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Edge Computing & Edge Intelligence

Edge computing and edge intelligence shifts data processing, computing applications, and services away from centralized cloud based servers to the edges of a network. This enables analytics to occur at the source of the data where it can trigger events in real time, without time delays as data moved between cloud servers. Existing cloud-based technologies do not solve problems of data analytics, software deployment, or updates and security for remote devices. Edge or fog computing solves the problem of managing large amounts of machine-generated data by processing data at the edge of the network and converting it into actionable, useful business information. Software can be deployed at various points in the network to not only automate monitoring and control, but also to apply embedded intelligent agents that can adjust device behaviors in relation to ongoing performance variables, thereby reducing running costs by reducing power consumption during off-cycles, or even detecting imminent failures and notifying technicians to perform preventative maintenance. Edge computing also allows remote software deployment and secure M2M communication. Edge computing leverages resources that are not continuously connected to a network, such as laptops, smartphones, tablets, and sensors. It covers a wide range of technologies, from wireless sensor networks and mobile data acquisition to cooperative distributed peer-to-peer ad hoc networking and processing.

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  • INDUSTRIES
  • Energy
    Transportation
  • FUNCTIONS
  • Discrete Manufacturing
    Maintenance
  • CASE STUDIES
  • Dell Technologies: A repeatable model for industrial data intelligence
    Exara’s oil and gas client required a reliable way to gather, store, and process data from sophisticated machine assets in remote oil field sites. These harsh, real world environments present significant challenges for high performance computing devices.
    Hewlett Packard Enterprise (HPE): IIC - Edge Intelligence Testbed
    A test environment is needed for algorithms and architectures that meets a common set of requirements for many testbeds (see "Testbed in Depth")GOAL:A test facility that can be configured into complex edge compute environments, in order to further the state-of-the-art in edge analytics and algorithms
    Endian: Endian 4i and Switchboard for Instrumentation Laboratory
    Connect diagnostic equipment located in hospitals and laboratories to insecure LAN segments for remote monitoring and predictive maintenance.
  • MARKET SIZE
  • The global Edge Computing Market is expected to grow at USD 19.4 billion by the end of the year 2023 with 17.9% CAGR during the forecast period 2017-2023.

    Source: ABNEWSWIRE

    The overall edge computing market is expected to grow from USD 1.17 billion in 2016 to USD 6.73 billion by 2022, at a CAGR of 35.4% from 2017 to 2022.

    Source: Markets and Markets

     

  • BUSINESS VIEWPOINT
  • What are the advantages of edge computing?

    - Edge application services decrease the volumes of data that must be moved, the consequent traffic, and the distance the data must travel, thereby reducing transmission costs, shrinking latency, and improving quality of service (QoS). Computation offloading for real-time applications particularly benefits from shortening the distance between the user and the server.

    - Edge computing eliminates, or at least de-emphasizes, the core computing environment, limiting or removing a major bottleneck and a potential single point of failure.

    - Ability to ride the improvements by exploiting of the same architecture and fundamental underlying computing technologies as public and private clouds. Different cloud computing paradigms share common distributed systems architectures and technologies forming three modes defined by the distance from the edge: Centralized Clouds, Edge Clouds, and Edge nodes.

     

  • STAKEHOLDER VIEWPOINT
  • TECHNOLOGY VIEWPOINT
  • What kind of technologies Edge Computing includes?

    It includes a wide range of technologies including wireless sensor networks, mobile data acquisition, mobile signature analysis, cooperative distributed peer-to-peer ad hoc networking and processing also classifiable as local cloud/fog computing and grid computing, dew computing, mobile edge computing, cloudlet, distributed data storage and retrieval, autonomic self-healing networks, remote cloud services, augmented reality, the Internet of Things and more.

    How has Edge Computing modernized technology?

    It has introduced the concept of using gateway servers, cloudlets, fog nodes, and microdata centers ”all of which are highly advanced and sophisticated technologies to augment the benefits of Edge Computing. 

    Why do companies use Edge Computing technologies?

    To analyze the data locally, sending only the most important data to a centralized cloud. This reduces data transmission and storage costs while also allowing real-time analysis and action.

     

  • DATA VIEWPOINT
  • What is the difference in data processing between Edge Computing and a typical Cloud environment?

    In Edge Computing, data is processed near the data source or at the edge of the network while in a typical Cloud environment, data processing happens in a centralized data storage location.

     

  • DEPLOYMENT CHALLENGES
  • What are the challenges of Edge Computing?

    - It must be designed to work in the face of sporadic availability/connectivity of edge compute nodes, since edge nodes may only have power available sporadically.

    - It requires applications to be built for horizontal scalability. Generally,  the recommendation is to build applications that follow the 12-factor application guidelines.

    - It requires operations to be able to deploy to a distributed set of edge nodes, coordinate cross-node state, and storage, or handle inconsistent state gracefully.

     

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