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    Why Is Edge Computing the Unconventional Call Of The Hour?

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    The world of computing is not going to stop on Cloud or IoT. A technology shift must manage a large amount of data collected by cloud computing and objects connected to the internet. Here comes into the action trending buzzword ‘Edge Computing.’ Cloud computing, as you all know, offers centralized access to the shared resources. These servers are geographically distant and manage IoT devices like wristbands, tablets, phones, and PC. Users face latency issues due to high-frequency communication between these devices and the cloud.

    There is a need to optimize cloud computing processes to cater to applications that need real-time responses. This challenge has given rise to the emerging technology Edge Computing. This does not mean that the cloud will vanish, but the cloud is getting closer to you by the edge. like edge computing, everyone is familiar with cloud computing. Here are 5 Benefits of Cloud-Based Computing Analytics.

    Edge Computing Architecture

    What is Edge Computing:

    working of Edge Computing

    It is an architectural concept and an added layer between Cloud computing and the Internet of Thing devices. The edge computing nodes collect data from the devices connected to the internet, process it, and only provide results to the cloud leading to the reduced transmission cost.

    It brings data closer to the devices generating it rather than depending on the centralized cloud. This reduces the load on the cloud and helps to optimize its processes. 

    Advantages

    1. Real-time applications work well with this computing.
    2. Latency issues are resolved to a great extent.
    3. Data is more secured with Edge.
    4. The distributed structure of Edge reduces bandwidth-cost, network traffic immensely.

    Disadvantages

    1. It has a limited storage capacity.
    2. Data storage and transmission are expensive.
    3. It requires proprietary network edges.

    Why is Edge Computing required today?

    Consider an internet-connected video camera monitoring one floor of the company. This camera will transmit over the network live footage of the floor. When it is only one camera doing so, then this process is quick, but with the current explosive use of Internet of Things products many such devices simultaneously transmit data leading to the latency and bandwidth issues. It is needed to improve overall network performance.

    Many companies have noticed that cost incurred due to bandwidth is too high. This sole reason is sufficient to shift to edge computing. It is also essential to support the latest technologies like AI, self-driving cars, 5G wireless communication, and real-time applications. Hence, the rapid use of IoT and applications that require real-time responses are the driving force behind advancements in Edge computing.

    How tech giants are shifting towards Edge Computing

    Frost & Sullivan research firm has predicted that 90 percent of the enterprises will use Edge computing by 2022.

    All three primary cloud services providing enterprises, Google, Amazon, and Microsoft, have already started their race to accommodate techniques in their existing setups.

    Google’s latest announcement of Anthos at the Edge and Global Mobile Edge Cloud (GMEC) to speed up 5G monetization can confirm this.

    Similarly, last year famous telecom operators Verizon, Sk Telecom, KDDI, and Vodafone announced their partnership with Amazon’s AWS to develop Edge computing services running on 5G.

    Early this year, Microsoft, with Azure Edge Zones, has promised to enable new Edge computing scenarios for its partners and developers.

    Edge computing enterprises will have more control over users’ devices, reducing the clutters of installing and updating software on these devices. On the other side, it may also increase security threats. Cloud computing sometimes reminds us of the mainframe computing where all data was managed centrally. It has added a break to this notion enforcing that all data cannot be handled at one point. Distributing workload can help to improve performance.

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