- 1 What Is Edge Compute?
- 2 What is Edge Computing?
- 3 The Four V’s of Edge Computing
- 4 What is the Network Edge in Edge Compute?
- 5 Need for Edge Compute
- 6 Key Factors behind Edge Compute Adoption
- 7 Difference Between Edge Computing and other Computing Models
- 8 How Do You Implement Edge Compute?
- 9 Advantages of Edge Computing
- 10 Edge Computing with Amazon ECS
- 11 Challenges with Deploying an ECS architecture
- 12 Disadvantages of ECS architecture
- 13 Conclusion
In this blog post, we will give a detailed guide about Edge Computing and also discuss What is edge compute? and usabilities.
Edge computing refers to a distributed network architecture in which certain data processing, storage, and input/output functions are performed closer to the sources of the data (the edge) in comparison to the central processing facilities of the network. A common misconception is that edge and IoT are synonymous, but they’re not.
What Is Edge Compute?
An emerging computing architecture extends cloud computing to devices out of reach of high-speed networks, enabling real-time applications and analytics at low latency.
Also called fog computing or close to end-users, The concept of edge computing was first introduced in 2009 by Cisco Systems Inc., but it has gained traction only recently as wireless carriers and other companies roll out 5G networks that offer higher bandwidth than previous generations.
In addition, IoT (Internet of Things) devices have become more prevalent in homes and businesses.
According to research firm IDC, about 21 billion IoT devices will be installed worldwide by 2020. That’s a lot of data that needs processing power and fast connections to analyze.
But if all these devices were connected directly to clouds, they would generate an overwhelming amount of traffic. So how do you get around these issues? The answer lies in distributed computation and storage closer to where data originates.
This strategy makes sense for several reasons: It reduces network congestion by cutting down on long-distance transmission.
It improves response times because calculations don’t need to travel across wide areas, and it can save money because fewer hardware resources are needed in central locations.
What is Edge Computing?
To start, let’s demystify what exactly edge computing means. It’s not just an industry buzzword—though it has certainly caught on in recent years thanks to marketing efforts around connected devices and AI applications edge computing is a relatively new term that describes a concept that’s been around for some time.
Essentially, it refers to systems and networks that are designed to be responsive in real-time by processing or storing data as close as possible to its source.
In other words, data doesn’t have to travel far from where it originates before being processed.
This allows for faster response times when dealing with requests and queries from users, IoT devices, etc., while also saving bandwidth.
The Four V’s of Edge Computing
The ones with those characteristics are typically called the four Vs – Volume, Velocity, Variety, and Veracity.
Each V defines an important aspect of how different kinds of devices collect and process data, where they send it, and when they send it.
Together these form a framework for understanding what edge computing means and why it’s more than just another buzzword.
These 4 V’s also show how edge computing will be used across industries including IoT, autonomous driving, mobile, augmented reality, and more.
And even though each industry has its own unique use cases for edge compute, we believe all will see benefits from one or all of these 4 V’s.
In fact, as we dive into each V you may start to see other areas within your organization where you can leverage edge capabilities, But first, let’s look at what exactly we mean by the edge.
When talking about edge computing, edge refers to any location along with a network where processing power (CPU) and storage capacity (memory) exists in close proximity to sensors collecting data.
What is the Network Edge in Edge Compute?
The network edge generally refers to a router or similar point in a network where packets are entered and exited.
The network edge can be defined as either Layer 3 or Layer 2, depending on whether you’re looking at it from a MAC-layer perspective or an L3-layer (IP) perspective.
Because edges are different from one technology to another WiFi, Ethernet, MPLS, and so forth it’s important to understand what type of edge you have before discussing deployment strategies.
For example, if you’re working with a Layer 2 switch, then you need to think about how that traffic gets forwarded through your network.
If you’re working with an IP WAN, then other factors come into play regarding how traffic gets routed between sites.
Need for Edge Compute
A common misconception is that edge and IoT are synonymous, But there are several reasons why IoT devices may not need to be directly connected to cloud services.
In particular, while most IoT applications depend heavily on connectivity to carry out their functions remote monitoring and fleet management systems come to mind the performance demands of these applications do not require massive amounts of data processing or storage.
For example, a smart thermostat only needs to send small bits of data (e.g., temperature readings) back to its manufacturer’s servers at regular intervals.
Thus, it can make sense for an IoT device with limited computing power and bandwidth requirements to store its sensor readings locally rather than sending them over a network connection for remote analysis.
Key Factors behind Edge Compute Adoption
According to McKinsey, edge, and IoT combined comprise a US$290 billion market. Because of increasing use cases for both technologies, some cloud providers now offer edge services; Gartner predicts that more than 50% of enterprise applications will have a multi-cloud deployment strategy by 2020.
But there are many other drivers behind its adoption, for example, improvements in end-user experience (UX), thanks to lower latency times.
In addition, edge computing can be used to reduce cost because it allows companies to process data closer to where it’s generated, rather than sending it all back to a central location.
This can result in significant bandwidth savings and power consumption reductions as well as improved network performance with fewer bottlenecks.
It also enables better privacy protection because data doesn’t need to be transferred across networks or stored centrally.
Difference Between Edge Computing and other Computing Models
Edge computing isn’t a new thing. It’s been around for over five decades and has always been used in more complex industrial applications.
Nowadays, edge computing is getting traction in commercial settings because companies are realizing that it gives them faster response times, which brings down their overall costs and improves user experiences.
Edge computing is a bit different from cloud and fog computing.
In these two models, data travels to the cloud and then gets manipulated by software before being sent back to users.
Fog computing moves data through several layers of localized servers to reduce latency and increase reliability while maintaining speed.
So, how do they compare with edge computing? Edge computing differs from other models in that it’s closer to end-users than any other model.
This means that companies can save money by reducing bandwidth costs and improving response times.
However, it also means that companies have to invest in more infrastructure because there are no centralized servers that can be used for processing or storage.
Edge computing is ideal for applications where fast response times are critical or where there’s a need for continuous communication between devices and networks.
Related Article: On-premise Cloud vs Hybrid Cloud: What is the Difference?
How Do You Implement Edge Compute?
It’s easier than you think. The first step to implementing edge computing is to select a cloud service provider that supports edge computing in their infrastructure.
The next step is to choose an off-the-shelf or custom device from one of many companies that support serverless architecture, such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform.
After you make your selection, register with a local Internet Service Provider (ISP) if applicable and start serving your content directly from your chosen location.
If you don’t have a website already set up, it’s time to get started. You can either build your site using open source CMS software like WordPress or purchase hosting services from a third party.
Once you have everything up and running, test it thoroughly by making sure all of your pages load quickly and efficiently from anywhere in the world.
If everything checks out, go ahead and serve up some page views! And finally, take advantage of things like web analytics tools and mobile applications to track how people are interacting with your new edge application.
With real data at hand, you can begin to optimize for maximum performance based on real user needs and feedback.
Related Article: What is an On Premises Cloud? | Traditional Data Centers
Advantages of Edge Computing
Edge computing reduces latency by reducing the distance to data and can be useful for IoT (Internet of Things) or IPC (Industrial IoT) applications where low latency and real-time communication are critical.
Edge Compute also allows for combining massive amounts of data, which then can be processed with artificial intelligence algorithms to make sense of them.
It also helps companies save money because they don’t have to rely on cloud providers as much anymore.
They can use their servers or even rent servers from local service providers, This way they won’t have to pay for bandwidth and storage that they don’t need.
Edge Computing with Amazon ECS
This section details an example architecture for implementing edge computing with Amazon ECS.
In addition to EC2 and ECS, a key component of an ECS architecture is AWS Lambda the serverless computing platform that supports running code without provisioning or managing servers.
Because Lambda functions run in isolation, they are extremely well-suited for running at the edges of our network where it’s not possible to run traditional web servers due to security constraints.
Related Article: What are the Components of AWS Global Infrastructure?
Challenges with Deploying an ECS architecture
While there are many positives to consider in moving to an ECS architecture, it’s important to also consider some of its challenges.
The most common challenge when deploying an ECS architecture is security, Since IoT devices don’t have as much processing power or storage capabilities as a traditional server, they can be more vulnerable to hackers and other cyber threats.
It’s important that these devices are secure from attacks and that they comply with industry regulations such as HIPAA and PCI DSS.
Another challenge is managing large volumes of data produced by sensors, This data can quickly become overwhelming for IT departments without proper tools for analysis and visualization.
If you want to learn more about how edge computing works, check out our guide on what is edge computing.
Disadvantages of ECS architecture
One of the main disadvantages of ECS architecture is that it requires additional hardware and software to effectively execute its tasks.
It can also cause bottlenecks in performance, as most applications have local processing requirements or have a limited capacity for working in parallel with others.
In addition, an end-to-end connection between two parties will not be established unless all intermediaries are properly configured to route data appropriately.
This may lead to latency issues when trying to establish an end-to-end connection between two parties.
This guide was created to give you a broad overview of what edge computing is, and how it differs from IoT.
However, there are many facets to every concept in technology, so we encourage you to research further before making any decisions about your implementation.
The most important thing is that your organization finds a solution that best fits its needs, which may or may not include using edge computing or implementing an IoT system.
Nitin is a professional data Engineer, Who has a Post Graduation in Data Science and Analytics and working in the healthcare sector. Experts in Data analysis, Machine learning, AI, blockchain, Data related tools, and technologies. He is the Co-founder and editor of analyticslearn.com