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Edge Computing Interview Questions
Edge computing is a networking philosophy focused on bringing computing as close to the source of data as possible in order to reduce latency and bandwidth use. In simpler terms, edge computing means running fewer processes in the cloud and moving those processes to local places, such as on a user’s computer, an IoT device, or an edge server. Bringing computation to the network’s edge minimizes the amount of long-distance communication that has to happen between a client and server.
Although edge computing does not replace cloud computing technology, the emergence will certainly reduce and impact cloud computing. On the other hand, edge computing will enhance cloud computing technology by providing less complex solutions for handling messy data. Both these technologies have its own purpose and use, below we have discussed several points that distinguish between edge and cloud computing :

Edge Computing Cloud Computing
It is good to be used for those organizations that have a limited budget to invest in financial resources. So, mid-level organizations can use edge computing. It is generally recommended for processing and managing a high volume of data that is complex and massive enough. Thus, such organizations that deal with huge data storage use cloud computing.
It can use different programming languages on different platforms, each having different runtime. Cloud Computing works for one target platform using one programming language only.
Security in edge computing needs tight and robust plans such as advanced authentication methods, network security, etc. It does not need high and advanced security methods.
It processes time-sensitive data. It process that data that is not driven by time, i.e., not time-driven.
It processes data at remote locations and uses the Decentralization approach. It processes and deals with data at centralized locations by using a centralized approach.
Organizations can indulge in edge computing with the existing IoT devices, advance them, and use them. There is no need to purchase new devices. For advancement, existing IoT devices need to be exchanged with the new ones that will cost more money and time.
Edge Computing is the upcoming future. Cloud Computing is the currently existing technology.
For Internet devices, the network edge is where the device, or the local network containing the device, communicates with the Internet. The edge is a bit of a fuzzy term; for example a user’s computer or the processor inside of an IoT camera can be considered the network edge, but the user’s router, ISP, or local edge server are also considered the edge. The important takeaway is that the edge of the network is geographically close to the device, unlike origin servers and cloud servers, which can be very far from the devices they communicate with.
Consider a building secured with dozens of high-definition IoT video cameras. These are "dumb" cameras that simply output a raw video signal and continuously stream that signal to a cloud server. On the cloud server, the video output from all the cameras is put through a motion-detection application to ensure that only clips featuring activity are saved to the server’s database. This means there is a constant and significant strain on the building’s Internet infrastructure, as significant bandwidth gets consumed by the high volume of video footage being transferred. Additionally, there is very heavy load on the cloud server that has to process the video footage from all the cameras simultaneously.
Now imagine that the motion sensor computation is moved to the network edge. What if each camera used its own internal computer to run the motion-detecting application and then sent footage to the cloud server as needed? This would result in a significant reduction in bandwidth use, because much of the camera footage will never have to travel to the cloud server.
Additionally, the cloud server would now only be responsible for storing the important footage, meaning that the server could communicate with a higher number of cameras without getting overloaded. This is what edge computing looks like.
Edge computing can be incorporated into a wide variety of applications, products, and services. A few possibilities include:
* Security system monitoring : As described above.

* IoT devices : Smart devices that connect to the Internet can benefit from running code on the device itself, rather than in the cloud, for more efficient user interactions.

* Self-driving cars : Autonomous vehicles need to react in real time, without waiting for instructions from a server.

* More efficient caching : By running code on a CDN edge network, an application can customize how content is cached to more efficiently serve content to users.

* Medical monitoring devices : It is crucial for medical devices to respond in real time without waiting to hear from a cloud server.

* Video conferencing : Interactive live video takes quite a bit of bandwidth, so moving backend processes closer to the source of the video can decrease lag and latency.
Cost savings : As seen in the example above, edge computing helps minimize bandwidth use and server resources. Bandwidth and cloud resources are finite and cost money. With every household and office becoming equipped with smart cameras, printers, thermostats, and even toasters, Statista predicts that by 2025 there will be over 75 billion IoT devices installed worldwide. In order to support all those devices, significant amounts of computation will have to be moved to the edge.
Performance : Another significant benefit of moving processes to the edge is to reduce latency. Every time a device needs to communicate with a distant server somewhere, that creates a delay. For example, two coworkers in the same office chatting over an IM platform might experience a sizable delay because each message has to be routed out of the building, communicate with a server somewhere across the globe, and be brought back before it appears on the recipient’s screen. If that process is brought to the edge, and the company’s internal router is in charge of transferring intra-office chats, that noticeable delay would not exist.
Similarly, when users of all kinds of web applications run into processes that have to communicate with an external server, they will encounter delays. The duration of these delays will vary based upon their available bandwidth and the location of the server, but these delays can be avoided altogether by bringing more processes to the network edge.
New functionality : In addition, edge computing can provide new functionality that wasn’t previously available. For example, a company can use edge computing to process and analyze their data at the edge, which makes it possible to do so in real time.
To recap, the key benefits of edge computing are :
* Decreased latency
* Decrease in bandwidth use and associated cost
* Decrease in server resources and associated cost
* Added functionality
There are the following disadvantages of edge computing :
* Edge Computing requires more storage as data will be placed and processed at different and various locations.

* As in edge computing, data is kept on distributed locations, and security becomes a challenging task in such an environment. It often becomes risky to identify thefts and cybersecurity issues. Also, if some new IoT devices are added, it can open gates for the attackers for harming the data.

* It is known that edge computing saves many expenses in purchasing new devices, but edge computing is also expensive. It means the cost is too high.

* It needs advanced infrastructure for processing data in an advanced way.

* However, edge computing fails to pool resources in a resource pool. It means it is not capable of performing resource pooling.

* It has a limit to a smaller number of peripherals only.
5G needs edge computing for two reasons :

1) 5G will rely on edge computing to meet latency requirements of 5G applications
2) edge computing will help cultivate an ecosystem of applications that also need 5G.
Autonomous Vehicles : GE Digital partner, Intel, estimates that autonomous cars, with hundreds of on-vehicle sensors, will generate 40 TB of data for every eight hours of driving. Therefore, wheels—edge computing plays a dominant role. Sending all the data to cloud is unsafe and impractical. The car immediately response to the events which has valuable data when coupled into digital twin and performance of other cars of its class.

Fleet Management : Let’s example considering a trucking company, the main goal is to combine and send data from multiple operational data points like wheels, brakes, battery , etc to the cloud. Health key operational components are analysed by the cloud. Thus,essentially a fleet management solution encourages the vehicle to lower the cost.
* This technology increases the efficient usage of bandwidth by analyzing the data at edges itself unlike the cloud which requires transfer of data from the IOT requiring large bandwidth, making it useful to be used in remote location with minimum cost.

* It allows smart applications and devices to respond to data almost at the same time which is important in terms of business ad self driving cars.

* It has the ability to process data without even putting on a public cloud, this ensures full security.

* Data might get corrupt while on an extended network thus affecting the data reliability for the industries to use.

* Edge computation of data provides a limitation to the use of cloud.
Edge Computing is more specific towards computational processes for the edge devices. So, Fog Computing includes edge computing, but would also include the network for the processed data to its final destination.
* IOT (Internet Of Things)
* Gaming
* Health Care
* Smart City
* Intelligent Transportation
* Enterprise Security
The Internet of Things (IoT) refers to the process of connecting physical objects to the internet. IoT refers to any system of physical devices or hardware that receive and transfer data over networks without any human intervention. A typical IoT system works by continuously sending, receiving, and analyzing data in a feedback loop. Analysis can be conducted either by humans or artificial intelligence and machine learning (AI/ML), in near real-time or over a longer period. 
If something is referred to as smart, that generally implies IoT. Think of self-driving cars, smart homes, smartwatches, virtual and augmented reality, and industrial IoT, for example.
Edge computing takes place at or near the physical location of either the user or the source of the data. By placing computing services closer to these locations, users benefit from faster, more reliable services with better user experiences, while companies benefit by being better able to support latency-sensitive applications, identify trends, and offer better products and services.
Edge computing is one way that a company can use and distribute a common pool of resources across a large number of locations to help scale centralized infrastructure to meet the needs of increasing numbers of devices and data.
Edge devices are physical hardware located in remote locations at the edge of the network with enough memory, processing power, and computing resources to collect data, process that data, and execute upon it in almost real-time with limited help from other parts of the network.
An IoT device is a physical object that has been connected to the internet and is the source of the data. An edge device is where the data is collected and processed.
Edge devices can be considered part of the IoT when the object has enough storage and compute to make low latency decisions and process data in milliseconds.

The terms IoT device and edge device are sometimes used interchangeably.
There are many use cases which involve edge computing, from virtualised RAN to cloud gaming.

* Retail
* Farming
* Smart grid
* Smart homes
* Cloud gaming
* Manufacturing
* Content delivery
* Traffic management
* Autonomous vehicles
* Predictive maintenance
* In-hospital patient monitoring
* Virtualised radio networks and 5G (vRAN)
* Remote monitoring of assets in the oil and gas industry
What edge computing is basically, seems ambiguous and varies depending on who you ask. This post focuses on types of Edge Computing.

There are four types of edge computing.
Cloud : The first type of edge computing is Cloud. It refers mainly to large data centers run by cloud companies such as AWS, Azure, and GCP. Which may include VMware Cloud on AWS and other cloud or provide for too. The cloud’s key attributes centralized and runs at scale. The downside is you have very high availability of infrastructure. And also, access to a lot of facilities, and an unlimited amount of money.
The downside clustered. There is no guarantee of network access to sensors or computers at the edge and high latency. Internet traffic to and from the cloud also most likely entails an expense.
Device Edge : The second type of edge computing is the Device edge. This consists of one or more tiny servers and also known as a nano DC. It would consist only of one or a few customize and would have limited processing power. Databases in this segment would likely not mounted on a rack. And also, we would need to be able to run without refrigerating.
These are also located in places that are not typically associates in data centers. Such as warehouses, wind generators, and durable to cope with harsh weather. They positioned right next to IoT sensors, so there limit issues in latency, bandwidth, or communication. The downside is that these small devices can only have low power and facilities.
Compute Edge : The third type of edge computing is the Compute edge. It is also a micro-DC. Anyway, it is a small data center consisting of everything from a few up to many server racks. They are usually located near or next to IoT devices, and may also be needed for reasons of local enforcement. The point is that these server farms have ventilation and so on, and have regular servers installed on racks.
In these data centers, you will have quite a lot of resources. If, not the same variety of facilities and capabilities as in the cloud. One advantage is that system latency at the edge would be less relative to the cloud. So network bandwidth should be higher but more efficient.
Sensor : The final type of edge computing is the sensor. IoT sensors are instruments that either collect data or monitor things. Some sensors around the world like a surveillance camera, clock, light bulb, etc. Depending on the bandwidth, latency, or communication requirements, they would usually not include any computing power in themselves. And also, it will instead communicate with the system edge, network edge, or cloud.