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Industrial Ethernet Book 103

Applications (and ultimate goal) of SaaS for vendors is to use it as a platform for deploying products and solutions which include capabilities that are simply not possible with traditional on-premise deployment: Big Data analytics; geographic redundancy; IoT-level scalability; and world-wide access. Cloud computing has introduced, or at least raised to a high degree of public awareness, many new technologies and concepts. Big Data describes data sets of magnitudes and complexities never seen before. Big Data implies analysis of these data sets using specialized tools, algorithms programming languages and technologies to discover trends and patterns which may be used to drive business value. Data sets are characterized by the ‘Four Vs’, described below. Four Vs of Big Data The Four V’s of Big Data are Volume, Velocity, Variety and Veracity. Volume: Represents the scale of the data sets. One hears of systems running at ‘Big Data Scale’, which implies that they can consume and process data sets of unimaginable size. Technologies been built exclusively to address the need to scale with the many orders of magnitude expansion of storage needs. An example of this is NoSQL databases, which can scale to Big Data scale in IoT systems, replacing SQL databases which cannot. Velocity: Represents the ubiquitous and persistent collection and delivery of data, from consumers, sensors, systems into cloud-based Big Data oriented-systems. Variety: Represents the ability to combine dissimilar data sets which previously could not have been combined are now being processed together. The Cloud enables the combination of public and private data to provide insights previously unattainable. Tweets, traffic, weather, Facebook, stock prices postings are examples of well-known dissimilar data sets which could be combined and analyzed to provide previously unattainable analytic outcomes. Industrial examples of public/private data sets are current power costs, impending weather events, raw materials cost, motor/ machine/plant efficiency metrics, etc. Veracity: Represents the fact that much of Big Data information available is of questionable quality. Inaccuracy of data in consumer-based Big Data applications is a challenge, requiring cleansing and validation processing and assessment. It may not pose such a challenge in more tightly managed and audited Industrial environments. Nevertheless, it must be accounted for. NoSQL NoSQL is a database model/approach which has evolved during the Big Data era and which contrasts markedly with Relational Database Management Systems (RDBMS or SQL): NoSQL databases have relaxed or non-existent referential integrity requirements, flexible storage options and limitless scalability and redundancy built in from the ground up. The acronym NoSQL originated from ‘non SQL’ and/ or arguably ‘not only SQL’. AMQP AMQP, Advanced Message Queuing Protocol, is the open standard and has emerged as a very popular protocol for sending messages to and receiving messages from Cloud-based systems. In addition to being open and standard, AMQP was designed with these characteristics Security, Reliability, Interoperability. MQTT MQTT, Message Queue Telemetry Transport, is an ISO standard (ISO/IEC PRF 200922), publish/subscribe, lightweight messaging protocol used for Cloud-connectivity for limited network bandwidth and remote applications. JSON JSON, JavaScript Object Notation, is a terse, readable, structured data format. It is very popular as a payload format for Device-to- Cloud and Cloud-to-Device messaging. A benefit to using JSON is that many stream processing applications are built to natively consume JSON structures efficiently and cost-effectively. Below is a very basic JSON message: { “name”=”CICI”, “message”=”Hello World!” } Guiding principles or concerns As we apply cloud technology and cloud communication patterns to use cases, a number of concerns have been identified. These concerns form the basis for guiding principles to be applied to any resulting work. The technical, semantic and application difference between cloud-based and CIP Device-based ecosystems is vast. The purpose of CICI is to define an integration approach to bridge between the two environments so that cloud-based applications (and application developers) can consume CIP Device data and produce actionable information useful to the CIP Device, directly or indirectly. It is not the purpose of CICI to replicate the CIP networkbased ecosystem in the Cloud but to abstract and represent CIP Device data so that its use by Cloud applications is simple and efficient. The following are some of fundamental differences of Cloud-based from CIP-network based application development: Distribution: Cloud-based applications are intrinsically distributed, combining resources from multiple compute, storage and service platforms to achieve function. Real-Time: due to its distributed nature and platform dependencies, the notion of real-time is an uncommon concept in public cloud computing. 16 industrial ethernet book 11.2017 SOURCE: ODVA The Inquiry Information Exchange pattern using the reference architecture.


Industrial Ethernet Book 103
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