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Introduction to Edge Computing

Brief introduction to edge computing, its relationship with cloud computer, why edge computing, what it can do.

lenmei
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Introduction to Edge Computing

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  1. Dr. Len Mei 2020/08 INTRODUCTION TO EDGE COMPUTING

  2. DEFINITION OF EDGE COMPUTING  Edge computing is a distributed computing close to the sources of data.  Edge computing infrastructure includes  Edge devices connecting external objects to collect data  Edge servers to process data in real time onsite  It forms a localized system with data collection and processing capabilities near the data source.  By doing so, it avoids the need of large quantity data transmission between data source and centralized cloud, thus providing fast response.

  3. CLOUD AND EDGE COMPUTING CLOUD COMPUTING ‘Edge' refers to the computing infrastructure that exists close to the sources of data data Wide area network Mobile Edge computing Mobile device Edge computing Mobile device Mobile device Edge devices IoT IoT IoT Local area network . Data generators machines

  4. Data Center/ Cloud computing Wide Area Network/ Internet Edge computing (Edge servers & edge devices) local Local Area Network Sensors, IoT, cameras etc. Physical world

  5. WHAT IS AN EDGE DEVICE?  An edge device is a bridge between LAN and WAN/Internet. WHAT IS AN EDGE SERVER?  Edge server is a computer running applications close to the edge of the network.

  6. WHAT IS MOBILE EDGE COMPUTING?  Mobile edge computing (MEC) is a system having computing capabilities at the edge of the cellular network serving mobile devices.  MEC can run applications closer to the cellular customer.

  7. EDGE COMPUTING  Edge computing pushes the frontier of computing applications, data, and services away from centralized nodes to the logical edge of a network.  It leverages resources that may not be continuously connected to a network.

  8. DRIVING FORCE OF EDGE COMPUTING  Companies are constantly pursuing new levels of performance and productivity using technology innovations.  By applying big data, advanced analytics, and machine learning to operations, industrials can reduce unplanned downtime, improve asset performance, lower cost of maintenance, and open up potential for new business models that capture as-yet untapped value from machine data.

  9. THE RISE OF IIOT AND DATA VOLUME  Tens of billions of connected things will generate massive volumes of data from disparate sources.  McKinsey & Co., estimates that the Industrial Internet of Things (IIoT) will create $7.5T in value by 2025.

  10. EDGE COMPUTING IS THE OVERFLOW OF CLOUD COMPUTING  Cloud computing requires a large volume of data to be transmitted from the IoT sites to the centralized data center.  The burden on the communication bandwidth increases proportionally to the volume of data.  With the ever improving computing power, much of the computing can be done near the data source to avoid long data transfer.  Edge computing is to turn massive amounts of machine-based data into actionable intelligence closer to the source of the data.

  11. CLOUD COMPUTING IS STILL IMPORTANT  Cloud manages large volumes of data to achieve key business outcomes.  Cloud computing plays a critical role in enabling new levels of performance through the Industrial IoT, where significant computing power is required to effectively manage vast data volumes from machines.

  12. THE ROLE OF EDGE COMPUTING  It is unnecessary and impractical to send all data to the cloud because the data has only short-term value.  Speed of actuation on that data is paramount.  As more computing, storage, and analytic capability is bundled into smaller devices that sit closer to the source of data, edge computing becomes more realistic to serve the computing needs of the Industrial IoT.

  13. EDGE AI  Artificial Intelligence at edge computing helps devices send and get their data processed in real-time.  With Edge AI, AI algorithms are processed locally hardware) without requiring an Internet connection to cloud server.  Edge AI provides more precise automation, quality control, faster decision-making, more safety, and lower costs.

  14. WHAT MAKES EDGE COMPUTING GROW?  Cost of compute and sensors continue to plunge.  More computing power with machine learning and data analytical capability in smaller footprint devices.  Growing volume of data from machines and/or the environment.

  15. EDGE COMPUTING IS THE BEST FOR  Low/intermittent connectivity (such as a remote location)  Limited bandwidth and high cost of transferring data to the cloud  Low latency, such as closed-loop interaction between machine insights and actuation (i.e. taking action on the machine)  Quick response (say, a technician working in the field to check machine performance)  Real-time analytics  Compliance, regulation, or cyber security constraints

  16. EDGE COMPUTING IS USED FOR Predictive maintenance  Reducing costs  Security assurance  Product-to-service extension (new revenue streams) Energy Efficiency Management  Lower energy consumption  Lower maintenance costs  Higher reliability Smart manufacturing  Increased customer demands mean product service life is dramatically reduced  Customization of production modes  Small-quantity and multi-batch modes are beginning to replace high-volume manufacturing Flexible device replacement  Flexible adjustments to production plan  Rapid deployment of new processes and models    

  17. CLOUD VS EDGE COMPUTING  Edge computer is ideal for  low latency application where speed is important  where there are bandwidth constraints or when Internet or cellular connections are spotty  Cloud computing is ideal  when actions require significant computing power,  managing data volumes from across plants,  asset health monitoring, and machine learning  Edge computing can be viewed as an extension of cloud computing.

  18. EDGE COMPUTING: TODAY AND TOMORROW  Today, edge computing performs a limited role to ingest, store, filter, and send data to cloud systems.  With the rapid progress in computing, data processing capability and artificial intelligence, edge computing will acquire more computing, storage, and analytic power, therefore playing a greater role.  As edge computing becomes more powerful, more computing needs will be addressed by edge computing.

  19. EDGE COMPUTING FOR INDUSTRY  Edge computing allows industrial companies to track, manage, and communicate with all network edge devices anytime, anywhere, and for advanced monitoring and diagnostics, machine performance optimization, proactive maintenance, and operational intelligence.  This gives industrials the flexibility to manage and process machine data wherever it makes the most sense for optimal operation—at the edge, in the cloud or a combination of the two.

  20. EXAMPLE OF EDGE COMPUTING: AUTONOMOUS VEHICLES  Autonomous automobiles is essentially an edge computing system.  It must react to the driving situation in real time with ultra-low latency to ensure safe operation for passengers and the public.

  21. EXAMPLE OF EDGE COMPUTING: SMART GRID  Edge grid computing enables utilities with real-time monitoring and analytics capabilities, generating insights on distributed energy generating resources.  It allows renewable energy sources such as small scale solar, wind power generators to draw needed power and sell surplus power locally, reducing overall costs and energy waste.

  22. EXAMPLE OF EDGE COMPUTING: MONITORING OF CRITICAL INFRASTRUCTURE  Any infrastructure which requires real-time monitoring is a candidate for edge computing, such as oil and gas exploration.  Edge computing allows data to be analyzed, processed and delivered to end-users in real- time, enabling control center foreseeing and preventing malfunctions in the timely manner.

  23. EXAMPLE OF EDGE COMPUTING: TRAFFIC MANAGEMENT  Traffic management requires real time intervention.  For example: traffic light can be controlled by the traffic flux in the intersection. Real time alerts can be delivered if an accident impede the traffic flow.

  24. EXAMPLE OF EDGE COMPUTING: INDUSTRIAL CONTROL  Running AI-algorithms (such as machine vision and machine learning) can guarantee more precision and quality control on the manufacturing floor.  The cameras and sensors deployed across the manufacturing plant collect and feed data into the edge computer with AI algorithm.

  25. CONCLUSION  Edge computing will become more prevalent because: Data volume grows exponentially Computing power increases rapidly Internet of things will be omnipresent Computing will become more distributed The need for fast response computing  Edge computing will create many real-time applications

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