What is edge computing?

00:03:19
https://www.youtube.com/watch?v=3hScMLH7B4o

Summary

TLDREdge computing takes place at the edge of corporate networks, near devices like phones, sensors, and robots. Unlike traditional systems that rely on central data centers or the cloud, edge computing allows for local data processing, reducing latency crucial for Internet of Things (IoT) applications. This local processing means faster response times, crucial in situations where quick actions are needed, like shutting down equipment at a refinery upon detecting critical levels. Edge computing also allows non-time-sensitive data to be transmitted cost-efficiently over slower connections. While offering these advantages, edge computing introduces security challenges as data can be vulnerable if edge and IoT devices are compromised. Redundancy and failover mechanisms are essential to prevent the edge devices from becoming single points of failure. As real-time applications grow, edge computing is expected to become an integral part of IT infrastructure.

Takeaways

  • 📱 Edge computing occurs where end devices meet the network, reducing latency.
  • 📈 The explosion of IoT has highlighted shortcomings in traditional models.
  • ⚙️ Faster connections between data centers and IoT devices are crucial.
  • 🚨 Local processing can prevent critical delays in emergency situations.
  • 🌾 Edge devices perform preliminary data analysis before sending it on.
  • 💡 Slower connections can handle non-time-sensitive data, reducing costs.
  • 🔒 Security is essential for protecting data at the edge.
  • 🔄 Redundancy is required to prevent single points of failure in edge devices.
  • 🔗 Connected to the cloud, edge devices still need secure infrastructure.
  • 🚀 Edge computing is increasingly essential for real-time applications.

Timeline

  • 00:00:00 - 00:03:19

    Edge computing involves processing data at the edge of corporate networks, near end devices like phones and sensors. Traditionally, these devices connected to central data centers for data exchange and updates. However, the rise of IoT has overloaded this model, requiring faster, sometimes local processing to handle real-time data like in oil refineries for prompt safety responses. Edge computing enables preliminary data analysis close to data sources, reducing latency and expensive data center connectivity, although it still requires connections for less time-sensitive data. While offering benefits in speed and reduced growth of expensive network connections, it raises security concerns, necessitating protection for both the IoT and edge devices to prevent network vulnerabilities. Industry efforts are focusing on building redundancy and failover contingencies to prevent downtime, as edge computing becomes integral for real-time applications and continues to gain importance.

Mind Map

Video Q&A

  • What is edge computing?

    Edge computing involves processing data at the network's edge, closer to where it is generated, to reduce latency.

  • Why is edge computing important for IoT?

    It reduces the latency between data generation and processing, which is critical for timely responses in IoT applications.

  • How does edge computing reduce costs?

    By processing data locally, it minimizes the need for expensive, high-capacity connections to central data centers.

  • What are the security concerns in edge computing?

    Data at the edge must be secured to prevent unauthorized access and potential network compromise.

  • How does edge computing help in emergency situations?

    Local processing allows for faster response times, such as stopping machinery in case of detected issues.

  • What is the role of edge devices in agriculture?

    They collect and analyze data locally, like temperature and humidity, to optimize data flow to central storage.

  • Can edge computing prevent downtime?

    Yes, by incorporating redundancy and failover strategies to avoid single points of failure.

  • Is edge computing becoming mainstream?

    Yes, its importance is growing with the increased demand for real-time application performance.

  • What does 'latency' mean in the context of edge computing?

    Latency refers to the delay before data processing begins after data is requested, which edge computing aims to minimize.

  • Are all edge data connections time-sensitive?

    No, not all data connections are time-sensitive; non-critical data can travel over slower connections to reduce costs.

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  • 00:00:06
    Edge computing is what it sounds like: computing that takes place at the edge of corporate
  • 00:00:13
    networks, with “the edge” being defined as the place where end devices access the
  • 00:00:17
    rest of the network – things like phones, laptops, industrial robots, and sensors.
  • 00:00:22
    The edge used to be a place where these devices connected so they could deliver data to, and
  • 00:00:27
    receive instructions and download software updates from a centrally located data center
  • 00:00:32
    or the cloud.
  • 00:00:34
    Now with the explosion of the Internet of Things, that model has shortcomings. IoT devices
  • 00:00:39
    gather so much data that the sheer volume requires larger and more expensive connections
  • 00:00:44
    to data centers and the cloud.
  • 00:00:45
    The nature of the work performed by these IoT devices is also creating a need for much
  • 00:00:51
    faster connections between the data center or cloud and the devices. For example, if
  • 00:00:57
    sensors in valves at a petroleum refinery detect dangerously high pressure in the pipes,
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    shutoffs need to be triggered as soon as possible. With analysis of that pressure data taking
  • 00:01:07
    place at distant processing centers, the automatic shutoff instructions may come too late. But
  • 00:01:15
    with processing power placed local to the end devices, latency is less, and that roundtrip
  • 00:01:20
    time can be significantly reduced, potentially saving downtime, damage to property and even
  • 00:01:25
    lives. Even with the introduction of edge devices that provide local computing and storage,
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    there will still be a need to connect them to data centers, whether they are on premises
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    or in the cloud. For example, temperature and humidity sensors in agricultural fields
  • 00:01:40
    gather valuable data, but that data doesn’t have to be analyzed or stored in real time.
  • 00:01:46
    Edge devices can collect, sort and perform preliminary analysis of the data, then send
  • 00:01:51
    it along to where it needs to go: to centralized applications or some form of long-term storage,
  • 00:01:57
    again either on-prem or in the cloud. Because this traffic may not be time-sensitive, slower,
  • 00:02:03
    less expensive connections – possibly over the internet – can be used. And because
  • 00:02:08
    the data is presorted, the volume of traffic that needs to be sent at all may be reduced.
  • 00:02:13
    So the upside of edge computing is faster response time for applications that require
  • 00:02:17
    it and slowing the growth of expensive long-haul connections to processing and storage centers.
  • 00:02:22
    The downside can be security. With data being collected and analyzed at the edge, it’s
  • 00:02:28
    important to include security for the IoT devices that connect to the edge devices and
  • 00:02:32
    for the edge devices themselves. They contains valuable data, but they are also network elements
  • 00:02:37
    that, if exploited, could compromise other devices that contain stores of valuable assets.
  • 00:02:44
    With edge computing becoming more essential, it’s also important to make sure that the
  • 00:02:47
    edge devices themselves don’t become a single point of failure. Network architects need
  • 00:02:52
    to build in redundancy and provide failover contingencies in order to avoid crippling
  • 00:02:57
    downtime if a primary node goes down. The industry has already gone a long way toward
  • 00:03:02
    addressing the demands of edge computing, and it is becoming mainstream. Its importance
  • 00:03:07
    is likely to grow even more as the use of real-time applications becomes more prevalent.
Tags
  • edge computing
  • IoT
  • latency
  • data centers
  • cloud
  • security
  • network redundancy
  • real-time applications
  • agriculture sensors
  • failure prevention