Connected devices leading to the growth of edge computing

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The rise of the connected devices ecosystem has led to a rise in demand for high-speed networks. It has exposed the limitations of the overall network architecture, highlighting the need for edge computing. There are hundreds of sensors in a modern-day car and creates tons of data. And while most of it is processed in the vehicle itself, transfer of data to the cloud might be needed by some in-car applications. Data moved to the cloud could be constrained more intelligently with edge computing. With the advent of next-gen technologies in the autonomous vehicle ecosystem, challenges such as delay in data transmission, real-time results, on-the-spot accurate and vital decisions, processing of large quantities of data, etc., have also increased. Automotive players are focused on leveraging edge computing to address these ever-evolving challenges.

In traditional enterprise computing, data is produced at a user's computer. That data is moved across a WAN such as the internet, through the corporate LAN, where the data is stored and worked upon by an enterprise application. Results of that work are then conveyed back to the end-user. With regards to infrastructure of edge computing, it is a network of local micro datacenters for storage and processing purposes. At the same time, the central datacenter oversees the proceedings and gets valuable insights into the local data processing. However, we need to be mindful that edge computing is a kind of expansion of cloud computing architecture - an optimized solution for decentralized infrastructure. The fact is in a digital-first world, personalized content drives everything in the digital economy.


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    Connected devices leading to the growth of edge computing

    The rise of the connected devices ecosystem has led to a rise in demand for high-speed networks. It has exposed the limitations of the overall network architecture, highlighting the need for edge computing. There are hundreds of sensors in a modern-day car and creates tons of data. And while most of it is processed in the vehicle itself, transfer of data to the cloud might be needed by some in-car applications. Data moved to the cloud could be constrained more intelligently with edge computing. With the advent of next-gen technologies in the autonomous vehicle ecosystem, challenges such as delay in data transmission, real-time results, on-the-spot accurate and vital decisions, processing of large quantities of data, etc., have also increased. Automotive players are focused on leveraging edge computing to address these ever-evolving challenges.

    In traditional enterprise computing, data is produced at a user's computer. That data is moved across a WAN such as the internet, through the corporate LAN, where the data is stored and worked upon by an enterprise application. Results of that work are then conveyed back to the end-user. With regards to infrastructure of edge computing, it is a network of local micro datacenters for storage and processing purposes. At the same time, the central datacenter oversees the proceedings and gets valuable insights into the local data processing. However, we need to be mindful that edge computing is a kind of expansion of cloud computing architecture - an optimized solution for decentralized infrastructure. The fact is in a digital-first world, personalized content drives everything in the digital economy.


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    There is a continued dialogue among (Cloud Vs Edge), on who will overtake whom. The future of edge computing is right around the corner. Experts feel that it has the power to protect the climate, improve our health, and squash road rage. However, even though cloud-based computing is great for many things, there are some drawbacks.

    IDC forecasts the global edge computing market to reach $250 billion by 2024, with a compounded annual growth of 12.5%. No wonder the industry is talking about Edge Computing.

    #Edgecomputing reclaims much of the storage and power that cloud computing took on over the years. Businesses are always trying to improve the reliability and performance of their software for users, while at the same time trying to reduce their own costs. We have seen how CDNs (content delivery networks) are an early form of edge software, with companies originally serving static content from locations closer to their users. The rise of CDNs has made it easier to roll out your entire application as close to your users as possible.

    Edge computing is one of the 'new revolutionary technologies' that can change organizations wanting to break free from previous limitations of traditional cloud-based networks. The next 12-18 months will prove to be the natural inflection for edge computing. Practical applications are finally emerging where this architecture can bring real benefits.

    91% of our data today is created and processed in centralized data centres. Cloud computing will continue to contribute to businesses regarding cost optimization, agility, resiliency, and innovation catalyst. But in the future, the 'Internet of Behaviours (IoB)' will power the next level of growth with endless new possibilities to re-imagine the products & services, user experiences, and operational excellence.

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  • Watch Edge computing is gaining popularity in COVID-19 Video
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    Edge computing was considered a futuristic concept – something that was attractive to talk about but lacked practical world examples. This has ceased to be the truth! Amidst spiraling numbers of COVID-19 cases, organisations are relooking their operational structures, to meet the new challenges that the second wave has brought about. As remote working has once again become a buzzword, paving the way for cloud enabled technologies to shape the new normal. At the same time, the cloud is gaining momentum, and enterprises are frantically looking for ways to optimize their network, storage and agility, edge computing has turned out to be the perfect solution.

    Edge computing” is a type of distributed architecture in which data processing occurs close to the source of data, i.e., at the “edge” of the system. With the growing number of IoT devices along with connected automotive and industrial applications; latency, privacy, and bandwidth become critical limiting factors and edge computing solves this by bringing the intelligence closer to the source. Edge computing represents a crucial investment for any smart city in order to really reap the benefits of a next-generation IoT network. Its prime feature comprises closer proximity between data storage and processing which can be leveraged in enhancing the data management and processing for smart cities. Edge computing has emerged as the viable architecture as it supports distributed computing to deploy compute and storage resources closer to the data source.


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    Although cloud computing continues to play an important role in modern network architecture, the exciting possibilities offered by Internet of Things (IoT) devices, which are capable of processing the data they gather closer to the source, are forcing companies to rethink their approach to IT infrastructure. Often touted as the “next big thing,” . As many companies are wondering how edge computing differs from more traditional data processing solutions and how it could benefit their business. Edge computing not only has the ability to deliver a direct on-ramp to a company’s preferred cloud platform, but it also can help achieve flexibility and nimbleness when it comes to ensuring a streamlined, efficient IT infrastructure. There are challenges that demonstrate the growing need for an alternative to cloud computing.



    Cloud computing is an inherently centralized computing paradigm. Edge Computing is a decentralized topology that is based on keeping data local, at the ‘edge’ of the network, as close to the source as possible. Today’s IoT devices are capable of gathering, storing, and processing more data than ever before. This opens up opportunities for companies to optimize their networks and relocate more processing functions closer to where data is gathered at the network edge. As the data in cloud computing networks are centralized, with data being gathered on the outermost edges and transmitted back to the main servers for processing. This architecture grew out of the fact that most of the devices located near the edge lacked the computational power and storage capacity to analyze or process the data they collected. Even as more devices became capable of connecting to networks over cellular and WiFi, their functionality was relatively limited by their hardware capabilities.

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    Data center is critical to an organization’s performance and success, this decision needs to be taken with due diligence. Cloud computing has played an integral role in building modern network architecture. With the advancement of emerging technologies such as the Internet of Things (IoT), the amount of data collected at the source is growing exponentially. This has significantly raised the demand for computation of this data at the edge. It is an absolute fact that, Edge Data Centers are bringing significant benefits by reducing latency. With these innovations, there is a parallel evolution in the data center environment. From on-premise data center to cloud to the new entrant edge; data centers are growing more sophisticated with each passing day to keep up with the dynamic business world.



    The four types of data centres we see are On-premise Data Centers, Colocation Data Centers, Hyperscale Data Centers and Edge Data Centers. On-premise data centers reduce latency as they are stored within the company location, thereby ensuring higher level of data security. However, they are not cost-efficient solutions as they involve higher cost for maintenance, security, and regular repairs. Whereas, Colocation Data Centers bring cost-effective methods of housing data centers as the companies need not worry about its maintenance, security, and repair. However, Hyperscale data centers are large and can house thousands or millions of data center servers. These data centers are meant to be scaled, which can be done by increasing the power of the system or adding more data center racks and equipment. Companies with large amounts of data can benefit from the scalability of hyperscale data centers. As technology is getting advanced, many are preferring to go for Edge data centers which are smaller facilities located close to the edge of the end-user network.


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    As digital transformation continues across industries, modern data management is set to change. Most of the world’s data is expected to move out of the public cloud environment where non-real-time, centralized data is being managed. We anticipate that the entire data management ecosystem will develop and utilize edge IT capacity at the ingress and egress of their data pipelines, and remotely process and digest data on the edge. As the data management ecosystem extends to the edge, the number of edge workloads will continue to grow dramatically, and dat

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