Edge Computing: The Magic Spell to Bring Homes and Industries to Life with IoT

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Why Does Edge Computing Matter?

Picture this scene: You’re lying asleep in your temperature-controlled bed and your alarm clock goes off, directing your curtains to part, letting in just enough sunshine to gently wake you from your slumber. As you’re getting out of bed, you hear upbeat music playing through your smart speaker. Sensors in your floor tell your coffee machine you’re out of bed and it whirrs into action. You enter the kitchen where a pot of freshly brewed coffee awaits you as the music is replaced by a narration of the morning’s news headlines.

Sounds like the life of Tony Stark, doesn’t it? But what does it have to do with me, we hear you wonder. Well, this hyper-advanced lifestyle is yours to experience sooner than you might think.

Internet of Things (IoT) as a concept has been around for a while. A web of interconnected devices exchanging data with one another with the help of software, sensors and other technologies. The network has given rise to numerous revolutionary applications in fields like healthcare, transportation, communication, manufacturing and of course, smart homes. Such is the rise in its ubiquitousness that there were already 620 different publicly-recorded IoT platforms, rising from a mere 260 platforms in 2015. This includes behemoths such as Amazon and Microsoft who are vying for a slice of the ever-growing IoT platform pie. In terms of projections, it is estimated that by 2023, the global spending on IoT will be a massive 1.1 trillion USD. This can be accounted for by security becoming a point of focus for businesses as well as the need for improving efficiency and reducing operational costs. Smart homes and smart cities are also being seen as worthy causes for technology spending.

There however exists a bottleneck to IoT and other such technologies designed to take all of us into the future. This bottleneck is in the form of the processing of all the data that is generated by IoT devices (among other sources), choking up the centralised networks of today. Enter: Edge Computing. With improvements in parameters of efficiency, latency, bandwidth consumption and network congestion, Edge Computing is the magic spell that’s going to bring us closer to that delicious, automatically-brewed pot of coffee in the morning. Not to mention its far-reaching implications for industries in terms of security, efficiency and cost-savings.

What is Edge Computing?

Edge computing is the paradigm that allows for computing to be conducted near or at the data source. This is different from the traditional method of having the cloud at the data centre to serve as the sole location for computing to occur. This doesn’t mean that the cloud is going to disappear. It just means that the cloud is coming closer to you.

Edge computing optimises web applications by getting computing closer to the data source. Literal geographical distribution is the meaning of the word “edge” in this context. This minimises the need for client and server long-distance communications, which reduces the use of latency and bandwidth. By getting computing closer to the source of the data, edge computing optimises Internet devices and web applications. Such is its usefulness that the global edge computing market is set to rise to 15.7 billion in 2025 from a mere 3.6 billion in 2017.

For an even simpler breakdown of Edge Computing and the concepts surrounding it, allow us to point you to the two-parter STL TechTalk on the same topic:

Part 1 :

Part 2 :

Why Is There a Need for Edge Computing?

The proliferation of connected devices such as smartphones, tablets and electronics along with the online content consumption explosion in recent years may soon overwhelm the centralised networks of today. As per IDC estimates, by 2025 there will be 55.7 billion connected devices worldwide, 75% of which will be connected to an IoT platform. According to estimates put forward by IDC, connected IoT devices will see as much as 73.1 ZB data generated by 2025 (up from 18.3 ZB in 2019). While video surveillance and security make up most of this data, industrial IoT applications will also form a significant portion of it.

With such an explosion of data, the centralised networks of today may soon be overwhelmed with traffic. With a distributed IT architecture that pushes data centre capital toward the network periphery, edge computing aims to counter the imminent data boom.

How does edge computing work?

Be it email services like Gmail or online image storage services like iCloud, our lives have been completely taken over by what we’ve come to know as cloud computing services. Even large enterprises are heavily reliant on the cloud and have started moving their critical applications to the cloud, including data from thousands of sensors sitting inside their manufacturing units in order to get quick insights and the ability to monitor their machines remotely.

However, as these sensors that make up the Internet of Things start gaining prominence and propel explosive data growth, they put immense pressure on central cloud servers and begin to choke network bandwidth.

To fix these challenges, edge computing allows computing to happen closer to these IoT devices. You can think of them as processing your video edits on the iPhone instead of sending them to a central server for processing. Moreover, computing needs are increasingly shifting closer to where the device is or where the data is being consumed, requiring the data processing also to move closer to the devices.

Operators build several of these edge data centres instead of a single cloud data centre to build an edge cloud. By moving compute closer to the devices, edge cloud makes several new applications possible. These include autonomous driving, high-end cloud gaming, remote 3D modelling, among many others. Each of these applications requires extremely low latency, which demands more and more processing closer to the edge, making edge computing the architecture of choice.

Edge computing, therefore, comes across as the perfect solution by processing data and even analytics at or near the original source of data, cutting down on latency, reducing bandwidth costs and at the same time making the edge network less prone to a single point of failure.

Analysts estimate there will be 30.9 billion IoT devices by 2025, a steep rise from today’s 13.8 billion units in 2021. Such increases in IoT devices will put immense pressure on cloud data centers and will therefore require edge IoT to support the growth.

What Are the Major Benefits of Edge Computing?

Edge computing topology can address networks problems by helping with latency for time-sensitive applications, IoT efficiency in low bandwidth environments, and overall network congestion.

  • Latency: When data processing takes place locally rather than in a remote data centre or cloud, the time-to-action is shortened due to physical proximity. IoT and mobile endpoints will respond to critical information in near real-time because data processing and storage will take place at or near edge devices.
  • Congestion: Edge computing will help alleviate the burden on the wide-area network. This will help you save time and money by reducing the amount of bandwidth you need. In the age of mobile computing and the Internet of Things, this is a major obstacle. Edge devices will process, filter, and compress data locally, rather than flooding the network with relatively insignificant raw data.
  • Bandwidth: In environments where network access is unreliable, the edge computing topology will support IoT devices. Offshore oil rigs, remote power plants, and remote military outposts are examples of such habitats. Local compute and storage resources will allow continuous operation even if the cloud link is erratic.

Now that we’ve established the importance of Edge Computing to the future of data processing, it’s time to explain STL’s place in the grand scheme of this paradigm.

How do 5G and edge computing function together?

For most of us, 5G is not likely to have a significant impact on our lives. The speeds of 4G are enough to support the most gruelling data speed requirements of pretty much every application available today. Why 5G is significant, however, is because of the new possibilities that it enables, such as the use of autonomous drones, remote telesurgery and autonomous driving, among others.

Now, 5G can theoretically offer speeds up to 10 times that of 4G, yet the user experience may not always be as expected. That’s because, despite the speeds that the network offers, latency can take away much of the benefits. This low-latency requirement would also be crucial to the success of 5G rollouts to enable to the new use cases that the technology promises. And this is where edge computing comes in as the perfect partner by reducing latency since it brings computing capabilities closer to the user or the end device.

That said, without the support of 5G speeds and coverage, software developers of these new use cases will have no incentive to roll out these services, which have so far been tested out with a 4G-edge computing combination.

The combination of 5G and edge computing brings together a bigger, faster pipe combined with a shorter distance for the data to travel, making the two not just complementary but even inseparable.

So What Is STL’s Role in the World of Edge Computing?

When it comes to Edge Computing, STL – Sterlite Technologies Limited has a presence in edge cloud infrastructure and multi-cloud platforms. Just last year, STL engineered a cloud-native software stack on a micro-service architecture that is disaggregated and programmable. This technology is used to separate software from the hardware layer, which is then used to create virtual networks with open interfaces. This has the advantage of substantially reducing the time to market for new digital services. Furthermore, broadband network disaggregation and central office re-architecture give edge computing a stimulus that reshapes the way we work.

These efforts have been recognised by STL Partners – a research and consulting firm – in their recently concluded competition titled “Edge companies to watch in 2021” whose purpose is to draw attention to companies that are deemed to be at the cutting-edge of, well, edge technology. STL is honoured to have been featured in the list of Top 60 Companies and is even more motivated to continue pushing boundaries in the edge computing space for years to come.

Ambitions for 2021

STL aims to build multi-access edge applications in addition to designing wireless and wireline focused edge computing converged platform of the multi-access kind.

This, of course, folds neatly into STL’s greater commitment to helping large enterprises, citizen networks, cloud companies and telcos present state-of-the-art experiences to their customers. Our company’s focus remains on transforming everyday living by harnessing the power of technology to design next-gen connected experiences.

Frequently Asked Questions:

What Is the Difference Between Edge Computing and Cloud Computing?

To understand the difference between cloud and edge computing, one should focus on understanding how data processing takes place in each network configuration.

Currently, the bulk of data processing in IoT systems is done in the cloud, using a collection of centralised servers. As a result, all low-end applications, as well as gateway devices, are used for data aggregation and low-level processing.

Edge computing is distinct in that it takes a wholly different approach. It takes processing closer to the end-users by shifting it away from centralised servers. By 2020, approximately 45 per cent of the world’s data will be stored and processed at the network’s edge, or perhaps even closer.

What is Cloud Computing’s Limiting Factor?

The ongoing data consumption boom will not be adequately supported by cloud computing. During the processing stage, two issues arise. These are processing latency and a high number of unused resources. Decentralised data centres, mobile edge nodes, and cloudlets are all impacted by these problems.

When connected devices generate data, everything is piled on and transferred to the cloud for further processing. Because of this, the cloud’s data centres and networks are overloaded which results in increased latency and network inefficiency.

Edge computing enables data to be processed closer to the source of the data. This approach not only helps to reduce data dependence on the app or service but also helps to accelerate the processing of data.

What Are the Use Cases for Edge Computing?

Virtualised Radio Networks and 5G (vRAN)

Sections of mobile networks are gradually being virtualised by operators (vRAN). This is beneficial in terms of both cost and versatility. Complex processing with low latency is expected of the modern virtualised RAN hardware. Edge servers would be expected by operators to enable the virtualisation of their RAN close to the cell tower.

Predictive Maintenance

Manufacturing companies would want to have the power of being able to proactively detect and diagnose the health of the machines in the production line before a failure occurs.

Edge computing’s benefit is in helping bring data processing and storage closer to the equipment. IoT sensors can monitor machine health can perform analytics in real-time with low latencies.

Content Delivery

Content distribution can be significantly enhanced by caching content – such as music, video streams, and web pages – at the edge. Latency can be greatly decreased. Content providers are aiming to extend the delivery of content delivery networks to the edge, ensuring network stability and customisation based on user traffic demands.

Traffic Management

Edge computing can help cities handle traffic more effectively. Edge computing removes the need to transport vast volumes of traffic data to a centralised cloud, lowering bandwidth and latency costs. Optimising bus frequency in response to demand variability, controlling the opening and closing of extra lanes, and, in the future, managing automated car flows are all examples of this.

What Are Some Edge Computing Trends to Look out for in 2021?

As with most things technology, we can expect rapid advancements in the field of Edge Computing to continue this year. Some of the major trends we should look out for are:

COVID-19 to Accelerate Innovation

The pandemic has brought forward technological developments in every noticeable sphere and Edge Computing is no exception. 2021 will likely see innovation relating to the generation of actionable insights from raw data accumulated from (as an example), IoT technologies such as cleaning, sanitation, thermal imaging and social distancing.

Partnerships Between Cloud and Edge Providers

Applications built on cloud platform will see edge data integrated with them in the not so distant future. This will require partnerships to be forged between cloud providers and edge providers. The idea will be to have the cloud provide big data computing with edge supporting immediacy.

Combination of Edge with Machine Learning and Artificial Intelligence

Until now, the increased complexity of data solutions has presented challenges in terms of pre-processing of data using near-edge tech. But now, data analytics at the edge and on-device ML are being made possible with the help of container-packaged analytics applications, open standards and AI/ML-optimised hardware. These will allow for real-time personalisation and faster decision making.

What are the drawbacks of edge computing?

Like any other technology, edge computing also comes with its own set of drawbacks. The first is the price. When compared to a central data center that manages all your computing requirements single-handedly, edge cloud requires setting a vast number of edge nodes or edge devices that each has its own compute and storage capability in order to perform local processing. That can translate into cost escalations.

The second major challenge rather than a drawback to edge computing is security. Edge nodes and edge devices are usually small computers with limited computing capabilities and therefore are often not equipped with the kind of security measures that one would expect from a central data center. This is made even more cumbersome by the fact that every edge device supports different levels of authentication and security measures, making it vital for enterprises to carefully work on the security architecture of the edge cloud to prevent any breaches.

What is the network edge?

The network edge acts as a fence between the area where all the edge devices and edge networks lie and the internet. This crucial point between the internal and external network also acts as a security bridge that network administrators need to design their edge network for.

Network edge can also be seen as the point at which the enterprise network meets a third-party network. Other terms that are used to describe this point are internet edge or WAN edge.

What are the other use cases of edge computing?

Edge compute can be used for any application that requires instant processing and quick response or, in more complex words, applications with low latency requirements. There are several applications that have been demonstrated for years but lack widespread usage because of the lack of low-latency that have existed thus far. Edge computing bridges that gap and makes such applications viable for widespread adoption.

Take, for example, AR/VR applications that have existed for decades. But to get a lag-free experience and make the technology widely accepted, it requires a low-latency network that becomes possible with edge computing. More such use cases include remote monitoring of oil and gas industry assets, autonomous driving, telehealthcare and telesurgery, cloud gaming, and predictive maintenance. Edge computing has also thrown open a host of new possibilities, and with time, we can expect thousands of new use cases to emerge.

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