Patent Publication Number: US-11397620-B2

Title: Deployment of event-driven application in an IoT environment

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of priority to and is a continuation under 35 U.S.C. 111(a) of International Application No. PCT/US2019/060999, filed Nov. 12, 2019, and published as WO 2020/112349 A1 on Jun. 4, 2020, which claims the benefit of the filing date of U.S. Provisional Application Ser. No. 62/773,142, filed Nov. 29, 2018, the disclosures of each of which are incorporated by reference herein in their entireties. 
    
    
     BACKGROUND 
     Real-time, event-driven applications are taking center stage as the next generation of business applications, supporting the transition of businesses to become digital businesses. Next-generation planning, operations and customer engagement applications that provide optimal, personalized experiences depend on real-time sensing and near real-time decision making. Such applications must be built on a modern, event-driven application platform. 
     The term “application partitioning” refers to the process of developing applications that distribute the application logic among two or more computers in a network. In the simplest case, the application can run on a single PC, as a remote service, and send task requests for execution to a server. In more advanced cases, the application logic can be distributed among several servers. 
     Current application partitioning systems focus on portioning object-oriented applications distributed across a local area network. Such partitioning systems depend on users to identify specific object instances that are then manually placed on specific computational nodes. With the manual assignments in place, a partitioning system proceeds to allocate the remaining components to the partitions without consideration for the manual assignments and then binds the objects representing each node for access over the distributed communications system. 
     BRIEF SUMMARY 
     Example embodiments relate to a partitioning system that automatically allocates (or assigns) components of an event-driven computer application to computational nodes distributed throughout a computer network. Automatic allocation, which may include both analyses of component relationships and assignment of components, to reduce manual labor required to allocate the components of the application to the optimal computational node, reduce errors in assembling all required components for each node in the computer network and improve the efficiency of the partitioned application. 
     An example partitioning system includes a mechanism that expresses distributed computation in an intentional fashion. The partitioning system implements a set of source code analyzers that identify component relationships in the source code and apply a set of rules to the results of the analysis to determine both the optimal allocation of components to computational nodes and to determine the necessary and sufficient set of components required to support the execution of the components assigned to each computational node. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
       To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced. 
         FIG. 1  illustrates a Platform-as-a-Service (PaaS), in accordance with one embodiment. 
         FIG. 2  illustrates an architecture, in accordance with one embodiment. 
         FIG. 3  illustrates a deployment, in accordance with one embodiment. 
         FIG. 4  illustrates a deployment environment, in accordance with one embodiment. 
         FIG. 5  illustrates a deployment environment, in accordance with one embodiment. 
         FIG. 6  illustrates a deployment environment, in accordance with one embodiment. 
         FIG. 7  illustrates a distributed computing environment, in accordance with one embodiment. 
         FIG. 8  illustrates an operation, in accordance with one embodiment. 
         FIG. 9  illustrates a method, in accordance with one embodiment. 
         FIG. 10  illustrates an operation, in accordance with one embodiment. 
         FIG. 11  illustrates an operation, in accordance with one embodiment. 
         FIG. 12  illustrates an operation, in accordance with one embodiment. 
         FIG. 13  is a block diagram showing a software architecture within which the present disclosure may be implemented, in accordance with some example embodiments. 
         FIG. 14  is a diagrammatic representation of a machine in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein, in accordance with some example embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     “Carrier Signal” refers to any intangible medium that is capable of storing, encoding, or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible media to facilitate communication of such instructions. Instructions may be transmitted or received over a network using a transmission medium via a network interface. 
     “Communication Network” refers to one or more portions of a network that may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), the Internet, a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, a network or a portion of a network may include a wireless or cellular network and the coupling may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or other types of cellular or wireless coupling. In this example, the coupling may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standard, others defined by various standard-setting organizations, other long-range protocols, or other data transfer technology. 
     “Component” refers to a device, physical entity, or logic having boundaries defined by function or subroutine calls, branch points, APIs, or other technologies that provide for the partitioning or modularization of particular processing or control functions. Components may be combined via their interfaces with other components to carry out a machine process. A component may be a packaged functional hardware unit designed for use with other components and a part of a program that usually performs a particular function of related functions. Components may constitute either software components (e.g., code embodied on a machine-readable medium) or hardware components. A “hardware component” is a tangible unit capable of performing certain operations and may be configured or arranged in a certain physical manner. In various example embodiments, one or more computer systems (e.g., a standalone computer system, a client computer system, or a server computer system) or one or more hardware components of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion/component) as a hardware component that operates to perform certain operations as described herein. A hardware component may also be implemented mechanically, electronically, or any suitable combination thereof. For example, a hardware component may include dedicated circuitry or logic that is permanently configured to perform certain operations. A hardware component may be a special-purpose processor, such as a field-programmable gate array (FPGA) or an application specific integrated circuit (ASIC). A hardware component may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations. For example, a hardware component may include software executed by a general-purpose processor or other programmable processor. Once configured by such software, hardware components become specific machines (or specific components of a machine) uniquely tailored to perform the configured functions and are no longer general-purpose processors. It will be appreciated that the decision to implement a hardware component mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software), may be driven by cost and time considerations. Accordingly, the phrase “hardware component” (or “hardware-implemented component”) should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. Considering embodiments in which hardware components are temporarily configured (e.g., programmed), each of the hardware components need not be configured or instantiated at any one instance in time. For example, where a hardware component comprises a general-purpose processor configured by software to become a special-purpose processor, the general-purpose processor may be configured as respectively different special-purpose processors (e.g., comprising different hardware components) at different times. Software accordingly configures a particular processor or processors, for example, to constitute a particular hardware component at one instance of time and to constitute a different hardware component at a different instance of time. Hardware components can provide information to, and receive information from, other hardware components. Accordingly, the described hardware components may be regarded as being communicatively coupled. Where multiple hardware components exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) between or among two or more of the hardware components. In embodiments in which multiple hardware components are configured or instantiated at different times, communications between such hardware components may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware components have access. For example, one hardware component may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware component may then, at a later time, access the memory device to retrieve and process the stored output. Hardware components may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information). The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented components that operate to perform one or more operations or functions described herein. As used herein, “processor-implemented component” refers to a hardware component implemented using one or more processors. Similarly, the methods described herein may be at least partially processor-implemented, with a particular processor or processors being an example of hardware. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented components. Moreover, the one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., an API). The performance of certain of the operations may be distributed among the processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processors or processor-implemented components may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the processors or processor-implemented components may be distributed across a number of geographic locations. 
     “Computer-Readable Medium” refers to both machine-storage media and transmission media. Thus, the terms include both storage devices/media and carrier waves/modulated data signals. The terms “machine-readable medium,” “computer-readable medium” and “device-readable medium” mean the same thing and may be used interchangeably in this disclosure. 
     “Machine-Storage Medium” refers to a single or multiple storage devices and/or media (e.g., a centralized or distributed database, and/or associated caches and servers) that store executable instructions, routines and/or data. The term shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, including memory internal or external to processors. Specific examples of machine-storage media, computer-storage media and/or device-storage media include non-volatile memory, including by way of example semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), FPGA, 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 terms “machine-storage medium,” “device-storage medium,” “computer-storage medium” mean the same thing and may be used interchangeably in this disclosure. The terms “machine-storage media,” “computer-storage media,” and “device-storage media” specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term “signal medium.” 
     “Signal Medium” refers to any intangible medium that is capable of storing, encoding, or carrying the instructions for execution by a machine and includes digital or analog communications signals or other intangible media to facilitate communication of software or data. The term “signal medium” shall be taken to include any form of a modulated data signal, carrier wave, and so forth. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a matter as to encode information in the signal. The terms “transmission medium” and “signal medium” mean the same thing and may be used interchangeably in this disclosure. 
     “Mesh Network” refers to a network topology for a distributed computing environment in which the computational nodes connect, for example, directly, dynamically and non-hierarchically to as many other nodes as possible. The nodes may then cooperate to route network traffic from its source to its destination. Mesh networks may self-organize and self-configure dynamically. Mesh networks in which each node connects to all other peer nodes is known as a full mesh network. Mesh networks in which each node connects to a large but potentially selective set of peer nodes is known as a partial mesh. 
     Partitioning: 
     Technology and mechanisms, according to some example embodiments, to partition event-based applications are described herein. Specifically, a Platform-as-a-Service (PaaS)  100  is described, which includes a deployment manager that operates to identify node sets within a distributed computing environment, and then uses the identification of events, in event-based applications, to perform automated partitioning of such event-based applications. To this end, the deployment manager, and more specifically a partitioning system that forms part of the deployment manager, analyzes the source code of the event driven-application to infer relationships between components of the event-driven application. The partitioning system then applies a knowledge base of known relationships, via a rules system (e.g., assignment rules) to perform partitioning activity. The partitioning system then outputs configurations, reflecting the automated partitioning, as one or more configuration files. 
     The described embodiments provide a number of technical advantages over known solutions, in that the described embodiments do not require the a priori creation of object instances to drive the partitioning activity. Further, the described embodiments optimize the placement of components, whereas many current solutions operate to place all executable code on all computational nodes within a computing environment. Further, the described embodiments do not bind objects together because objects are simply not required, and bindings are dynamic as a function of the architectures of the event-based applications. 
     Event Broker: 
     According to some example embodiments, there is provided a mesh event broker, which distributes acquisition, augmentation and delivery of events across distributed nodes participating in one or more event ecosystems, such distributed nodes including event publishers, event subscribers and intermediaries. The mesh event broker, in some embodiments, seeks to eliminate a mediator as a single point of failure, distribute workload, to simplify and extend scaling, and to provide interconnection between event publishers and event subscribers within different networks. 
     According to some example embodiments, a collection of cooperating agents operates as a “mediator,” and form a mesh network supporting direct point-to-point communication. Each cooperating agent is provisioned to support augmentation, and to provide delivery of a specific set of publishers and subscribers to events published by supported publishers. Similarly, subscribers may be provisioned to augment and deliver events for which they are local subscribers. In addition, the cooperating agents, as a mesh, can forward messages across the mesh to improve event transmission efficiency, and to bridge between agents that are configured on different networks. 
     Event-Driven Applications 
     According to some example embodiments, event-driven applications may be deployed in a distributed manner for improved responsiveness, robustness, and security. As described herein, an event-driven application may be developed in a single cloud location and then automatically partitioned, resulting in the components of the application being distributed to the most optimal nodes for execution whether the nodes are cloud hosted, data center hosted, intelligent devices at the edge, or a combination thereof. Logic is located where it is the most effective. A wide range of system topologies including star, hierarchical, and peer-to-peer are supported. The provisioning and management of these networks are made automatic and managed by intelligent features of the Platform-as-a-Service (PaaS)  100  described herein. Application components can be dynamically changed anywhere in a distributed environment for one or tens of thousands of nodes while the system is running. 
     Described example embodiments also seek to automate the design, provisioning, and management of real-time, event-driven applications so that the development of the systems can focus on the business logic and not necessarily the underlying infrastructure. To this end, the Platform-as-a-Service (PaaS)  100  provides capabilities and integrations that seek to improve the speed and efficiency with which event-driven business applications can be constructed, deployed and operated. 
     An event-driven application, according to some example embodiments, may incorporate the following flow:
         Input is received from a number of sensors, for example over an extended period of time. Sensors may be, for example, physical sensors, data streams produced by other enterprise systems or public data streams.   The sensor data is analyzed to produce the events, consisting of information and context, on which automation, recommendation and collaboration decisions are made. Additional context may be extracted from other systems to augment the sensor data   The events are evaluated in real time to determine the actions that need to be taken. For example, discrete rules and/or machine learning strategies may be used to perform the real-time evaluation.   Actions are transmitted to the responsible systems for implementation, or human-machine collaboration is initiated with responsible personnel to determine the most appropriate response to the current situation.       

     In real-time event-driven business applications, processing may be performed local to the device under control, improving response time and reliability. For example, in an industrial setting, managing the position of a materials handling system requires near real-time responses within a few hundred milliseconds. Such response times cannot be guaranteed by a remote decision-making system that may be delayed by thousands of milliseconds if there is a network problem. Processing is done in a secure environment that carefully manages access to situational data and the ability to initiate control actions. 
     At a high level, an event-driven application, as described herein, may operate to perform operations including data acquisition, situational analysis and response action responsive to a detected situation. 
     Dealing firstly with data acquisition or sensing, an event-driven application may receive data from any one of a number of sensors. Sensors may include, for example:
         Mobile devices hosting sensor data including location, acceleration, audio, video and behavioral patterns derived from the raw sensor data.   Wearable devices such as watches, activity trackers, health monitors, audio and video headsets.   Machines including industrial machines, land and airborne transportation, home appliances and any mechanical or electronic equipment that can be sensed and/or controlled. For example, imagine a robot&#39;s manipulators instrumented with pressure sensors to vary the pressure applied to objects that may have different crush points.   Stand-alone sensors deployed in great numbers. For example, moisture sensors distributed across the fields of a farm to minimize water consumption while maximizing growth rates for the crops   Video and audio feeds that produce high volumes of what can be considered sensor data. Recognition software is used to determine what the video represents to translate the video into more discrete events on which automation decisions can depend.   Existing enterprise applications producing streams of transactions.       

     Such sensors can be connected directly to the Internet with their own IP communications stack or may be indirectly connected to the Internet via an edge node. In the latter case, the sensors themselves may communicate over more specialized protocols such as Modbus or ZigBee with the edge node providing protocol conversion so that the sensors appear as virtual nodes participating in the IoT. 
     Turning now to situational analysis, once data has been acquired, a real-time, event-driven application may be responsible for analyzing the data, and producing events or situations that represent business or technical conditions that require a response. An event-driven application may then initiate an automatic response to the current state of the machine or customer, and/or a collaboration between the appropriate operations personnel and the system, to produce the optimal response. 
     Events and situations may be detected by analyzing the data streams and their context using rules, statistical methods, and machine learning. Examples of events or situations that may be detected during analysis include, merely for example:
         Equipment that is not performing to expectations with conditions such as high temperature or low speed.   Customers that have arrived at an interesting location in a store or facility. For example, they are standing at a checkout kiosk or a specific merchandise display.   A user is in an unsafe area and needs help.   The distribution of orders has changed requiring the attention of product management.       

     Once a situation is detected, a response to the situation may be generated by an event-driven application. The response may be a response initiated autonomously by the automation system or a response determined via collaboration among the automation system and the responsible individuals. Responses may include:
         Providing relevant responses to consumers based on their current situation (e.g., items on sale, facility map, emergency response recommendations).   Respond intelligently to exceptional conditions (e.g., close a valve, turn on sprinklers, stop a malfunctioning robot).   Proactively alert personnel to opportunities/problems based on the current situation (e.g., extra delivery trucks available, shortage in part of the supply chain).   Optimize the user or business resources to improve productivity and/or customer satisfaction (e.g., speed up an assembly line, advise sports attendees on the shortest path to their car).       

     In response to a situation, an automated response may be taken directly by the real-time, event-driven business application or may be forwarded to a more specialized system for implementation. For example, an action to shut down a machine may be forwarded to the control system that directly manages the machine rather than having the application directly send a shutdown command to it. 
     For situations where the optimal response may be somewhat ambiguous or where determining the optimal response is beyond the capabilities of the system, a collaboration activity involving the system and the responsible individuals develops the optimal response. For example, the sensor readings may indicate there is a potential problem with a machine but not provide enough information to automatically decide to shut it down. Instead, the operations team collaborates with the system to review the current data and obtain further information, for example via a visual inspection of the machine, to determine if the situation warrants a shutdown of the equipment. 
     Some cases in which collaboration can produce optimal outcomes:
         Exception situations for which the data streams are inadequate to uniquely define the root cause and determine the best course of action.   Situations in which the operations team is privy to additional information not available to the system.   Situations in which a manual action must be taken on the part of a system that is not controllable online.   Situations in which policies or regulations demand more in-depth analysis of the situation before an action can be taken.       

     Another important class of collaborations notifies interested parties of actions taken and the resulting new state of the system. Notifications can be delivered to other automated systems so that they can independently respond to the situation, or delivered to responsible staff via desktop PCs, mobile devices, and wearable devices. Notifications can also include recommended actions and situational awareness of pending problems. 
     Real-time, event-driven business applications may be distributed. In manufacturing environments, for example, Programmable Logic Controllers (PLCs) communicate with area controllers and edge nodes that forward the data to more centralized IT systems. In consumer environments, data may be collected from numerous position sensors, processed locally into logical locations on which immediate automation decisions are made and forwarded to remote systems that optimize the experience for the consumer. Such a wide variety of distributed applications require support for an equally broad set of distributed topologies ranging from devices directly reporting to a central site, to hierarchically structured automation systems, to federated peers collaborating to improve a collection of organizations or businesses. 
     Simple architectures are sensors reporting to a central site. For example, a system collecting sensor data from a mobile phone and reporting that data to a cloud service represents an example of a centralized architecture. 
     More sophisticated architectures contain additional levels of processing and connectivity. Hierarchical systems are more complex and mimic many existing physical and organizational structures. For example, an industrial IoT system that consists of sensors reporting to local controllers that report to plant-wide controllers that report to divisional headquarters that report to corporate headquarters represents a tree topology. These systems provide both centralized and decentralized monitoring and control. Such systems are more responsive in real-time or near real-time situations. For example, it may be sub-optimal to control factory equipment in real time by collecting the data, transmitting the data to corporate HQ and having corporate HQ systems determine the next action for the machine. It may be more effective to do such an analysis on the local controller and simply report the situation and the action taken to the plant-wide controllers and, subsequently, to regional and corporate HQ. Faster response times, improved availability and local control make the distribution of the situational evaluation, collaborative decision making and response processing across the hierarchical topology more efficient than moving everything to HQ and making all decisions in a centralized fashion. 
     Another example of hierarchical real-time, event-driven business applications is the use of edge nodes to act as local processors for a collection of sensors and control points with the edge nodes then interacting with more centralized systems. 
     Examples of sophisticated distributed real-time, event-driven business applications are peer-to-peer systems, where peers are managed by separate organizations. For example, in an electrical demand-response system, the overall system consists of sensors managed by power utilities and sensors managed by utility customers while control of the system is distributed across the utility and its customers. To provide real-time demand-response, the utility system and the customer systems must collaborate. This is accomplished by each system making local decisions and transmitting both the local situation and the local decisions to the other party and then agreeing to modify their real-time behavior based on feedback from each other. 
     Platform-as-a-Service (Paas) 
       FIG. 1  is a block diagram illustrating the high-level functionality of a Platform-as-a-Service (PaaS)  100 , within which example embodiments of the described technology may be deployed. The Platform-as-a-Service (PaaS)  100  is designed and architected to support development, deployment, and operation of real-time business applications. Specifically, the Platform-as-a-Service (PaaS)  100  includes a developer portal  102 , using which developers develop event-driven application  104 , which are then deployed to distributed run-time nodes  106 . A system monitor  108  monitors operations of the event-driven application  104  on the distributed run-time nodes  106  and provides feedback to the developer portal  102  so as to enable a developer to evolve the event-driven application  104 . 
     The event-driven application  104  may be event-driven (e.g., act instantly on an event rather than storing data and performing the latest status checks). The Platform-as-a-Service (PaaS)  100  may furthermore be implemented on a Reactive framework, so as to support the real-time functionality by providing an asynchronous and non-blocking platform. Event streams in a highly distributed and large-scale environment (e.g., when receiving events from an Internet-of-Things (IoT) environment) provide technical motivation for a move away from a traditional three-tier architecture, to an event-based model. 
     The Platform-as-a-Service (PaaS)  100  further supports the design and runtime of event-driven application  104  serving up large numbers of events. To this end, the Platform-as-a-Service (PaaS)  100  enables a topology of a massive number of distributed run-time nodes  106  in a distributed environment. The distributed run-time nodes  106  may be peered horizontally in order to provide additional processing power. Where the volume of data collected (or events generated) exceeds limits for upload to a central processor, or with low latency is required, the distributed run-time nodes  106  may be arranged in a tree-structure in order to migrate processing close to the data at the edge of the topology. 
     Further, the distributed run-time nodes  106  may be clustered horizontally to ensure mission-critical availability. 
     While the event-driven application  104  provides the benefits of an event-based architecture, and Reactive programming, the developer portal  102  may require only an understanding of JavaScript and SQL through the provision of “low-code” development tools. The development tools support the visual declaration of components where productive, as well as high-level scripting for more complex elements of the event-driven application  104  not suited for visual development. Specifically, the developer portal  102  may provide visual editors for rules, types, sources, collaborations, topics and configurations; scripting editors for rules and procedures; and a domain-specific language (DSL) based on SQL and JavaScript to leverage existing skills. In addition, the developer portal  102  provides testing capabilities through a rule and procedure debugger, tracing and logging facilities, real-time subscription support and data visualization, synthetic data generators, and incremental deployment. Further, the developer portal  102  supports deployment through a distributed configuration (e.g., cloud, private cloud, on-premise, hybrid, and edge), and a visual deployment tool (e.g. the event binding tool  110  which allows event binding as described below). 
     Event-Driven Application 
       FIG. 2  is a block diagram, illustrating further details regarding an architecture  200  of an event-driven application  104  of the Platform-as-a-Service (PaaS)  100 , according to some example embodiments. 
     The Platform-as-a-Service (PaaS)  100  provides a platform support developing, deploying and operating high performance, distributed real-time, event-driven business applications (e.g., the event-driven application  104 ) consisting of: 
     1. Data Acquisition: Technologies for obtaining data from IoT and enterprise sources, filtering the data and making it available to an automation decision engine. 
     2. Event and Situational Analysis: A decision engine for analyzing the data in real-time and making decisions based on the results. 
     3. Action: Technologies for sending control information to devices and for notifying external systems and users of the decisions or recommendations for subsequent actions being made by the automation solution. Technologies for managing collaboration between the automation system and the responsible individuals to develop optimal responses to complex situations. 
     To this end,  FIG. 2  shows that the event-driven application  104  includes several adapters, including data adapters  202  and control adapters  204 . The event-driven application  104  also includes a number of rules, specifically data ingestion and enrichment rules  206 , situation identification rules  210 . and collaboration rules  212 . 
     The data ingestion and enrichment rules  206  are responsible for the ingesting and enrichment of data received by the data adapters  202 . The data adapters  202  and the data ingestion and enrichment rules  206  form part of a data acquisition subsystem and enable integration with several enterprise systems, public data sources, social data sources (e.g., messaging systems, or any system with a REST interface). The data ingestion and enrichment rules  206  are responsible for the ingesting and enrichment of data received by the data adapters  202 . 
     Broadly, the data acquisition subsystem acquires data from a wide array of data sources by using standard protocols such as, for example, REST, MQTT, and AMQP. The data sources may include, for example, IoT devices and enterprise systems that hold context required to evaluate the data flowing from sensors and placing of the sensor data in the proper context. For example, if an event-driven application is assisting a customer by tracking their location, access to information in a Customer Resource Management (CRM) system may be required to obtain the customer&#39;s profile information and to assess the opportunities to assist the user at their current location. This places a heavy emphasis on the integration of existing systems as part of the application. The Platform-as-a-Service (PaaS)  100  supplies a wide range of declarative integrations to facilitate the incorporation of existing enterprise systems into the real-time, event-driven business application. 
     The Platform-as-a-Service (PaaS)  100  may support:
         Both push and pull models   Synchronous and asynchronous models   RPC (Remote Procedure Call), as well as store and forward messaging systems   The source may elect to send data by matching documented specified formats or can choose to have the Platform-as-a-Service (PaaS)  100  accept the native source format and use a filtering system to convert it to the proper format for internal processing.       

     With these capabilities, the data acquisition subsystem makes source integration simple by matching the interaction model and message protocols of the source, rather than requiring the source to match messaging models of the Platform-as-a-Service (PaaS)  100 . 
     Further, the Platform-as-a-Service (PaaS)  100  supports a model for managing data hosted behind firewalls (e.g., a firewall  624  shown in  FIG. 6 ) that do not allow external systems to communicate directly with the data sources. 
     The flexible nature of the data acquisition subsystem allows such sources to provide data at their discretion rather than requiring the source to respond to an external request that cannot be delivered through a firewall (e.g., the firewall  624  of  FIG. 6 ). 
     Security may be maintained by requiring the Platform-as-a-Service (PaaS)  100  to use user-supplied credentials to access data in peer nodes. Thus, every node has complete control in determining which peer nodes are authorized to access the local node. 
     Event and situational identification and analysis is performed by the situation identification rules  210 . Specifically, the situation identification rules  210  may process streaming data in both simple and complex configurations:
         Data from multiple streams can be correlated to assist in situational analysis. The developer uses a simple domain-specific language derived from SQL to specify that an event detected in one stream must come before or after an event in another stream, or both events must happen within a specific timeframe with the events occurring in either order. Even in cases where events do not occur, a common error indicator can be specified in a simple fashion. Event constraints can be composed to any level making the specification of complex conditions simple. For example, an automation system may monitor two sensor streams for a mechanical device with the first stream reporting speed and the second reporting position. If the automation system sends a stop request to the device, it expects to see the speed of the device as read by the first sensor go to zero and the position of the device to remain unchanged once a speed reading of zero has been seen. If the position changes AFTER a speed of zero has been reported an alert is generated. Also, if a position is NOT reported within 30 seconds of a speed of zero being reported an alert is generated indicating a potential failure of the device control system.   Some of the streaming data is processed immediately or held only for a short time to facilitate time-series construction while other data may represent an extended time series or historical data that must be maintained over longer periods of time. The  100  simplifies the use of both transient and persistent data by unifying the abstractions used to represent series and set data in both its transient and persistent form.   Data is analyzed by discrete collections of rules or by algorithms produced by machine learning systems and subsequently integrated into the application.   A complete set of services is available to forward data to other nodes in a distributed topology using the SQL-based domain-specific language to easily support real-time processing throughout the distributed environment.       

     Automation and collaboration are supported by the control adapters  204  and the collaboration rules  212 . 
     The collaboration rules  212  are used to implement human-machine collaboration, between a human user and components of the Platform-as-a-Service (PaaS)  100 . The collaboration rules  212  seek to enable human users and machines within the Platform-as-a-Service (PaaS)  100  to work as independently or collaboratively as possible, depending on the situation, and to adjust to each other&#39;s requirements (e.g., the human user drives operations, while the system reacts, or system drives operations, while the user reacts). 
     Actions may be applied directly to the internal state of a system. Actions may be applied to external devices using source integrations (e.g., the control adapters  204 ) that deliver the actions to external devices or edge nodes using standard integrations such as REST, MQTT, AMQP and others, or custom integrations. 
     The Platform-as-a-Service (PaaS)  100  provides a model for creating actions or responses that involve collaborations between the application and its users. The collaboration model supports development of collaborations by composing high-level collaboration patterns using a graphical editor of the developer portal  102 . For example, the collaboration rules  212  may support a number of collaboration patterns including:
         Notification—handle notifications and responses via SMS, EMAIL, push notifications and messaging systems.   Assignment—negotiate assignments of users to tasks.   Location Tracking—significantly simplifies the task of knowing when a user reaches a specified destination, as well as their current location during their travels toward their destination.   Conversation—mediate a conversation among users over third-party messaging systems.   Escalation—respond to critical delays in completing tasks.       

     The Platform-as-a-Service (PaaS)  100  also supports mobile clients that can be used to easily integrate people into the overall collaborative decision-making process. The clients are designed to support natural and efficient interactions. Users are automatically notified of situations that need their attention, and custom interfaces for each notification supply the user with needed information. The user can respond by using data capture features of the mobile device—videos, photos, audio, location, acceleration, voice with natural language recognition, as well as traditional text entry. 
     While many systems force the distributed nature of an application to be explicitly programmed, configured and deployed, the Platform-as-a-Service (PaaS)  100  simplifies these operations by separating a logical definition of the application from its physical deployment. Using the developer portal  102 , developers may define applications as if they are to run on a single system, while application components are automatically provisioned to nodes using vail rules  214 . At runtime, the distributed run-time nodes  106  of the Platform-as-a-Service (PaaS)  100  operate together to act as a single real-time business application in a distributed computing environment  216 , with events related to that application being processed by an event broker  208 . 
     Topologies 
     The Platform-as-a-Service (PaaS)  100  supports a general model of distributed and federated topologies. A distributed application (e.g., event-driven application  104 ) may consist of two or more nodes, with each node representing an installation. An installation can contain a single service instance or a cluster of service instances. Installations are assembled into a distributed topology when an installation declares at least one “peer” node with which it desires to exchange messages. 
     Installations, by default, are considered independently managed. A node, A, declaring another node, B, as a peer must have credentials to access node B. Thus, the Platform-as-a-Service (PaaS)  100  is naturally federated since a node may only exchange messages with another node if it has been granted sufficient rights to perform the desired operation on the peer node. Peering is symmetric. If node B wishes to exchange messages with Node A, Node B must provision Node A as a peer and have sufficient rights to access node A. 
     Since the peering relationships can be defined between any two nodes, the Platform-as-a-Service (PaaS)  100  can support any distributed topology. Also, the topologies are implicitly federated since authentication and authorization are independently managed at each node. 
     Certain usage patterns may require (or favor) topologies in which all nodes in the distributed system are managed by a single authority. Such systems may be organized into star and tree topologies:
         Star—consists of a single parent node with an arbitrary number of child nodes.   Tree—consists of a root node with an arbitrary number of child nodes where each child node may act as a parent for an arbitrary number of child nodes.       

     As the deployed system becomes more collaborative, more general federated peer-to-peer networks may be constructed. In such a network topology, any node may peer with any other node leading to a general graph structure representing the connections among the nodes. The network model tends to be the most complex since cycles in the graph are possible and the cycles must be handled by any functions that operate on more than one node in the graph. 
     Also, because each node represents an independent system that may require separate credentials, the Platform-as-a-Service (PaaS)  100  naturally generalizes to federations among collaborating organizations. 
     Deployment 
       FIG. 3  is a diagrammatic representation showing a deployment  300 , according to one example embodiment, of an event-driven application  104 . Specifically, the Platform-as-a-Service (PaaS)  100  includes a deployment manager  304 , which operationally manages of the deployment of an event-driven application  104  to a target environment, such as the distributed computing environment  216 .  FIG. 3  illustrates the distributed computing environment  216  as consisting of a number of nodes that are reachable, either directly or indirectly from a node on which the deployment manager  304  is running. The physical nodes in the distributed computing environment  216  may be organized into node sets, where a node is a member of a particular node set based on having descriptive properties that satisfy criteria established for the node set. 
     Each node within the distributed computing environment  216  may be a computational resource associated with a particular device or component. The network of nodes within the distributed computing environment  216  may thus be used to implement an Internet of Things (IoT), in which case of the event-driven application  104  may comprise an IoT application. For example,  FIG. 3  shows that node  312 , node  308  and node  320  (which may constitute a particular set of nodes or a partition) each associated with a respective camera  324 , camera  328  and camera  326 . Similarly, node  316 , node  314  and node  310 , which again may constitute a particular partition together with a node  318 , are associated with a respective RFID tag  332 , RFID tag  336 , and RFID tag  334 . Node  322  is likewise associated with a microphone  330 . 
     The deployment manager  304  deploys configurations  338  of components (or artifacts) of an event-driven application  104  to specific nodes or node sets. A single configuration contains a manifest of components to deploy to a single node set. Each of the configurations  338  may define a corresponding partition, and a set of project artifacts (including components of an event-driven application  104 ) to be deployed to a specific partition. In one example, a partition logically represents a set of nodes onto which the project artifacts identified in a specific configuration will be deployed. A partition is defined by a constraint of a configuration on the attributes of the qualifying nodes, selected from the set of nodes within a target environment (e.g., the distributed computing environment  216 .) Configurations  338  may be contained within one or more projects (as described below with reference to  FIG. 4 ). A set of configurations  338  are defined within each respective project and define the set of partitions to which the artifacts of the project are to be deployed. 
     The deployment manager  304  is also used to define environments  340 , with each environment consisting of a list of nodes contained within a particular environment (e.g., the node  302 -node  322  within the distributed computing environment  216 ). When a project is deployed by the deployment manager  304  to an environment, each node in the environment is allocated to one or more partitions (e.g., a logical set of nodes). The project artifacts assigned to each partition are then deployed onto the nodes that are qualified members of the corresponding partition. It should also be noted that a set of nodes assigned to an environment (e.g., the distributed computing environment  216 ) may be a subset of the nodes defined within a namespace in which the deployment manager  304  is executing. 
       FIG. 3  also shows the deployment manager  304  as being associated with deployments  342 . Each of the deployments  342  defines a binding between a project and an environment defined in that project. A deployment action takes a particular deployment of the deployments  342  as its argument and deploys the associated project into the environment. 
     Deployment parameters may be used to customize project artifacts for deployment in a particular environment. For example, each parameter may identify an artifact, and a property of that artifact. During deployment, the value associated with the parameter replaces the default value of that property in the definition of the relevant artifact. 
     Deployment Manager 
       FIG. 4  is a block diagram showing further details of a deployment environment  400 , according to some example embodiments. The deployment environment  400  includes a deployment manager  304 , which operates to simplify development tasks for a developer, by focusing on the deployment of projects (e.g., project  404 , project  408  and project  412 ). 
     The deployment manager  304  performs a number of functions, including:
         Automatically creating default partitions and the assignment of development artifacts to each partition.   Automatically assigning of partitions to nodes defined in target environments  340 .   Enabling the user to customize configurations  338 , environments  340  and deployments  342 .   Deploying projects and visualizing the status of the deployment activities.   Via the CLI, making the deployment activities available to scripting and automation tools       

     The deployment manager  304  presents a developer with a graphical environment  402 , in which the developer can manage the configurations  338 , environments  340  (e.g., a data structure that defines a target environment in which to manage configurations), environments  340  (e.g., a data structure that defines a target environment in which configurations  338  of a project are deployed), and deployments  342  (e.g., a data structure that defines a binding between configurations  338  and environments  340  of a project, as well as deployment activities). A particular project (e.g., project  404 ) may be deployed to more than one environment (e.g., development environment  406 , test environment  410 , and production environment  414 ), thus satisfying a need to deploy to multiple such environment types. 
     Further details are now provided regarding each of configurations  338 , environments  340 , and deployments  342 . 
     Configurations 
     The configurations  338  contain the manifest of artifacts that are part of the configuration and the definition of the partition to which they are deployed. A configuration may define a single partition and the artifacts assigned to the partition. A project may contain one or more configurations with each configuration describing the artifacts deployed to a unique partition. An artifact may be a member of more than one configuration. A configuration may also contain other configurations in its manifest. In such cases, the child configuration may be deployed to the partition and then subsequently the child configuration is deployed using the deployment manager on the nodes assigned to the target partition. 
     Configurations  338  may contain only artifacts that are members of the containing project. 
     Artifacts that are included in configurations  338  and placed in partitions include:
         Rule   Source   Type   Procedure   Topic   Visual rule   Configuration   Client   RCS request   RCS payload   Collaboration type       

     Applications and collaborations are included in configurations  338  but are not partitioned because they are comprised of more primitive, partitionable rules and procedures. 
     Environments 
     Each of the environments  340  enumerates a set of nodes that are members of the environment. The nodes may be members of the project in which the environment is defined. Environments  340  describe project independent computing topologies, making it possible to deploy multiple projects to a single environment. Nodes in an environment definition are not required to be assigned to a partition, further improving the reusability of environments  340  across multiple configurations and projects. 
     Deployments 
     A deployment identifies a binding between a development project and an environment, with the intention of deploying the project to the environment. The result of a deployment operation applied to a deployment is the artifacts in the project&#39;s configurations deployed onto the nodes defined in the environment. 
     Deployment parameters make projects and their configurations portable across environments. A deployment parameter identifies an artifact, a property of that artifact and a value assigned to the property. At deployment time, each deployment parameter value is substituted into the identified artifact property replacing the default value that was originally configured for the artifact. This allows the artifacts to be bound to physical resources that may be unique to each environment. 
     Projects 
     Turning specifically to projects (e.g., project  404 , project  408 , or project  412 ), each project contains a subset of artifacts that are defined within a namespace. As such, a project represents a deployable unit of functionality, which may informally be denoted as an application or a service. Regarding distinctions between applications and services, an application may operatively accept an inbound event stream, regardless of whether such an event stream is produced by an external system (e.g., an MQTT queue) or produced by a user via a user interface. Services, on the other hand, respond to invocation requests delivered via a REST interface, or by being invoked directly by a script. Services may be considered “micro-services,” as they are independently deployed and transparently managed. As shown in  FIG. 4 , projects are also a unit of deployment, with the result of the deployment being an active application or service that executes in response to inbound requests. 
     Graphical Environment 
     As noted above, the deployment manager  304  presents developers with a graphical environment  402  (or visual editor) for visualizing and editing configurations  338  (using a configuration editor  418  component), environments  340  (using an environment editor  420  component), and deployments  342  (using a deployment editor  422  component). 
     The configuration editor  418  visually displays configurations  338 /partitions in a drawing panel with a rectangular area representing each configuration/partition. A configuration can be thought of as the declaration of a partition, so the use of either “configuration” or “partition” is equally valid, depending on whether the emphasis is on the declaration or the resulting partition. 
     The artifacts that are assigned to each partition are placed within the partition to which they are assigned by a partitioning system  416  that forms part of the deployment manager  304 . Within the configuration editor  418 , artifacts are represented by the icons used to represent the artifacts in the project&#39;s resource graph, with each artifact icon placed in the area representing the partition to which the artifact is assigned. Since an artifact may be assigned to multiple partitions (or configurations  338 ), the artifact may be represented multiple times within the visualization. 
     A developer may edit partition definitions by adding and/or removing artifacts from a configuration by re-assigning them to a different partition. Additional partitions can be created, and artifacts assigned to them. Artifact assignments are subject to correctness constraints enforced by the partitioning system  416 . The act of modifying configurations  338  invokes the partitioning system  416  to complete any reassignments required by the developer&#39;s actions. 
     The environment editor  420  visually displays the nodes that are members of an environment. The user may drill down to view the details of any node. Some environments  340  may contain a very large number of nodes. In such cases, all nodes are not necessarily enumerated on a diagram, but each class of nodes is represented by the constraints that define the members of the class of nodes. 
     These constraints are used by the developer to identify nodes that are defined in a namespace (e.g., an abstraction of a virtual environment) that should be members of a particular environment. An environment may have any number of constraints for identifying nodes. Nodes may also be assigned to the environment individually by the developer. 
     An environment may be edited by explicitly adding/removing nodes from the environment or by adding/removing/modifying a constraint that identifies a set of nodes to be included in the environment. If the developer is using constraints to specify membership, the environment editor  420  provides a mechanism for the developer to view the set of nodes identified by each constraint. 
     The nodes in the environment may be edited by drilling down into a node. Any changes to the properties of the node may cause the node to become a member of a different node class. 
     The deployment editor  422  visually displays the assignment of partitions to nodes or node classes based on the binding declared in the deployment. The deployment environment editor  420  is also capable of visualizing artifacts assigned to each partition and deployment parameters that modify the definition of each artifact within the deployment. A developer may edit the deployment parameters by selecting an artifact and changing the environment parameters bound to that artifact. If the artifact does not support environment parameters, the edit option will be disabled. 
     The developer may also view the status of the deployment on the visualization as each node visible on the diagram displays a status indicating whether the deployment is in progress, completed or has produced an error. The developer may drill down into indicated errors to diagnose the deployment problem. 
     Referring again specifically to  FIG. 4 , the deployment manager  304  may be used by a developer (or development team) to define a project for each target environment (which in turn comprises a collection of nodes). For example, the project  404  may be developed for the development environment  406 , the project  408  may be developed for test environment  410 , and the project  412  may be developed for the production environment  414 . Thus, the deployment manager  304  facilitates the deployment of multiple instances of an event-driven application to separate environments, while addressing the following challenge: the application may be bound to different physical resources in each of the target environments (e.g., the development environment  406 , the test environment  410 , and the production environment  414 ). For example, the target nodes may all belong to common logical partitions, but production nodes may be physically distinct from test nodes. Similarly, sources are bound to different physical resources in different environments.  FIG. 4  also shows that configurations  338 , in the form of respective configuration files, are deployed to the respective projects from the partitioning system  416 . 
       FIG. 5  and  FIG. 6  illustrate respective scenarios for the deployment of configuration files to target environments. Referring specifically to  FIG. 5 , in a deployment environment  500 , deployment manager  304 , operating on a node  502 , outputs two configuration files, namely configuration file  520  and configuration file  522 , via a network  524  to a target environment  504 . The configuration file  520  defines a first configuration (or partition) for a node set  506 , which includes node  510 -node  516 . The configuration file  522  defines a second configuration (or partition) for a node set  508 , which includes node  516  and node  518 . It will accordingly be appreciated that a particular node may be shared between multiple node sets, such as node  516  which is shared by the node set  506  and of the node set  508 . 
     Referring now to  FIG. 6 , a further deployment environment  600  is shown in which a particular parent configuration, represented by configuration file  618 , contains other child configurations (e.g., represented by configuration file  620 ) in its manifest.  FIG. 6  shows that the parent configuration file  618  is deployed from a first deployment manager  304 , executing on a node  602 , out to a target environment  606 , via a network  622 . The target environment  606  includes multiple node sets, including a node set  610  and a node set  614 . The parent configuration file  618  is used to instantiate a further child deployment manager  304  on the node  604  of the node set  614 . The child deployment manager  304  is then responsible for the deployment of the child configuration, represented by the child configuration file  620 , to a further target environment  608 , which includes further node sets, including node set  612  and node set  616 . 
       FIG. 7  is a diagrammatic representation showing further details regarding the partitioning system  416 , according to some example embodiments. Specifically, the partitioning system  416  is shown to include several source code analyzers  720 , which operationally analyze the source code of an event-driven application  104 , to infer relationships between the components of the event-driven application  104 , and further to discover remote references by such components. A remote reference identifies a set of nodes on which the processing occurs, and thus represents an abstract set of computing resources that satisfy the specified constraint. Further details regarding the source code analysis, as performed by the source code analyzers  720 , are discussed with reference to  FIG. 11 . 
     Considering the event-driven application  104 , such applications are executed in response to the reporting of an event within a distributed computing environment  216  in which the event-driven application  104  is deployed. Each such event may indicate the completion of an activity that is of interest to the event-driven application  104 . Events arrive at a particular event-driven application  104  from a variety of sources over a communications network  722  and may range, for example, from the reading of a value from a sensor within the distributed computing environment  216 , to the identification of a new strategic initiative by a user operator of the event-driven application  104 . 
       FIG. 7  shows that an event-driven application  104  may include a number of specialized components, with primary component classes including:
         event components  704 ;   source components  706 ;   rule components  708 ;   procedure components  710 ; and   type components  712 .       

     An event-driven application  104  may run within the distributed computing environment  216  such that each of the components of the event-driven application  104  is located on one or more computational nodes (e.g., node  714 , node  716 , or node  718 ) within the distributed computing environment  216 . 
     The distributed computing environment  216  is shown in  FIG. 7  to include a set of computational nodes in the form of node  714 -node  718 , with each node representing computing resources that can execute code, store data and communicate with other computational nodes over the communications network  722 . The computational nodes may be hosted in public clouds, private clouds, data centers or edge environments. Furthermore, a computational node has a unique address, using which other computational nodes may communicate with the relevant computational node. An event-driven application  104  executes in the distributed computing environment  216  by allocating components of the event-driven application  104  to one or more available computational nodes, such that execution proceeds through the collaborative actions of the participating computational nodes within the distributed computing environment  216 . 
       FIG. 8  is a diagrammatic representation of the rules system  800 , shown in  FIG. 7 , which operatively outputs configurations, in the form of configuration files  702 , according to which various components of the event-driven application  104  are deployed to computational nodes within the distributed computing environment  216 . Specifically, the rules system  800  includes rule sets that are applied to analyze the source code of the event-driven application  104 , and to determine node set assignments. The identification of node sets, as well as the assignment of components of an event-driven application  104  to these node sets, are described in further detail below with reference to  FIG. 11  and  FIG. 12 . 
     As shown in  FIG. 8 , the rules system  800  includes a number of rule sets to define abstract sets of computational resources, known as node sets, to which application components are assigned. The rules system  800 , and the relevant rule sets, apply assignment rules to the analyzed code to determine node set assignments. The rules system  800  also includes a schema for extending assignment rules to accommodate the specialized needs of specific event-based applications. 
     The rules system  800  includes three broad categories of rules, namely general rules  802 , component-type specific rules  806 , and custom rules  804 . The rules system  800  applies these three categories of rules to assign components of an event-driven application  104  to node sets. This is done by recursively applying the general rules  802 , which are broadly applicable to all component types, followed by the applying of the component-type specific rules  806 , which are applicable to single component types, and then followed by the applying of the custom rules  804 . 
     The component-type specific rules  806  are applicable to events  808 , rules  810 , types  812 , procedures  814 , and sources  816 . Further details regarding the application of these rules are discussed below, with reference to  FIG. 9 . 
       FIG. 9  is a flowchart illustrating a method  900 , according to some example embodiments, to partition, deploy and execute an event-driven application  104  within a distributed computing environment  216 . 
     In contrast to method  900 , certain methods and tools for the construction of a distributed, event-based application may require the manual assignment of components to computational nodes, via explicit programmer actions. Such assignment activity is labor-intensive, error-prone and, in many cases, results in a suboptimal allocation of application components to computational nodes. Certain efforts to automate such assignments have focused on identifying objects, which are then manually assigned to a specific computational node as the basis for distributed communication among the nodes, but with limited focus on the optimal allocation of additional components to each node. 
     The method  900 , in some example embodiments, may be deployed to precisely determine minimal code that can be assigned to each computational node of a distributed computing environment  216 . To this end, the method  900  may exploit a programming notation for specifying classes of computational nodes, on which a programming directive should be executed. 
     At a high-level, the method  900  includes a partitioning process  916  to automatically allocate components of an event-driven application  104  to node sets (and, by inference to computational nodes assigned to each node set), followed by a deployment process for the event-driven application  104  (operation  910 ), and an execution process for the event-driven application  104  (operation  912 ). The partitioning process  916  exploits a programming notation to specify a class of computational nodes on which a programming directive should be executed. 
     Programming Notation 
     The partitioning process  916 , as performed by the partitioning system  416 , is, in some example embodiments, based on the notion that components of an event-driven application  104  are allocated to nodes in a distributed computing environment  216  to ensure the correctness of the event-driven application  104 , and to optimize performance and availability of the event-driven application  104 . However, the assignment to specific nodes during an application development process is challenging, for the reason that developers may have only an abstract view of the ultimate topology of a target distributed computing environment  216  during application development. To address this technical challenge, the partitioning process  916  and partitioning system  416  define, in some example embodiments, an abstract model of a distributed computing technology by identifying node sets that represent one or more nodes that will exhibit properties associated with the particular node set. Consider the example where a developer knows that there will be computational resources (e.g., computational nodes) associated with refrigeration units in an IoT application, but a number of such nodes, their locations and identities remain unknown until late in the deployment process (operation  910 ), and until well after the allocation of components of the event-driven application  104  to computing resources is complete. The partitioning system  416  and partitioning process  916 , according to some example embodiments, abstract these assignments by supporting a declarative model, in which the developer specifies references to components by specifying a logical constraint that the computing resource must meet. The logical constraint is subsequently formalized as a node-set, which may contain one or more nodes in a final deployment topology. Returning to the example of the refrigeration units, the computing resources associated with the refrigeration units may be specified, for example, by the processing constraint as follows: 
     PROCESSED BY ManagedEquipment==“refrigeration” 
     Application Analysis 
     Referring specifically to the partitioning process  916 , at operation  1000 , the source code analyzers  720  analyze the event-driven application  104 , in preparation for node set of discovery and component assignment activities. 
       FIG. 10  is a flowchart illustrating further sub-operations or the operation  1000 , performed by the source code analyzers  720 , to analyze application source code. Specifically, operation  1000  commences with operation  1002 , where the source code analyzers  720  access the source code of a particular event-driven application  104 . 
     At operation  1004 , the source code analyzers  720 , identify statements, within the application source code, containing remote references, whereafter, at operation  1006 , the source code analyzers  720  identify components being referenced in such statements. 
     At operation  1008 , the source code analyzers  720  determine logical computing resource constraints on the class of computing resources needed to host the referenced component. 
     For example, the statement: EXECUTE PROCEDURE checkRefrigerationSettings (“temperature”) PROCESSED BY ManagedEquipment==“refrigeration” will cause the procedure checkRefrigerationSettings to be partitioned to the node set that represents “ManagedEquipment”. Another example might be the statement: SELECT * FROM Person WHERE age&gt;21 PROCESSED BY department==“HR” will cause the type “Person” to be partitioned to all nodes in a node set that supports HR locations. This implies the data will be distributed across all “HR” nodes. Subsequently, a query such as: SELECT * FROM Person PROCESSED BY department==“HR” will run a distributed query against all Person types in all nodes in the node-set that supports HR locations to construct the complete result set. 
     At decision operation  1010 , the source code analyzers  720  determine whether there are further statements, within the application source code, containing remote references. If so, the operation  1000  loops back to operation  1004 . On the other hand, should it be determined at decision operation  1010  that no further statements containing remote references to process, the operation  1000  progresses to operation  1012 , wherein the source code analyzers  720  operate to identify dependencies among components of the event-driven application. 
     For example, a rule, R, that references a procedure, P, and the reference is not a remote reference causes the procedure P to be partitioned into the same node-sets as rule R, so that the dependency of R on P can be satisfied by a local reference within the node-set. In turn, procedure P might reference type, T, in a statement that does not include a remote reference. This causes the type T to be partitioned into the same node-sets as R and P. 
     At operation  1014 , the source code analyzers  720  then generates analysis metadata, which is used to drive further operations of the partitioning process  916 . 
     Identifying Node Sets (Operation  1100 ) 
     Returning to  FIG. 9 , at operation  1100 , the source code analyzers  720  of the partitioning system  416  proceeds to identify node sets. 
       FIG. 11  is a flowchart illustrating further sub steps of the operation  1100 , as may be performed by the source code analyzers  720 . The operation  1100  commences at operation  1102 , with the assignment of a default node set to hold assignments for all unqualified components of the event-driven application. 
     At operation  1104 , the source code analyzers  720  identify and inspect each logical computing resource in the application source code, and perform two determinations at decision operation  1106  and decision operation  1112 . 
     Specifically, at decision operation  1106 , for a specific logical computing resource, a determination is made as to whether that specific logical computing resource constraint has been previously processed by the source code analyzers  720 . Following a negative determination at decision operation  1106 , at operation  1108 , the source code analyzers  720  created a new node set, which is then assigned to the specific logical computing resource constraint. 
     At operation  1110 , the source code analyzers  720  record a dependency of the referenced event-driven application components to a logical computing resource constraint. In other words, once a new logical constraint (that will map to a node set) has been identified, the source code analyzers  720  operate to record the list of event-driven application components that are referenced via that logical constraint (as these event-driven application components will be partitioned onto the corresponding node set). At decision operation  1112 , the source code analyzers  720  assess whether there are any further logical computing resource constraints, in the application source code, that require analysis. If so, the operation  1100  loops back to decision operation  1106 . On the other hand, if not, the operation  1100  terminates at done operation  1114 . 
     Assigning Components to Node Sets (Operation  902 ) 
     Returning to  FIG. 9 , at operation  902 , the rules system  800  assigns components of the event-driven application to node sets identified at operation  1100 . Specifically, the rules system  800  performs the assignment of these components by recursively applying a set of general rules  802  applicable to all component types at operation  1200 , followed by applying a set of more specialized rules applicable to only a single component type, namely component-type specific rules  806 , at operation  904 . Finally, a set of custom rules  804  is applied by the rules system  800  at operation  914 . These custom rules  804  may be for specific classes of applications and applied to more optimally assigned components to node sets for such specific classes of applications. For example, a programmer may wish to assign all power management components of an event-driven application to node sets that manage equipment that has high power consumption (e.g., refrigeration units). The custom rules  804  support incorporation of such additional partitioning semantics. 
       FIG. 12  is a flowchart illustrating further substeps of the operation  1200 , namely the application of the set of general rules  802  which are applicable to all component types, to assign components of the event-driven application to node sets. 
     Operation  1200  commences at operation  1202 , with the identification of an unassigned component, following which a determination is made, by the rules system  800  at decision operation  1204 , regarding whether the identified component includes a notational declaration (e.g., a“PROCESSED BY&lt;resource&gt;” clause) of a logical computing resource constraint associated with a specific node set (e.g., a node set created by the relevant logical computing resource constraint). 
     Following a positive determination at decision operation  1204 , the rules system  800  proceeds to assign the relevant component to the node set created for that logical computing resource constraint at operation  1206 . Upon completion of operation  1206 , or following a negative determination at decision operation  1204 , the operation  1200  progresses to decision operation  1208 . At decision operation  1208 , a determination is made as to whether the component (without a notational declaration of a logical computing resource) is referenced by other components that do include such notational declarations. If so, the relevant component is assigned to the same node set as the referencing component at operation  1210 . Operation  1210  is performed recursively until all artifacts have been inspected. Accordingly, a component may be assigned all configurations that contain a reference to that component. 
     A component that remains unassigned following a negative determination at decision operation  1208  is then, at operation  1212 , assigned to a default, unconstrained node set. For each component assigned to this default, unconstrained node set, a recursive traversal of all artifacts that it references is further performed at operation  1212 , and any artifact referenced without a notational declaration (e.g., a “PROCESSED BY” clause) is also partitioned onto the default node set. 
     Returning to the application of the component-type specific rules  806  at operation  904 , each of these rule types is considered separately below. 
     Events  808  An event is partitioned to the node set on which it is produced. More specifically, events may be produced by types, rules, procedures, sources and external requests. 
     If no resource notational declaration (e.g., a “PROCESSED BY” clause) is specified in a process (e.g., publish) request, the event is partitioned by the rules system  800  onto the same node set as the artifact that produces the event. On the other hand, if a resource declaration (e.g., a “PROCESSED BY” clause) is specified in a process (e.g., publish) request, the event is partitioned onto the node set specified by the resource declaration. 
     A process (e.g., publish) request initiated as an external request is partitioned to the default node set since no information as to the destination of the request is known to the rules system  800 . Similarly, user-defined events are partitioned to the default node set if no other information about the event is known. Other information might be a reference to the event in a process request (e.g., a PUBLISH statement). Such a reference causes the full set of event partitioning rules described above to be evaluated against the event. The converse is also true; if an event is partitioned onto a node set, the component that produces the event is also partitioned to the same node set. 
     Rules  810 : rules  810  are partitioned to the same node set on which the triggering event is produced. The converse is also true; if a rule is partitioned onto a node set, the event that triggers the rule is also partitioned to the same node set and, by implication, the artifact producing the event is partitioned to the same node set. 
     Types  812 : types  812  referenced by a component are placed on the same node set as the artifact. A type may be referenced by components on different node sets and is provisioned on all node sets that contain a component that references the type. If a component references a type using a resource declaration (e.g., a “PROCESSED BY” clause), the type is provisioned onto the node set defined by the resource declaration (e.g., a “PROCESSED BY” clause). 
     If a component references a type without a resource declaration, the type is partitioned onto the same node set as the referencing component. 
     Procedures  814 : procedures  814  are partitioned to all node sets that contain a component that references the procedure. It should be noted that there may be no information available on procedures invoked via an external interface. Procedures that have no known references and that are, therefore, likely to be invoked via the external interface, are partitioned to the default node set. 
     A procedure that is referenced by a component and is invoked via the external interface may be partitioned manually to the node set that services external requests. 
     Sources  816 : sources  816  are partitioned to the default node set by default. If the event produced by a source triggers a rule that has been explicitly partitioned, the source is placed in the same node set as the triggered rule. Sources  816  may be partitioned manually by a developer that knows the best place to obtain the data. This manual placement then drives automatic partitioning using the manual placement as fixed. 
     Having completed the assignment of components to node sets at operation  902 , the method  900  progresses to operation  906 , where manual overrides may be performed. Specifically, once the rules described above have been applied recursively to all components of an event-driven application, partitioning by the partitioning system  416  is complete and the event-driven application is ready for deployment. At this point, programmers or deployment managers may view a visualization of the partitioned application, this visualization displaying each of the components and the node sets to which the components have been assigned. A programmer then manually modifies the partition definitions, for example using a drag and drop interface. When a component is manually allocated to a node set, the partitioning rules described above may be evaluated to assure that the partitioning satisfies the constraints embodied in the rules system  800  Any changes to the allocation of components to node sets required to satisfy the constraints of the rules system  800  are then automatically performed by the rules system  800 . 
     Having then completed operation  906 , the method  900  progresses to terminate the partitioning processes at operation  912 . Thereafter, the event-driven application is ready for deployment at operation  914 , and subsequent execution at  916 . 
       FIG. 13  is a block diagram  1300  illustrating a software architecture  1304 , which can be installed on any one or more of the devices described herein. The software architecture  1304  is supported by hardware such as a machine  1302  that includes processors  1320 , memory  1326 , and I/O components  1338 . In this example, the software architecture  1304  can be conceptualized as a stack of layers, where each layer provides a particular functionality. The software architecture  1304  includes layers such as an operating system  1312 , libraries  1310 , frameworks  1308 , and applications  1306 . Operationally, the applications  1306  invoke API calls  1350  through the software stack and receive messages  1352  in response to the API calls  1350 . 
     The operating system  1312  manages hardware resources and provides common services. The operating system  1312  includes, for example, a kernel  1314 , services  1316 , and drivers  1322 . The kernel  1314  acts as an abstraction layer between the hardware and the other software layers. For example, the kernel  1314  provides memory management, processor management (e.g., scheduling), component management, networking, and security settings, among other functionality. The services  1316  can provide other common services for the other software layers. The drivers  1322  are responsible for controlling or interfacing with the underlying hardware. For instance, the drivers  1322  can include display drivers, camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flash memory drivers, serial communication drivers (e.g., Universal Serial Bus (USB) drivers), WI-FI® drivers, audio drivers, power management drivers, and so forth. 
     The libraries  1310  provide a low-level common infrastructure used by the applications  1306 . The libraries  1310  can include system libraries  1318  (e.g., C standard library) that provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, the libraries  1310  can include API libraries  1324  such as media libraries (e.g., libraries to support presentation and manipulation of various media formats such as Moving Picture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC), Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC), Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group (JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries (e.g., an OpenGL framework used to render in two dimensions (2D) and three dimensions (3D) in a graphic content on a display), database libraries (e.g., SQLite to provide various relational database functions), web libraries (e.g., WebKit to provide web browsing functionality), and the like. The libraries  1310  can also include a wide variety of other libraries  1328  to provide many other APIs to the applications  1306 . 
     The frameworks  1308  provide a high-level common infrastructure that is used by the applications  1306 . For example, the frameworks  1308  provide various graphical user interface (GUI) functions, high-level resource management, and high-level location services. The frameworks  1308  can provide a broad spectrum of other APIs that can be used by the applications  1306 , some of which may be specific to a particular operating system or platform. 
     In an example embodiment, the applications  1306  may include a home application  1336 , a contacts application  1330 , a browser application  1332 , a book reader application  1334 , a location application  1342 , a media application  1344 , a messaging application  1346 , a game application  1348 , and a broad assortment of other applications such as a third-party application  1340 . The applications  1306  are programs that execute functions defined in the programs. Various programming languages can be employed to create one or more of the applications  1306 , structured in a variety of manners, such as object-oriented programming languages (e.g., Objective-C, Java, or C++) or procedural programming languages (e.g., C or assembly language). In a specific example, the third-party application  1340  (e.g., an application developed using the ANDROID™ or IOS™ software development kit (SDK) by an entity other than the vendor of the particular platform) may be mobile software running on a mobile operating system such as IOS™, ANDROID™, WINDOWS® Phone, or another mobile operating system. In this example, the third-party application  1340  can invoke the API calls  1350  provided by the operating system  1312  to facilitate functionality described herein. 
       FIG. 14  is a diagrammatic representation of the machine  1400  within which instructions  1408  (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine  1400  to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions  1408  may cause the machine  1400  to execute any one or more of the methods described herein. The instructions  1408  transform the general, non-programmed machine  1400  into a particular machine  1400  programmed to carry out the described and illustrated functions in the manner described. The machine  1400  may operate as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine  1400  may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine  1400  may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a PDA, an entertainment media system, a cellular telephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions  1408 , sequentially or otherwise, that specify actions to be taken by the machine  1400 . Further, while only a single machine  1400  is illustrated, the term “machine” shall also be taken to include a collection of machines that individually or jointly execute the instructions  1408  to perform any one or more of the methodologies discussed herein. 
     The machine  1400  may include processors  1402 , memory  1404 , and I/O components  1442 , which may be configured to communicate with each other via a bus  1444 . In an example embodiment, the processors  1402  (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) processor, a Complex Instruction Set Computing (CISC) processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an ASIC, a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor  1406  and a processor  1410  that execute the instructions  1408 . The term “processor” is intended to include multi-core processors that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. Although  FIG. 14  shows multiple processors  1402 , the machine  1400  may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof. 
     The memory  1404  includes a main memory  1412 , a static memory  1414 , and a storage unit  1416 , both accessible to the processors  1402  via the bus  1444 . The main memory  1404 , the static memory  1414 , and storage unit  1416  store the instructions  1408  embodying any one or more of the methodologies or functions described herein. The instructions  1408  may also reside, completely or partially, within the main memory  1412 , within the static memory  1414 , within machine-readable medium  1418  within the storage unit  1416 , within at least one of the processors  1402  (e.g., within the processor&#39;s cache memory), or any suitable combination thereof, during execution thereof by the machine  1400 . 
     The I/O components  1442  may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components  1442  that are included in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones may include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components  1442  may include many other components that are not shown in  FIG. 14 . In various example embodiments, the I/O components  1442  may include output components  1428  and input components  1430 . The output components  1428  may include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The input components  1430  may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point-based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or another pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and/or force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like. 
     In further example embodiments, the I/O components  1442  may include biometric components  1432 , motion components  1434 , environmental components  1436 , or position components  1438 , among a wide array of other components. For example, the biometric components  1432  include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram-based identification), and the like. The motion components  1434  include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. The environmental components  1436  include, for example, illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometers that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensors (e.g., gas detection sensors to detection concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment. The position components  1438  include location sensor components (e.g., a GPS receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like. 
     Communication may be implemented using a wide variety of technologies. The I/O components  1442  further include communication components  1440  operable to couple the machine  1400  to a network  1420  or devices  1422  via a coupling  1424  and a coupling  1426 , respectively. For example, the communication components  1440  may include a network interface component or another suitable device to interface with the network  1420 . In further examples, the communication components  1440  may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices  1422  may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB). 
     Moreover, the communication components  1440  may detect identifiers or include components operable to detect identifiers. For example, the communication components  1440  may include Radio Frequency Identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components  1440 , such as location via Internet Protocol (IP) geolocation, location via Wi-Fi® signal triangulation, location via detecting an NFC beacon signal that may indicate a particular location, and so forth. 
     The various memories (e.g., memory  1404 , main memory  1412 , static memory  1414 , and/or memory of the processors  1402 ) and/or storage unit  1416  may store one or more sets of instructions and data structures (e.g., software) embodying or used by any one or more of the methodologies or functions described herein. These instructions (e.g., the instructions  1408 ), when executed by processors  1402 , cause various operations to implement the disclosed embodiments. 
     The instructions  1408  may be transmitted or received over the network  1420 , using a transmission medium, via a network interface device (e.g., a network interface component included in the communication components  1440 ) and using any one of a number of well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions  1408  may be transmitted or received using a transmission medium via the coupling  1426  (e.g., a peer-to-peer coupling) to the devices  1422 . 
     Statements 
     1. A method to deploy a plurality of event-driven application components of an event-driven application in a distributed computing environment, the method comprising:
         automatically analyzing application source code of the event-driven application, using one or more processors, to identify relationships between the plurality of event-driven application components; and   applying, using the one or more processors, a set of rules to:   based on the automatic analysis, generate assignment data recording assignments of event-driven application components to a plurality of computational nodes in the distributed computing environment; and   determine component requirements for each of the plurality of event-driven application components required to support execution at an assigned computational node in the distributed computing environment.       

     2. The method according to any one or more of the preceding claims, including assigning each of the plurality of computational nodes to a respective node set of a plurality of node sets. 
     3. The method according to any one or more of the preceding claims, wherein the generating of the assignment data comprises assigning a common set of event-application components to each node of a respective node set of the plurality of node sets. 
     4. The method according to any one or more of the preceding claims, wherein the set of rules is applied to assign each of the plurality of event-driven application components to an optimal node set of the plurality of node sets that is sufficient to provide proper operation of the event-driven application. 
     5. The method according to any one or more of the preceding claims, wherein the optimal node set to which a respective event-driven application component is assigned is identified based on a determination of minimal code that may be assigned to the respective event-driven application component. 
     6. The method according to any one or more of the preceding claims, wherein the automatic analysis of the application source code includes identifying a notational declaration of a remote reference by each of the event-driven application components of the plurality of event-driven application components. 
     7. The method according to any one or more of the preceding claims wherein the notational declaration of the remote reference comprises a declaration of a logical computing resource constraint of the remote reference. 
     8. The method according to any one or more of the preceding claims, wherein the analyzing of the application source code includes analyzing the notational declaration to identify a class of computational nodes on which a programming directive, specified by a specific event-driven application component, is to be executed. 
     9. The method according to any one or more of the preceding claim  1 , wherein the analyzing of the application source code includes determining a resource that operationally triggers execution of a specific event-driven application component. 
     10. The method according to any one or more of the preceding claims, wherein the analyzing of application source codes includes determining a resource referenced by a specific event-driven application component 
     11. The method according to any one or more of the preceding claims, wherein analyzing the application source code includes identifying a dependency of an identified code segment of a specific event-driven application component. 
     12. The method according to any one or more of the preceding claims wherein the automatic analysis of the application source code includes generating analysis metadata that is used to generate the assignment data. 
     13. A computing apparatus, the computing apparatus comprising:
         a processor; and   a memory storing instructions that, when executed by the processor, configure the apparatus to:   automatically analyze application source code of an event-driven application, using one or more processors, to identify relationships between a plurality of event-driven application components; and   apply, using the one or more processors, a set of rules to:
           based on the automatic analysis, generate assignment data recording assignments of event-driven application components to a plurality of computational nodes in a distributed computing environment; and   determine component requirements for each of the plurality of event-driven application components required to support execution at an assigned computational node in the distributed computing environment.   
               

     14. The computing apparatus according to any one or more of the preceding claims including assigning each of the plurality of computational nodes to a respective node set of a plurality of node sets. 
     15. The computing apparatus according to any one or more of the preceding claims, wherein the generating of the assignment data comprises assigning a common set of event-application components to each node of a respective node set of the plurality of node sets. 
     16. The computing apparatus according to any one or more of the preceding claims, wherein the set of rules is applied to assign each of the plurality of event-driven application components to an optimal node set of the plurality of node sets that is sufficient to provide proper operation of the event-driven application. 
     17. The computing apparatus according to any one or more of the preceding claims, wherein the optimal node set to which a respective event-driven application component is assigned is identified based on a determination of minimal code that may be assigned to the respective event-driven application component. 
     18. The computing apparatus according to any one or more of the preceding claims, wherein the automatic analysis of the application source code includes identifying a notational declaration of a remote reference by each of the event-driven application components of the plurality of event-driven application components. 
     19. The computing apparatus according to any one or more of the preceding claims, wherein the notational declaration of the remote reference comprises a declaration of a logical computing resource constraint of the remote reference. 
     20. The computing apparatus according to any one or more of the preceding claims, wherein the analyzing of the application source code includes analyzing the notational declaration to identify a class of computational nodes on which a programming directive, specified by a specific event-driven application component, is to be executed. 
     21. The computing apparatus according to any one or more of the preceding claims, wherein the analyzing of the application source code includes determining a resource that operationally triggers execution of a specific event-driven application component. 
     22. The computing apparatus according to any one or more of the preceding claims wherein the analyzing of application source codes includes determining a resource referenced by a specific event-driven application component 
     23. The computing apparatus according to any one or more of the preceding claims, wherein analyzing the application source code includes identifying a dependency of an identified code segment of a specific event-driven application component. 
     24. The computing apparatus according to any one or more of the preceding claims, wherein the automatic analysis of the application source code includes generating analysis metadata that is used to generate the assignment data. 
     25. A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to:
         automatically analyze application source code of an event-driven application, using one or more processors, to identify relationships between a plurality of event-driven application components; and   apply, using the one or more processors, a set of rules to:
           based on the automatic analysis, generate assignment data recording assignments of event-driven application components to a plurality of computational nodes in a distributed computing environment; and   determine component requirements for each of the plurality of event-driven application components required to support execution at an assigned computational node in the distributed computing environment.