. 
     In Example 21, the subject matter of any one or more of Examples 14-20 optionally include wherein the collecting service traffic information further comprises: utilizing a diamond application programming interface to collect service traffic statistics. 
     In Example 22, the subject matter of any one or more of Examples 14-21 optionally include wherein the adjusting the resources further comprises: reducing resources allocated at a geographic location when demand decreases at the geographic location. 
     In Example 23, the subject matter of any one or more of Examples 14-22 optionally include wherein the adjusting the resources further comprises: determining that more resources are needed to support a quality of service metric: identifying whether additional resources are available; and assigning additional resources when the additional resources are available. 
     In Example 24, the subject matter of Example 23 optionally includes wherein additional resources are not available, the method further comprising: identifying another network service with a lower priority and allocated resources; and reallocating resources from the another network service to the network service. 
     In Example 25, the subject matter of any one or more of Examples 14-24 optionally include wherein a first configuration rule comprises a number of service handlers to be allocated at a first geographic area. 
     In Example 26, the subject matter of any one or more of Examples 14-25 optionally include wherein a second configuration rule includes a policy for collecting service metrics and for reporting the collected service metrics. 
     Example 27 is a system comprising means to perform any method of Examples 14 to 26. 
     Example 28 is at least one machine-readable media including instructions that, when executed by a machine, cause the machine to perform any method of Examples 14-26. 
     Example 29 is at least one machine readable medium including instructions that, when executed by a machine, cause the machine to: identify resources for delivering a network service, the resources being classified by geographic area from a plurality of geographic areas; select service agents to configure the identified resources, each service agent to manage service pools for delivering the network service across at least one geographic area, the service agents being selected to provide configurability for the service pools to deliver the network service for the identified resources; send respective configuration rules to each selected service agent, the configuration rules configured to establish the service pools for deliver the network service across the plurality of geographic areas; collect service traffic information from the service agents; adjust the resources for delivering the network service based on the collected service traffic information; and send updated respective configuration rules to the service agents based on the adjusting. 
     In Example 30, the subject matter of Example 29 optionally includes wherein to identify resources the instructions further cause the machine to: identify a number of clients of the network service by each of the geographic areas. 
     In Example 31, the subject matter of any one or more of Examples 29-30 optionally include wherein to identify resources the instructions further cause the machine to: determine a number of service handlers by geography. 
     In Example 32, the subject matter of any one or more of Examples 29-31 optionally include wherein to identify resources the instructions further cause the machine to: allocate service handlers to locations at an edge of a network for lower latency based on client demand for the network service. 
     In Example 33, the subject matter of Example 32 optionally includes wherein to select service agents the instructions further cause the machine to: determine an amount of clients accessing the network service by geographic area; and select a service handler based on the amount of clients accessing the service. 
     In Example 34, the subject matter of Example 33 optionally includes wherein at least one service agent configures the selected service handlers to deliver the network service. 
     In Example 35, the subject matter of any one or more of Examples 29-34 optionally include wherein each service pool includes at least one handler executing on a server device or in a processor configured within a network switching device. 
     In Example 36, the subject matter of any one or more of Examples 29-35 optionally include wherein to collect the service traffic information the instructions further cause the machine to: utilize a diamond application programming interface to collect service traffic statistics. 
     In Example 37, the subject matter of any one or more of Examples 29-36 optionally include wherein to adjust the resources the instructions further cause the machine to: reduce resources allocated at a geographic location when demand decreases at the geographic location. 
     In Example 38, the subject matter of any one or more of Examples 29-37 optionally include wherein to adjust the resources the instructions further cause the machine to: determine that more resources are needed to support a quality of service metric; identify whether additional resources are available; and assign additional resources when the additional resources are available. 
     In Example 39, the subject matter of Example 38 optionally includes wherein additional resources are not available, the instructions further cause the machine to: identify another network service with a lower priority and allocated resources; and reallocate resources from the another network service to the network service. 
     In Example 40, the subject matter of any one or more of Examples 29-39 optionally include wherein a first configuration rule comprises a number of service handlers to be allocated at a first geographic area. 
     In Example 41, the subject matter of any one or more of Examples 29-40 optionally include wherein a second configuration rule includes a policy for collecting service metrics and for reporting the collected service metrics. 
     Example 42 is a system comprising: means for identifying resources for delivering a network service, the resources being classified by geographic area from a plurality of geographic areas; means for selecting service agents to configure the identified resources, each service agent to manage service pools for delivering the network service across at least one geographic area, the service agents being selected to provide configurability for the service pools to deliver the network service for the identified resources; means for sending respective configuration rules to each selected service agent, the configuration rules configured to establish the service pools for delivering the network service across the plurality of geographic areas; means for collecting service traffic information from the service agents; means for adjusting the resources for delivering the network service based on the collected service traffic information; and means for sending updated respective configuration rules to the service agents based on the adjusting. 
     In Example 43, the subject matter of Example 42 optionally includes wherein the identifying resources further comprises: means for identifying a number of clients of the network service by each of the geographic areas. 
     In Example 44, the subject matter of any one or more of Examples 42-43 optionally include wherein the identifying resources further comprises: means for determining a number of service handlers by geography. 
     In Example 45, the subject matter of any one or more of Examples 42-44 optionally include wherein the identifying resources further comprises: means for allocating service handlers to locations at an edge of a network for lower latency based on client demand for the network service. 
     In Example 46, the subject matter of Example 45 optionally includes wherein the selecting of service agents further comprises: means for determining an amount of clients accessing the network service by geographic area; and means for selecting a service handler based on the amount of clients accessing the service. 
     In Example 47, the subject matter of Example 46 optionally includes wherein at least one service agent configures the selected service handlers to deliver the network service. 
     In Example 48, the subject matter of any one or more of Examples 42-47 optionally include wherein each service pool includes at least one handler executing on a server device or in a processor configured within a network switching device. 
     In Example 49, the subject matter of any one or more of Examples 42-48 optionally include wherein the collecting service traffic information further comprises: means for utilizing a diamond application programming interface to collect service traffic statistics. 
     In Example 50, the subject matter of any one or more of Examples 42-49 optionally include wherein the adjusting the resources further comprises: means for reducing resources allocated at a geographic location when demand decreases at the geographic location. 
     In Example 51, the subject matter of any one or more of Examples 42-50 optionally include wherein the adjusting the resources further comprises: means for determining that more resources are needed to support a quality of service metric; means for identifying whether additional resources are available; and means for assigning additional resources when the additional resources are available. 
     In Example 52, the subject matter of Example 51 optionally includes wherein additional resources are not available, the system further comprising: means for identifying another network service with a lower priority and allocated resources; and means for reallocating resources from the another network service to the network service. 
     In Example 53, the subject matter of any one or more of Examples 42-52 optionally include wherein a first configuration rule comprises a number of service handlers to be allocated at a first geographic area. 
     In Example 54, the subject matter of any one or more of Examples 42-53 optionally include wherein a second configuration rule includes a policy for collecting service metrics and for reporting the collected service metrics. 
     The above detailed description includes references to the accompanying drawings, which form a part of the detailed description. The drawings show, by way of illustration, specific embodiments that may be practiced. These embodiments are also referred to herein as “examples.” Such examples may include elements in addition to those shown or described. However, the present embodiments also contemplate examples in which only those elements shown or described are provided. Moreover, the present embodiments also contemplate examples using any combination or permutation of those elements shown or described (or one or more aspects thereof), either with respect to a particular example (or one or more aspects thereof), or with respect to other examples (or one or more aspects thereof) shown or described herein. 
     All publications, patents, and patent documents referred to in this document are incorporated by reference herein in their entirety, as though individually incorporated by reference. In the event of inconsistent usages between this document and those documents so incorporated by reference, the usage in the incorporated reference(s) should be considered supplementary to that of this document; for irreconcilable inconsistencies, the usage in this document controls. 
     In this document, the terms “a” or “an” are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of “at least one” or “one or more.” In this document, the term “or” is used to refer to a nonexclusive or, such that “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Also, in the following claims, the terms “including” and “comprising” are open-ended, that is, a system, device, article, or process that includes elements in addition to those listed after such a term in a claim are still deemed to fall within the scope of that claim. Moreover, in the following claims, the terms “first,” “second,” and “third,” etc. are used merely as labels, and are not intended to impose numerical requirements on their objects. 
     The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. Other embodiments may be used, such as by one of ordinary skill in the art upon reviewing the above description. The Abstract is to allow the reader to quickly ascertain the nature of the technical disclosure and is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. Also, in the above Detailed Description, various features may be grouped together to streamline the disclosure. This should not be interpreted as intending that an unclaimed disclosed feature is essential to any claim. Rather, inventive subject matter may lie in less than all features of a particular disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment. The scope of the embodiments should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.