Patent Publication Number: US-2017353537-A1

Title: Predictive load balancing for a digital environment

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation-in-part of U.S. application Ser. No. 15/409,510, titled “MULTI-CORPORATION VENTURE PLAN VALIDATION EMPLOYING AN ADVANCED DECISION PLATFORM” and filed on Jan. 18, 2017, which is a continuation-in-part of U.S. application Ser. No. 15/379,899, titled “ INCLUSION OF TIME SERIES GEOSPATIAL MARKERS IN ANALYSES EMPLOYING AN ADVANCED CYBER-DECISION PLATFORM” and filed on Dec. 15, 2016, which is a continuation-in-part of U.S. application Ser. No. 15/376,657, titled “QUANTIFICATION FOR INVESTMENT VEHICLE MANAGEMENT EMPLOYING AN ADVANCED DECISION PLATFORM” and filed on Dec. 13, 2016, which is a continuation-in-part of U.S. patent application Ser. No. 15/237,625, titled “DETECTION MITIGATION AND REMEDIATION OF CYBERATTACKS EMPLOYING AN ADVANCED CYBER-DECISION PLATFORM”, and filed on Aug. 15, 2016, which is a continuation-in-part of U.S. patent application Ser. No. 15/206,195, titled “SYSTEM FOR AUTOMATED CAPTURE AND ANALYSIS OF BUSINESS INFORMATION FOR RELIABLE BUSINESS VENTURE OUTCOME PREDICTION”, and filed on Jul. 8, 2016, which is continuation-in-part of U.S. patent application Ser. No. 15/186,453, titled “SYSTEM FOR AUTOMATED CAPTURE AND ANALYSIS OF BUSINESS INFORMATION FOR RELIABLE BUSINESS VENTURE OUTCOME PREDICTION” and filed on Jun. 18, 2016, which is a continuation-in-part of U.S. patent application Ser. No. 15/166,158, titled “SYSTEM FOR AUTOMATED CAPTURE AND ANALYSIS OF BUSINESS INFORMATION FOR SECURITY AND CLIENT-FACING INFRASTRUCTURE RELIABILITY”, and filed on May 26, 2016, which is a continuation-in-part of U.S. patent application Ser. No. 15/141,752, titled “SYSTEM FOR FULLY INTEGRATED CAPTURE, AND ANALYSIS OF BUSINESS INFORMATION RESULTING IN PREDICTIVE DECISION MAKING AND SIMULATION, and filed on Apr. 28, 2016, which is a continuation-in-part of U.S. patent application Ser. No. 14/925,974, titled “RAPID PREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USING THE DISTRIBUTED COMPUTATIONAL GRAPH” and filed on Oct. 28, 2015, and is also a continuation-in-part of U.S. patent application Ser. No. 14/986,536, titled “DISTRIBUTED SYSTEM FOR LARGE VOLUME DEEP WEB DATA EXTRACTION”, and filed on Dec. 31, 2015, and is also a continuation-in-part of U.S. patent application Ser. No. 15/091,563, titled “SYSTEM FOR CAPTURE, ANALYSIS AND STORAGE OF TIME SERIES DATA FROM SENSORS WITH HETEROGENEOUS REPORT INTERVAL PROFILES”, and filed on Apr. 5, 2016, the entire specification of each of which is incorporated herein by reference in its entirety. 
    
    
     BACKGROUND OF THE INVENTION 
     Field of the Invention 
     The disclosure relates to the field server load balancing, particularly to load balancing of a server serving data for a digital environment. 
     Discussion of the State of the Art 
     Massively multiplayer online (MMO) games first made their appearance in the mid-1990&#39;s. What started as a niche genre of games with few titles available has matured into dozens of games worldwide, and has gone on to become a huge staple to the gaming industry. As of 2016, with popular MMO games such as BLIZZARD ENTERTAINMENT′S WORLD OF WARCRAFT and SQUARE ENIX&#39;S FINAL FANTASY 14, the total MMO game market revenue was estimated at approximately $20 billion. However, the underlying technology has generally not made much progress. Many of the MMO games of today rely on separate servers to handle population loads. For instance, more popular MMO games may have a higher number of servers to accommodate the demand. Each server may be comprised of virtual nodes, each serving data for a particular section of a game environment. But simply providing more servers for players may not solve many of the problems that still persists today. These problems include long queue times to log into a server during peak gaming hours, server crashes caused by overwhelming demand, or slow response times due to lack of server resources. While some influxes of demand may be predictable, for example peak hours for a server, other influxes may not be so obvious, for example, an in-game event that is unexpectedly popular may cause outages due to the unpredicted congregation of players within a relatively small area. More advanced servers may provide a means to dynamically allocate server resources, but the allocation of resources may not occur until after a high load is already observed and some of the effects of server overburden have may already been experienced by the players. Furthermore, the scalability of such servers may be limited. 
     What is needed is a system that can dynamically deal with load influxes while providing a seamless experience to the players. Such a system would be able to predict load influxes, and dynamically allocate server resources before problems caused by server loading occurs. Also, such a system would be capable of mass scalability, potentially scaling to millions of players. 
     SUMMARY OF THE INVENTION 
     Accordingly, the inventor has conceived a system for predictive load balancing for a digital environment. In a typical embodiment, the system continuously monitors and evaluates current load data, and analyzed against historical data pertaining to node load. The evaluation includes predictive analysis to allow the system to forecast load influxes, and preemptively enact load balancing measures before the effects of server overburden is encountered by the players to allow an uninterrupted, and overall smooth experience. 
     In one aspect of the invention, a system for predictive load balancing for a digital environment is provided comprising a time series data retrieval and storage server comprising a memory, a processor, and a plurality of programming instructions stored in the memory thereof and operable on the processor thereof, wherein the programmable instructions, when operating on the processor, cause the processor to monitor and record load-related data of a computational node; and an automated planning service module comprising a memory, a processor, and a plurality of programming instructions stored in the memory thereof and operable on the processor thereof, wherein the programmable instructions, when operating on the processor, cause the processor to retrieve node load-related data from the time series data retrieval and storage server, perform predictive analysis using the node load-related data, and determine whether load balancing is required. Also in one embodiment, additional nodes are automatically added and integrated when load balancing has been determined to be required. Also in one embodiment, resources from other nodes are reallocated to at least a busy node when load balancing has been determined to be required. Also in one embodiment, a connector module is included, wherein a node may utilize the connector module to communicate and exchange data with other nodes to facilitate load balancing. Also in one embodiment, at least a portion of the load-related data used for predictive analysis is user contribution to system load. Also in one embodiment, at least a portion of the load-related data used for predictive analysis is game-related environmental contribution to system load. Also in one embodiment, at least a portion of the load-related data used for predictive analysis are scheduled events. 
     In another aspect of the invention, a method for predictive load balancing for a digital environment is provided, comprising the steps of (a) monitoring and recording load-related data of a computational node with a time series data retrieval and storage server; (b) retrieving node load-related data from the time series data retrieval and storage server with an automated planning service module; (c) performing predictive analysis using the node load-related data with the automated planning service module; and (d) determining whether load balancing is required with an automated planning service module. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWING FIGURES 
       The accompanying drawings illustrate several aspects and, together with the description, serve to explain the principles of the invention according to the aspects. It will be appreciated by one skilled in the art that the particular arrangements illustrated in the drawings are merely exemplary, and are not to be considered as limiting of the scope of the invention or the claims herein in any way. 
         FIG. 1  is a diagram of an exemplary architecture of a business operating system according to an embodiment of the invention. 
         FIG. 2A  is a diagram of a multidimensional time series data module as shown in  FIG. 1  further configured for use in various embodiments of the invention. 
         FIG. 2B  is a diagram of an automated planning service module as shown in  FIG. 1  further configured for use in various embodiments of the invention. 
         FIG. 2C  is a diagram of a connector module as shown in  FIG. 1  further configured for use in various embodiments of the invention. 
         FIG. 3  is a diagram of the web application based interface for an indexed global tile module as per one embodiment of the invention. 
         FIG. 4  is a diagram of an indexed global tile module as per one embodiment of the invention. 
         FIG. 5  is a flow diagram illustrating the function of the indexed global tile module as per one embodiment of the invention. 
         FIG. 6A  is a block diagram of an exemplary node as used in various embodiments of the invention. 
         FIG. 6B  is an illustration of a segment of an exemplary system of nodes as used in various embodiments of the invention. 
         FIG. 7  is a flow diagram illustrating a method for gathering data for use in forecasting future loads according to various embodiments of the invention. 
         FIG. 8  is a flow diagram illustrating a method for predictive load balancing according to various embodiments of the invention. 
         FIG. 9  is a block diagram illustrating an exemplary hardware architecture of a computing device used in various embodiments of the invention. 
         FIG. 10  is a block diagram illustrating an exemplary logical architecture for a client device, according to various embodiments of the invention. 
         FIG. 11  is a block diagram illustrating an exemplary architectural arrangement of clients, servers, and external services, according to various embodiments of the invention. 
         FIG. 12  is another block diagram illustrating an exemplary hardware architecture of a computing device used in various embodiments of the invention. 
     
    
    
     DETAILED DESCRIPTION 
     The inventor has conceived, and reduced to practice, a system for predictive load balancing for a digital environment. 
     One or more different aspects may be described in the present application. Further, for one or more of the aspects described herein, numerous alternative arrangements may be described; it should be appreciated that these are presented for illustrative purposes only and are not limiting of the aspects contained herein or the claims presented herein in any way. One or more of the arrangements may be widely applicable to numerous aspects, as may be readily apparent from the disclosure. In general, arrangements are described in sufficient detail to enable those skilled in the art to practice one or more of the aspects, and it should be appreciated that other arrangements may be utilized and that structural, logical, software, electrical and other changes may be made without departing from the scope of the particular aspects. Particular features of one or more of the aspects described herein may be described with reference to one or more particular aspects or figures that form a part of the present disclosure, and in which are shown, by way of illustration, specific arrangements of one or more of the aspects. It should be appreciated, however, that such features are not limited to usage in the one or more particular aspects or figures with reference to which they are described. The present disclosure is neither a literal description of all arrangements of one or more of the aspects nor a listing of features of one or more of the aspects that must be present in all arrangements. 
     Headings of sections provided in this patent application and the title of this patent application are for convenience only, and are not to be taken as limiting the disclosure in any way. 
     Devices that are in communication with each other need not be in continuous communication with each other, unless expressly specified otherwise. In addition, devices that are in communication with each other may communicate directly or indirectly through one or more communication means or intermediaries, logical or physical. 
     A description of an aspect with several components in communication with each other does not imply that all such components are required. To the contrary, a variety of optional components may be described to illustrate a wide variety of possible aspects and in order to more fully illustrate one or more aspects. Similarly, although process steps, method steps, algorithms or the like may be described in a sequential order, such processes, methods and algorithms may generally be configured to work in alternate orders, unless specifically stated to the contrary. In other words, any sequence or order of steps that may be described in this patent application does not, in and of itself, indicate a requirement that the steps be performed in that order. The steps of described processes may be performed in any order practical. Further, some steps may be performed simultaneously despite being described or implied as occurring non-simultaneously (e.g., because one step is described after the other step). Moreover, the illustration of a process by its depiction in a drawing does not imply that the illustrated process is exclusive of other variations and modifications thereto, does not imply that the illustrated process or any of its steps are necessary to one or more of the aspects, and does not imply that the illustrated process is preferred. Also, steps are generally described once per aspect, but this does not mean they must occur once, or that they may only occur once each time a process, method, or algorithm is carried out or executed. Some steps may be omitted in some aspects or some occurrences, or some steps may be executed more than once in a given aspect or occurrence. 
     When a single device or article is described herein, it will be readily apparent that more than one device or article may be used in place of a single device or article. Similarly, where more than one device or article is described herein, it will be readily apparent that a single device or article may be used in place of the more than one device or article. 
     The functionality or the features of a device may be alternatively embodied by one or more other devices that are not explicitly described as having such functionality or features. Thus, other aspects need not include the device itself. 
     Techniques and mechanisms described or referenced herein will sometimes be described in singular form for clarity. However, it should be appreciated that particular aspects may include multiple iterations of a technique or multiple instantiations of a mechanism unless noted otherwise. Process descriptions or blocks in figures should be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process. Alternate implementations are included within the scope of various aspects in which, for example, functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those having ordinary skill in the art. 
     Conceptual Architecture 
       FIG. 1  is a diagram of an exemplary architecture of a business operating system  100  according to an embodiment of the invention. Client access to system  105  for specific data entry, system control and for interaction with system output such as automated predictive decision making and planning and alternate pathway simulations, occurs through the system&#39;s distributed, extensible high bandwidth cloud interface  110  which uses a versatile, robust web application driven interface for both input and display of client-facing information and a data store  112  such as, but not limited to MONGODB™, COUCHDB™, CASSANDRA™ or REDIS™ depending on the embodiment. Much of the business data analyzed by the system both from sources within the confines of the client business, and from cloud based sources  107 , public or proprietary such as, but not limited to: subscribed business field specific data services, external remote sensors, subscribed satellite image and data feeds and web sites of interest to business operations both general and field specific, also enter the system through the cloud interface  110 , data being passed to the connector module  135  which may possess the API routines  135   a  needed to accept and convert the external data and then pass the normalized information to other analysis and transformation components of the system, the directed computational graph module  155 , high volume web crawler module  115 , multidimensional time series database  120  and a graph stack service  145 . Directed computational graph module  155  retrieves one or more streams of data from a plurality of sources, which includes, but is not limited to, a plurality of physical sensors, network service providers, web based questionnaires and surveys, monitoring of electronic infrastructure, crowd sourcing campaigns, and human input device information. Within directed computational graph module  155 , data may be split into two identical streams in a specialized pre-programmed data pipeline  155   a,  wherein one sub-stream may be sent for batch processing and storage while the other sub-stream may be reformatted for transformation pipeline analysis. The data may be then transferred to a general transformer service module  160  for linear data transformation as part of analysis or the decomposable transformer service module  150  for branching or iterative transformations that are part of analysis. Directed computational graph module  155  represents all data as directed graphs where the transformations are nodes and the result messages between transformations edges of the graph. High-volume web crawling module  115  may use multiple server hosted preprogrammed web spiders which, while autonomously configured, may be deployed within a web scraping framework  115   a  of which SCRAPY™ is an example, to identify and retrieve data of interest from web based sources that are not well tagged by conventional web crawling technology. Multiple dimension time series data store module  120  may receive streaming data from a large plurality of sensors that may be of several different types. Multiple dimension time series data store module  120  may also store any time series data encountered by system  100  such as, but not limited to, environmental factors at insured client infrastructure sites, component sensor readings and system logs of some or all insured client equipment, weather and catastrophic event reports for regions an insured client occupies, political communiques and/or news from regions hosting insured client infrastructure and network service information captures (such as, but not limited to, news, capital funding opportunities and financial feeds, and sales, market condition), and service related customer data. Multiple dimension time series data store module  120  may accommodate irregular and high-volume surges by dynamically allotting network bandwidth and server processing channels to process the incoming data. Inclusion of programming wrappers  120   a  for languages—examples of which may include, but are not limited to, C++, PERL, PYTHON, and ERLANG™—allows sophisticated programming logic to be added to default functions of multidimensional time series database  120  without intimate knowledge of the core programming, greatly extending breadth of function. Data retrieved by multidimensional time series database  120  and high-volume web crawling module  115  may be further analyzed and transformed into task-optimized results by directed computational graph  155  and associated general transformer service  160  and decomposable transformer service  150  modules. Alternately, data from the multidimensional time series database and high-volume web crawling modules may be sent, often with scripted cuing information determining important vertices  145   a,  to graph stack service module  145  which, employing standardized protocols for converting streams of information into graph representations of that data, for example open graph internet technology (although the invention is not reliant on any one standard). Through the steps, graph stack service module  145  represents data in graphical form influenced by any pre-determined scripted modifications  145   a  and stores it in a graph-based data store  145   b  such as GIRAPH™ or a key-value pair type data store REDIS™, or RIAK™, among others, any of which are suitable for storing graph-based information. 
     Results of the transformative analysis process may then be combined with further client directives, additional business rules and practices relevant to the analysis and situational information external to the data already available in automated planning service module  130 , which also runs powerful information theory-based predictive statistics functions and machine learning algorithms  130   a  to allow future trends and outcomes to be rapidly forecast based upon the current system derived results and choosing each a plurality of possible business decisions. Then, using all or most available data, automated planning service module  130  may propose business decisions most likely to result in favorable business outcomes with a usably high level of certainty. Closely related to the automated planning service module  130  in the use of system-derived results in conjunction with possible externally supplied additional information in the assistance of end user business decision making, action outcome simulation module  125  with a discrete event simulator programming module  125   a  coupled with an end user-facing observation and state estimation service  140 , which is highly scriptable  140   b  as circumstances require and has a game engine  140   a  to more realistically stage possible outcomes of business decisions under consideration, allows business decision makers to investigate the probable outcomes of choosing one pending course of action over another based upon analysis of the current available data. 
     A significant proportion of the data that is retrieved and transformed by the business operating system, both in real world analyses and as predictive simulations that build upon intelligent extrapolations of real world data, may include a geospatial component. The indexed global tile module  170  and its associated geo tile manager  170   a  may manage externally available, standardized geospatial tiles and may enable other components of the business operating system, through programming methods, to access and manipulate meta-information associated with geospatial tiles and stored by the system. The business operating system may manipulate this component over the time frame of an analysis and potentially beyond such that, in addition to other discriminators, the data is also tagged, or indexed, with their coordinates of origin on the globe. This may allow the system to better integrate and store analysis specific information with all available information within the same geographical region. Such ability makes possible not only another layer of transformative capability, but may greatly augment presentation of data by anchoring to geographic images including satellite imagery and superimposed maps both during presentation of real world data and simulation runs. 
       FIG. 2A  is a diagram of a multidimensional time series data module  120  as shown in  FIG. 1  further configured for use in various embodiments of the invention. Besides the programming wrapper  120   a  mentioned above, multidimensional time series data module  120  may include a node load monitor  220 , and an environment monitor  221 . Node load monitor  220  may continuously gather information on a particular node such as, without limitation, current player count on the present node, overall resources currently in use in the present node, and how various objects and actions are affecting the load on the present node. The information gathered by monitor  220  may be stored in memory for use in load prediction by other functions of business operating system  100 . 
     Environment monitor  221  may monitor the present environmental conditions within a node or system. This may include, for example, large-scale battles between alliances, in-game weather conditions, or some type of epidemic that may affect players. The data may be preserved as a snapshot at a specific time, and exported. The exported data may then be used as seed data for independent simulations. 
       FIG. 2B  is a diagram of an automated planning service module  130  as shown in  FIG. 1  further configured for use in various embodiments of the invention. Besides the information theory statistics engine  130   a,  automated planning service module  130  may also be configured to include functions for a load predictor  230 , a node operator  231 , and a game control function  232 . Load predictor  230  may use node load data as it is being gathered, or data that was previously gathered and stored by monitor  220 . 
     Node operator  231  may act on results generated by load predictor  230  to allocate resources, for example, by dynamically starting up and integrating a new node to accommodate a forecasted load influx, or shutting down a node as demand diminishes. 
     Game control  232  may be used by administrators of a game to seamlessly implement and propagate new rules or limitations within the game without interruption or downtime. Things that may be implemented include, but not limited by, change in environmental conditions, adding or removing questlines, or implementing incentives to influence the course of gameplay. Game control  232  may also implement rules without input from system administrators. For example, if system rules determine that a slight difficulty shift is needed, game control  232  may autonomously implement needed changes. 
       FIG. 2C  is a diagram of a connector module as shown in  FIG. 1  further configured for use in various embodiments of the invention. Besides messaging service  135   a,  connector module  135  may include a node communication module  235 . Node communication module  235  may provide functions for communication and exchanging of information between nodes, either within a system or outside of a system. 
       FIG. 3  is a diagram of the web application based interface  300  for an indexed global tile module as per one embodiment of the invention. User interaction with indexed global tile module  170  may be mediated through a web-based interface  310 . Web interface  310  may retrieve at least a portion of geospatial data not previously referenced from a plurality of remote, possibly cloud-based sources which include but are not limited to raster tile map sources  360 , interactive map libraries  370  and data and tile rendering services  380  which may resolve business decision data onto a geographical location or region. Other services known to those skilled in the field may exist and be used as needed. Direct interaction between the function of business operating system  100  and the capabilities of indexed global tile module  170  may pertain to both real world analyses and simulations run by the system. The web application therefore keeps galleries for simulations  311 , streaming analyses results  313  and historical analyses results  316  which may allow app users to retrieve these records from system data stores which may include multidimensional time series data store  120  for analyses involving series of repeated sensor readings or repeated updates of highly similar data, or other similar data series of short to moderate amounts of information; and other system associated data stores  318  for analyses of more extensive freeform text or similar data records. Once a simulation or real-world analysis is chosen, users may add simple geospatial information to them such as a unique geo-hash, calculated within geo-hash server  350  that is closely based upon the geographical coordinates where the data originated that then allows the simulation or real-world analysis to be correlated with other analyses within a pre-determined location radius based upon those geo-hashes which have increasing similarity based upon increased proximity. Addition of complex geospatial information, such as detailed indexed time-corrected geospatial tile images; or fully user annotated, labelled time-corrected geospatial tile series with map overlays for the length of the analysis, depending on the requirements of the study being carried out. To allow modification of running simulations, the web app may be capable of supplying the key for the correct, desired simulation and therefore keeps a repository of those keys  314 . A plurality of jobs run by the indexed global tile module require both a large amount of resources and time to complete and may therefore be run as batch jobs by a batch job module  312 . All output from the manipulation done by the web application may be mediated by an output module  315 . The web application may be hosted on a web server  320 . Indexed geospatial tiles are retrieved from at least one tile server  330  which may be local or cloud based. Geo-hashes may be calculated from specific tiles of interest by a geohash server  350 . Tiles and map overlays may be rasterized for output by a raster server  340 . 
       FIG. 4  is a diagram of an indexed global tile module  400  as per one embodiment of the invention. A significant amount of the data transformed and simulated by the business operating system has an important geospatial component. Indexed global tile module  170  allows both for the geo-tagging storage of data as retrieved by the system as a whole and for the manipulation and display of data using its geological data to augment the data&#39;s usefulness in transformation, for example creating ties between two independently acquired data points to more fully explain a phenomenon; or in the display of real world, or simulated results in their correct geospatial context for greatly increased visual comprehension and memorability. Indexed global tile module  170  may consist of a geospatial index information management module which retrieves indexed geospatial tiles from a cloud-based source  410 ,  420  known to those skilled in the art, and may also retrieve available geospatially indexed map overlays from a geospatially indexed map overlay source  430  known to those skilled in the art. Tiles and their overlays, once retrieved, represent large amounts of potentially reusable data and are therefore stored for a pre-determined amount of time to allow rapid recall during one or more analyses on a temporal staging module  450 . To be useful, it may be required that both the transformative modules of the business operating system, such as, but not limited to directed computational graph module  155 , automated planning service module  130 , action outcome simulation module  125 , and observational and state estimation service  140  be capable of both accessing and manipulating the retrieved tiles and overlays. A geospatial query processor interface  460  serves as a program interface between these system modules and geospatial index information management module  440  which fulfills the resource requests through specialized direct tile manipulation protocols, which for simplistic example may include “get tile xxx,” “zoom,” “rotate,” “crop,” “shape,” “stitch,” and “highlight” just to name a very few options known to those skilled in the field. During analysis, the geospatial index information management module may control the assignment of geospatial data and the running transforming functions to one or more swimlanes to expedite timely completion and correct storage of the resultant data with associated geotags. The transformed tiles with all associated transformation tagging may be stored in a geospatially tagged event data store  470  for future review. Alternatively, just the geotagged transformation data or geotagged tile views may be stored for future retrieval of the actual tile and review depending on the need and circumstance. There may also be occasions where time series data from specific geographical locations are stored in multidimensional time series data store  120  with geo-tags provided by geospatial index information management module  440 . 
       FIG. 5  is a flow diagram illustrating the function  500  of the indexed global tile module as per one embodiment of the invention. Predesignated, indexed geospatial tiles are retrieved from sources known to those skilled in the art at step  501 . Available map overlay data, retrieved from one of multiple sources known to those skilled in the art may be optionally retrieved per user design. The geospatial tiles may then be processed in one or more of a plurality of ways according to the design of the running analysis at step  502 , at which time geo-tagged event or sensor data may be associated with the indexed tile which may be directed programmatically or as a result of user interaction through web application  310 . Geo-tagging, either interactive or programmatic, may be applied to data at or near real time, applied to transformed data that has been previously archived to augment those data alone for direct presentation or further self-contained analysis, applied to augment the data so as to allow their inclusion in subsequent predictive analyses on the basis of their geographical location of occurrence in larger location based or regionally based analyses with either other archival or real-time data. Data relating to tile processing, which may include the tile itself is then stored for later review or analysis at step  507 . At step  508 , the geotagged may be utilized for practical applications, for instance, the data, in part, or in its entirety may be used in one or more transformations that are part of a real-world data presentation, part of in its entirety may be used in one or more transformations that are part of a simulation, and at least some of the geospatial data may be used in an analyst-determined direct visual presentation or may be formatted and transmitted for use in third party solutions. 
     Besides providing a means for the applications described above, global tile module  170  may also be used to serve data for a virtual environment, such as a virtual environment for a video game. Developers may create a virtual environment and implement the world to business operating system  100  and utilize global tile module  170 . The ability to easily add or remove geospatial tiles using global tile module  170  provides an ideal way for developers to scale their worlds to accommodate to their needs and requirements. Furthermore, for applications such as a massively multiplayer game, each geospatial tile may be configured to be its own node, which is described below in  FIG. 6A , and players within this online virtual world may move seamlessly from one tile to another. Each node may be configured to seamlessly split into a plurality of smaller nodes to provide a means for load balancing if a node should become overburdened. Additional nodes may also be implemented on a more permanent basis to provide easy scalability through the features of global tile module  170 , for example, if a developer wishes to add more areas to the virtual environment or a more permanent node capacity increase is required. 
       FIG. 6A  is a block diagram of an exemplary node  603  as used in various embodiments of the invention. Node  603  may be a physical system, a virtual node, or any other type of server commonly used in the art. The information stored within a node may include, but is not limited to, current node load  606 , player count  609 , contextual data  612 , load contributor data  615 , adjacent node data  618 , static game data  621 , and processed node load data  624 . Contextual data  612  may be information pertaining to the context of game&#39;s world including, but not limited to, ongoing in-game events, in-game alliances and rivalries, and progression of overarching storylines. 
     Load contributor data  615  may be information pertaining to a certain player or group of players, game events, or other environment factors and their effects and contributions to overall node load. This data may be used in conjunction with other data by load predictor  230  to more accurately forecast load influxes by taking into consideration the load contributed by highly variable factors. 
     Adjacent node data  618  may be data and statistics provided by adjacent nodes. This information may aid in the allocation of resources by allowing a particular node to be more spatially aware of what is occurring around it within the surrounding nodes. For example, if a large-scale battle as part of an in-game event is moving locations, adjacent nodes may be alerted to the movement. Load balancing measures may be preemptively enacted on the next node to provide a smooth experience for incoming players. 
     Processed node load data  624  may be historic node load data that has been processed by business operating system  100  for use in node load prediction. This data may be data gathered system-wide, or node-specific. 
       FIG. 6B  is an illustration of a segment  600  of an exemplary system of nodes as used in various embodiments of the invention. Segment  600  comprises a plurality of nodes  603   a  to  603   i.  Adjacent nodes are connected as indicated by dashed arrows. It should be understood that the shape and layout of the nodes in segment  600  are not indicative of the only layout possible of the invention. Layouts may vary, and nodes may take any practical shape or form. Adjacent nodes may not always be next to one another in some systems. Additionally, the illustrated number of nodes does not indicate any limitation of the present invention, and the specific number was chosen for simplicity of demonstration. With the mass-scalability of business operating system  100 , a system of nodes may potentially comprise an unlimited number of nodes of various shapes and sizes where physical resource may be the only limit, potentially capable of dealing with millions, if not billions, of players at a time. 
       FIG. 7  is a flow diagram illustrating a method  700  for gathering data for use in forecasting future loads according to various embodiments of the invention. At step  705 , node load data is observed and recorded by the system. Node load data may include, but is not limited to, current node load, player count, and load contributor data, all of which are described in greater detail in  FIG. 6A . At step  710 , the data may be processed, and cleansed for analysis by business operating system  100 . At step  715 , predictive analysis is done on the data previously analyzed in step  710  by business operating system  100  with automated planning service module  130 . At step  720 , results of the predictive analysis are stored by business operating system as reference data in forecasting node load by business operating system  100 . 
       FIG. 8  is a flow diagram illustrating a method  800  for predictive load balancing according to various embodiments of the invention. At step  805 , node load is monitored by the node load monitor. At step  810 , present node load patterns are evaluated against previously processed node load data using predictive algorithms of business operating system  100 . Some of the factors include, but not limited to, current player count, current game events, appearance of unusual contributors to overall node load, adjacent node loads, and the like. At step  815 , if a load spike that is beyond the current load-handling capabilities of a node, the flowchart goes to step  820 . At step  820 , more resources are allocated, and the load may be balanced to provide a smoother experience. This may be accomplished by either redistributing resources from adjacent nodes, or through the addition of more nodes. On the other hand, if a load influx is not forecasted, the flowchart goes to step  825 . At step  825 , the system proceeds as usual without resource reallocation. 
     After steps  820  or  825 , the process returns to step  805  and method  800  may be repeated. Method  800  may repeat as a background process, and may run as many times as is required while the system is active. 
     Hardware Architecture 
     Generally, the techniques disclosed herein may be implemented on hardware or a combination of software and hardware. For example, they may be implemented in an operating system kernel, in a separate user process, in a library package bound into network applications, on a specially constructed machine, on an application-specific integrated circuit (ASIC), or on a network interface card. 
     Software/hardware hybrid implementations of at least some of the aspects disclosed herein may be implemented on a programmable network-resident machine (which should be understood to include intermittently connected network-aware machines) selectively activated or reconfigured by a computer program stored in memory. Such network devices may have multiple network interfaces that may be configured or designed to utilize different types of network communication protocols. A general architecture for some of these machines may be described herein in order to illustrate one or more exemplary means by which a given unit of functionality may be implemented. According to specific aspects, at least some of the features or functionalities of the various aspects disclosed herein may be implemented on one or more general-purpose computers associated with one or more networks, such as for example an end-user computer system, a client computer, a network server or other server system, a mobile computing device (e.g., tablet computing device, mobile phone, smartphone, laptop, or other appropriate computing device), a consumer electronic device, a music player, or any other suitable electronic device, router, switch, or other suitable device, or any combination thereof. In at least some aspects, at least some of the features or functionalities of the various aspects disclosed herein may be implemented in one or more virtualized computing environments (e.g., network computing clouds, virtual machines hosted on one or more physical computing machines, or other appropriate virtual environments). 
     Referring now to  FIG. 9 , there is shown a block diagram depicting an exemplary computing device  10  suitable for implementing at least a portion of the features or functionalities disclosed herein. Computing device  10  may be, for example, any one of the computing machines listed in the previous paragraph, or indeed any other electronic device capable of executing software- or hardware-based instructions according to one or more programs stored in memory. Computing device  10  may be configured to communicate with a plurality of other computing devices, such as clients or servers, over communications networks such as a wide area network a metropolitan area network, a local area network, a wireless network, the Internet, or any other network, using known protocols for such communication, whether wireless or wired. 
     In one aspect, computing device  10  includes one or more central processing units (CPU)  12 , one or more interfaces  15 , and one or more busses  14  (such as a peripheral component interconnect (PCI) bus). When acting under the control of appropriate software or firmware, CPU  12  may be responsible for implementing specific functions associated with the functions of a specifically configured computing device or machine. For example, in at least one aspect, a computing device  10  may be configured or designed to function as a server system utilizing CPU  12 , local memory  11  and/or remote memory  16 , and interface(s)  15 . In at least one aspect, CPU  12  may be caused to perform one or more of the different types of functions and/or operations under the control of software modules or components, which for example, may include an operating system and any appropriate applications software, drivers, and the like. 
     CPU  12  may include one or more processors  13  such as, for example, a processor from one of the Intel, ARM, Qualcomm, and AMD families of microprocessors. In some aspects, processors  13  may include specially designed hardware such as application-specific integrated circuits (ASICs), electrically erasable programmable read-only memories (EEPROMs), field-programmable gate arrays (FPGAs), and so forth, for controlling operations of computing device  10 . In a particular aspect, a local memory  11  (such as non-volatile random access memory (RAM) and/or read-only memory (ROM), including for example one or more levels of cached memory) may also form part of CPU  12 . However, there are many different ways in which memory may be coupled to system  10 . Memory  11  may be used for a variety of purposes such as, for example, caching and/or storing data, programming instructions, and the like. It should be further appreciated that CPU  12  may be one of a variety of system-on-a-chip (SOC) type hardware that may include additional hardware such as memory or graphics processing chips, such as a QUALCOMM SNAPDRAGON™ or SAMSUNG EXYNOS™ CPU as are becoming increasingly common in the art, such as for use in mobile devices or integrated devices. 
     As used herein, the term “processor” is not limited merely to those integrated circuits referred to in the art as a processor, a mobile processor, or a microprocessor, but broadly refers to a microcontroller, a microcomputer, a programmable logic controller, an application-specific integrated circuit, and any other programmable circuit. 
     In one aspect, interfaces  15  are provided as network interface cards (NICs). Generally, NICs control the sending and receiving of data packets over a computer network; other types of interfaces  15  may for example support other peripherals used with computing device  10 . Among the interfaces that may be provided are Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, graphics interfaces, and the like. In addition, various types of interfaces may be provided such as, for example, universal serial bus (USB), Serial, Ethernet, FIREWIRE™, THUNDERBOLT™, PCI, parallel, radio frequency (RF), BLUETOOTH™, near-field communications (e.g., using near-field magnetics), 802.11 (WiFi), frame relay, TCP/IP, ISDN, fast Ethernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) or external SATA (ESATA) interfaces, high-definition multimedia interface (HDMI), digital visual interface (DVI), analog or digital audio interfaces, asynchronous transfer mode (ATM) interfaces, high-speed serial interface (HSSI) interfaces, Point of Sale (POS) interfaces, fiber data distributed interfaces (FDDIs), and the like. Generally, such interfaces  15  may include physical ports appropriate for communication with appropriate media. In some cases, they may also include an independent processor (such as a dedicated audio or video processor, as is common in the art for high-fidelity A/V hardware interfaces) and, in some instances, volatile and/or non-volatile memory (e.g., RAM). 
     Although the system shown in  FIG. 9  illustrates one specific architecture for a computing device  10  for implementing one or more of the aspects described herein, it is by no means the only device architecture on which at least a portion of the features and techniques described herein may be implemented. For example, architectures having one or any number of processors  13  may be used, and such processors  13  may be present in a single device or distributed among any number of devices. In one aspect, a single processor  13  handles communications as well as routing computations, while in other aspects a separate dedicated communications processor may be provided. In various aspects, different types of features or functionalities may be implemented in a system according to the aspect that includes a client device (such as a tablet device or smartphone running client software) and server systems (such as a server system described in more detail below). 
     Regardless of network device configuration, the system of an aspect may employ one or more memories or memory modules (such as, for example, remote memory block  16  and local memory  11 ) configured to store data, program instructions for the general-purpose network operations, or other information relating to the functionality of the aspects described herein (or any combinations of the above). Program instructions may control execution of or comprise an operating system and/or one or more applications, for example. Memory  16  or memories  11 ,  16  may also be configured to store data structures, configuration data, encryption data, historical system operations information, or any other specific or generic non-program information described herein. 
     Because such information and program instructions may be employed to implement one or more systems or methods described herein, at least some network device aspects may include nontransitory machine-readable storage media, which, for example, may be configured or designed to store program instructions, state information, and the like for performing various operations described herein. Examples of such nontransitory machine-readable storage media include, but are not limited to, magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM disks; magneto-optical media such as optical disks, and hardware devices that are specially configured to store and perform program instructions, such as read-only memory devices (ROM), flash memory (as is common in mobile devices and integrated systems), solid state drives (SSD) and “hybrid SSD” storage drives that may combine physical components of solid state and hard disk drives in a single hardware device (as are becoming increasingly common in the art with regard to personal computers), memristor memory, random access memory (RAM), and the like. It should be appreciated that such storage means may be integral and non-removable (such as RAM hardware modules that may be soldered onto a motherboard or otherwise integrated into an electronic device), or they may be removable such as swappable flash memory modules (such as “thumb drives” or other removable media designed for rapidly exchanging physical storage devices), “hot-swappable” hard disk drives or solid state drives, removable optical storage discs, or other such removable media, and that such integral and removable storage media may be utilized interchangeably. Examples of program instructions include both object code, such as may be produced by a compiler, machine code, such as may be produced by an assembler or a linker, byte code, such as may be generated by for example a JAVA™ compiler and may be executed using a Java virtual machine or equivalent, or files containing higher level code that may be executed by the computer using an interpreter (for example, scripts written in Python, Perl, Ruby, Groovy, or any other scripting language). 
     In some aspects, systems may be implemented on a standalone computing system. Referring now to  FIG. 10 , there is shown a block diagram depicting a typical exemplary architecture of one or more aspects or components thereof on a standalone computing system. Computing device  20  includes processors  21  that may run software that carry out one or more functions or applications of aspects, such as for example a client application  24 . Processors  21  may carry out computing instructions under control of an operating system  22  such as, for example, a version of MICROSOFT WINDOWS™ operating system, APPLE macOS™ or iOS™ operating systems, some variety of the Linux operating system, ANDROID™ operating system, or the like. In many cases, one or more shared services  23  may be operable in system  20 , and may be useful for providing common services to client applications  24 . Services  23  may for example be WINDOWS™ services, user-space common services in a Linux environment, or any other type of common service architecture used with operating system  21 . Input devices  28  may be of any type suitable for receiving user input, including for example a keyboard, touchscreen, microphone (for example, for voice input), mouse, touchpad, trackball, or any combination thereof. Output devices  27  may be of any type suitable for providing output to one or more users, whether remote or local to system  20 , and may include for example one or more screens for visual output, speakers, printers, or any combination thereof. Memory  25  may be random-access memory having any structure and architecture known in the art, for use by processors  21 , for example to run software. Storage devices  26  may be any magnetic, optical, mechanical, memristor, or electrical storage device for storage of data in digital form (such as those described above, referring to  FIG. 9 ). Examples of storage devices  26  include flash memory, magnetic hard drive, CD-ROM, and/or the like. 
     In some aspects, systems may be implemented on a distributed computing network, such as one having any number of clients and/or servers. Referring now to  FIG. 11 , there is shown a block diagram depicting an exemplary architecture  30  for implementing at least a portion of a system according to one aspect on a distributed computing network. According to the aspect, any number of clients  33  may be provided. Each client  33  may run software for implementing client-side portions of a system; clients may comprise a system  20  such as that illustrated in  FIG. 10 . In addition, any number of servers  32  may be provided for handling requests received from one or more clients  33 . Clients  33  and servers  32  may communicate with one another via one or more electronic networks  31 , which may be in various aspects any of the Internet, a wide area network, a mobile telephony network (such as CDMA or GSM cellular networks), a wireless network (such as WiFi, WiMAX, LTE, and so forth), or a local area network (or indeed any network topology known in the art; the aspect does not prefer any one network topology over any other). Networks  31  may be implemented using any known network protocols, including for example wired and/or wireless protocols. 
     In addition, in some aspects, servers  32  may call external services  37  when needed to obtain additional information, or to refer to additional data concerning a particular call. Communications with external services  37  may take place, for example, via one or more networks  31 . In various aspects, external services  37  may comprise web-enabled services or functionality related to or installed on the hardware device itself. For example, in one aspect where client applications  24  are implemented on a smartphone or other electronic device, client applications  24  may obtain information stored in a server system  32  in the cloud or on an external service  37  deployed on one or more of a particular enterprise&#39;s or user&#39;s premises. 
     In some aspects, clients  33  or servers  32  (or both) may make use of one or more specialized services or appliances that may be deployed locally or remotely across one or more networks  31 . For example, one or more databases  34  may be used or referred to by one or more aspects. It should be understood by one having ordinary skill in the art that databases  34  may be arranged in a wide variety of architectures and using a wide variety of data access and manipulation means. For example, in various aspects one or more databases  34  may comprise a relational database system using a structured query language (SQL), while others may comprise an alternative data storage technology such as those referred to in the art as “NoSQL” (for example, HADOOP CASSANDRA™, GOOGLE BIGTABLE™, and so forth). In some aspects, variant database architectures such as column-oriented databases, in-memory databases, clustered databases, distributed databases, or even flat file data repositories may be used according to the aspect. It will be appreciated by one having ordinary skill in the art that any combination of known or future database technologies may be used as appropriate, unless a specific database technology or a specific arrangement of components is specified for a particular aspect described herein. Moreover, it should be appreciated that the term “database” as used herein may refer to a physical database machine, a cluster of machines acting as a single database system, or a logical database within an overall database management system. Unless a specific meaning is specified for a given use of the term “database”, it should be construed to mean any of these senses of the word, all of which are understood as a plain meaning of the term “database” by those having ordinary skill in the art. 
     Similarly, some aspects may make use of one or more security systems  36  and configuration systems  35 . Security and configuration management are common information technology (IT) and web functions, and some amount of each are generally associated with any IT or web systems. It should be understood by one having ordinary skill in the art that any configuration or security subsystems known in the art now or in the future may be used in conjunction with aspects without limitation, unless a specific security  36  or configuration system  35  or approach is specifically required by the description of any specific aspect. 
       FIG. 12  shows an exemplary overview of a computer system  40  as may be used in any of the various locations throughout the system. It is exemplary of any computer that may execute code to process data. Various modifications and changes may be made to computer system  40  without departing from the broader scope of the system and method disclosed herein. Central processor unit (CPU)  41  is connected to bus  42 , to which bus is also connected memory  43 , nonvolatile memory  44 , display  47 , input/output (I/O) unit  48 , and network interface card (NIC)  53 . I/O unit  48  may, typically, be connected to keyboard  49 , pointing device  50 , hard disk  52 , and real-time clock  51 . NIC  53  connects to network  54 , which may be the Internet or a local network, which local network may or may not have connections to the Internet. Also shown as part of system  40  is power supply unit  45  connected, in this example, to a main alternating current (AC) supply  46 . Not shown are batteries that could be present, and many other devices and modifications that are well known but are not applicable to the specific novel functions of the current system and method disclosed herein. It should be appreciated that some or all components illustrated may be combined, such as in various integrated applications, for example Qualcomm or Samsung system-on-a-chip (SOC) devices, or whenever it may be appropriate to combine multiple capabilities or functions into a single hardware device (for instance, in mobile devices such as smartphones, video game consoles, in-vehicle computer systems such as navigation or multimedia systems in automobiles, or other integrated hardware devices). 
     In various aspects, functionality for implementing systems or methods of various aspects may be distributed among any number of client and/or server components. For example, various software modules may be implemented for performing various functions in connection with the system of any particular aspect, and such modules may be variously implemented to run on server and/or client components. 
     The skilled person will be aware of a range of possible modifications of the various aspects described above. Accordingly, the present invention is defined by the claims and their equivalents.