Familiarity with edge computing and its impact on mobile technologies

Familiarity with edge computing and its impact on mobile technologies

Familiarity with edge computing and its impact on mobile technologies

News unit EMGblog.com: expanding the use of all kinds of smart devices, in addition to The emergence of technologies such as artificial intelligence, machine learning and blockchain has greatly increased the volume of data that is continuously collected and must be exchanged between the user’s device and servers for analysis and processing. Although the speed of information exchange increases dramatically with the fifth generation of telecommunication networks, it will not be enough to respond to this volume of data exchange between all types of smart equipment today with servers located in data centers. This is where edge computing comes into play.

Climbing above the clouds and landing on the edge

Until recently, most companies kept their business-related data and software locally. For this purpose, they bought servers and installed them in special rooms with proper cooling system. Some companies also rented space from a nearby data center to keep their servers and data. With the development of information technology and the development of telecommunications, the conditions gradually changed. More people started working from home. Businesses related to information technology grew rapidly and their offices were set up one after another in different cities and countries. In this way, the purchase and maintenance of servers at the premises of companies and businesses soon lost its justification. For a rapidly growing company, purchasing and maintaining new servers is very difficult and can slow down and limit business development.

Cloud computing services such as Microsoft Azure and Amazon Web Services (AWS) have solved such problems. Companies can rent storage space and processing resources on cloud service platforms according to their needs and easily use more resources along with the development of their business. But like everything else in this world, cloud services have some disadvantages along with the advantages they bring. The main drawback of these types of services is that they are centralized. Large cloud service providers such as Microsoft, Amazon and Google have set up numerous data centers in different parts of the world and provide services through them. But depending on where you are on the planet, there are hundreds or even thousands of kilometers between you and the nearest data center of the service provider from which you receive services. Every time you intend to use cloud services, your request must be sent through optical fibers to the servers located in these data centers and the corresponding response returned to your location. Therefore, the greater the distance between you and the data center of the cloud service provider, the more delay you will experience in receiving the service. Another problem of relying on cloud services is the high dependence of the services on internet communication and the costs associated with providing the appropriate bandwidth for this purpose. A problem that becomes more pronounced especially in areas with unstable, low-speed and expensive internet connection.

To solve the problems related to cloud services, it seems that there is no choice but to repeat history. What will happen when the servers come from the cloud on the edge and are located near the user. An event that appears to be a return to the past, but is actually another step in the evolution of data management and processing systems.

What is edge computing?

In simple words, edge computing is It is about bringing applications and data closer to the users who use them. For large companies and their internally used software, edge computing can mean deploying dedicated servers near their main offices and branches. But in the case of services used by general users, edge computing operations may be performed by mobile phones, wearable devices and other types of smart equipment instead of servers. Performing image processing and face recognition by smartphones or traffic control devices, instead of sending data and performing processing operations through cloud services, can be a simple example of this type of edge computing.

Gartner divides edge computing into a segment. It refers to a distributed processing topology in which information processing operations are performed near the edge of the network—that is, where people and equipment produce or consume information. In centralized systems, it is necessary to transfer the data from the place where it was generated and collected to a remote center and after processing, it is returned to the original place again. Such a system causes a large amount of data to be constantly going back and forth, and many times their processing is accompanied by a noticeable delay. In contrast, edge computing, instead of relying on a central and remote location such as a data center, performs data storage and processing near the same devices that are responsible for data collection.

Takeout food chain centers can be considered as a simple example to understand the concept and application of edge computing. In order to be able to deliver their food to their customers in different parts of the city with less delay and before it gets cold, they open new branches in the areas where the most food orders are received. In this way, the food preparation process is done in a place closer to the customers and the ordered food reaches them faster and fresher. In a similar way, edge computing aims to provide better and faster results by performing data processing near users.

Benefits of edge computing

Using edge computing brings various benefits to end users and businesses, the most important of which are:

Speed ​​up: Edge computing greatly reduces the waiting time to receive a response by eliminating or significantly reducing the round-trip distance between the user and the location of the processing operation. For example, when the user uses routing applications, if the calculations and processes related to choosing the best route are done instead of being done by servers in a large data center thousands of kilometers away, by using the processing power of his smartphone and with the help of information stored in If a local data center is implemented, routing operations can be done much faster and even more accurately.

Reduction of Internet bandwidth consumption: With the expansion of the use of all kinds of smart devices and the increasing development of the Internet of Things (IoT) in recent years, the volume of data that is continuously collected and stored on the platform Internet transfers have increased dramatically. The use of centralized processing systems causes the data to travel a longer path to be processed and thus occupy more bandwidth. This increase in bandwidth consumption imposes a lot of costs on users and businesses and can face serious obstacles to the development of the Internet of Things. In such a situation, edge computing can play a big role in reducing Internet bandwidth consumption by performing processing operations near the same place where data is collected and used.

Security: Data transfer is always associated with security risks. In addition, large data centers and cloud service platforms are special and regular targets for hackers. For these reasons, information in the cloud is not very secure. In edge computing, only some information is sent to the cloud servers and at any moment a smaller part of the data is at risk. Even in some examples of edge computing, no internet connection is needed and no data is sent to the cloud. In this way, less data can be stolen while moving between the user’s device and cloud servers, and if one of the edge equipment is hacked, only part of the data that is stored locally on the same device will be exposed to theft or destruction. .

Reliability: Edge computing increases the reliability and dependability of services. Because unlike cloud computing systems, receiving services dependent on internet connection is not stable and users don’t need to worry about network connection interruption or slow internet. In edge computing, a significant part of the data may be stored in small local data centers or in the internal memory of the user’s smart devices. In this way, access to data and services with much higher reliability will be possible. For this reason, for remote areas and areas with inadequate internet connection, edge computing is a useful solution and is specially recommended.

Cost, scalability and agile development: in computing Centralized and cloud, it is necessary that all data is sent to a data center and processed there. Therefore, the growth and expansion of services requires the development of the data center, the promotion of stability and the increase of Internet bandwidth. Things that can be very time-consuming, troublesome and expensive. While with edge computing, companies can quickly increase their data processing and storage capacity as needed with a combination of smart mobile devices, IoT equipment, and small local data centers. The development of edge computing services by adding this type of equipment requires much less internet bandwidth.

Disadvantages of edge computing

Like any other technology, edge computing also brings various disadvantages along with all the mentioned advantages. Some of these disadvantages are inherent and others are related to the emerging nature of this technology and may be diminished over time or completely removed.

Security: Edge computing brings its own risks to information. The very high variety of smart devices that can participate in edge computing creates challenges to provide integrated information security in these devices. While a centralized system or a cloud platform can be secured in a better and easier way by using specialized information security teams and special purpose equipment.

Occupation of resources in edge equipment: And data processing in edge equipment will mean occupying hardware resources in these equipment. To store data, it is necessary to occupy a part of the memory of these devices, and as a result, the need for larger internal memory will increase in all kinds of edge smart equipment. Also, to process these data at the edge, the processor of these equipments is used more and their energy consumption increases.

Data loss: In edge computing, the data needed for User current and moment-to-moment decisions are not applicable, are given lower priority, or may be ignored altogether. If this data is of high importance. For example, when a self-driving car is driving alone on an empty road, it may seem useless to store data from sensors and cameras. While this seemingly insignificant data can provide useful information regarding the road conditions and the condition of the vehicle in that particular situation. This information may be useful in the future for the same car or other self-driving cars on that road.

Maintenance: Edge computing is a distributed system that is more Concentrated consists of more components and more complex connections. Maintenance of such systems will naturally be more difficult and costly.

Applications of edge computing

One of the main fields of application of edge computing is the Internet of Things. In fact, perhaps the Internet of Things can be considered the most important factor in the development of edge computing. Today, all kinds of smart equipment in homes, workplaces and city environments need to be connected to the Internet to provide services. These equipments are usually engaged in collecting data and sending it to cloud platforms or, on the contrary, they are receiving information from cloud platforms. With the increasing use of such equipment around the world, resorting to distributed and local solutions for computing the huge amount of data related to them seems obvious and inevitable. Therefore, it is expected that the biggest field of application of edge computing is the Internet of Things or IoT, which includes smart cities, smart homes, self-driving cars, live video streaming services, security systems, remote work platforms and smart medical services.

Among other fields of application of edge computing, cloud gaming and virtual reality (VR) platforms can be mentioned. The main reason for the failure of cloud game platforms such as Google Stadia can be found in the centralization of these systems and their complete dependence on high-speed, stable and low-latency internet connection with servers located in the respective data centers. A problem that may have the most obvious solution is the use of edge computing. Bringing cloud game servers to small data centers close to the user, along with the improvement and promotion of communication technologies with the development and expansion of 5G technology, will finally provide the necessary conditions for the flourishing and success of this type of service. In this way, it is possible that in the not too distant future we will see the possibility of playing high-end games with the help of cloud services and in one piece on all kinds of equipment, including smartphones, virtual reality glasses and televisions.

Mobile Edge Computing (MEC)

Mobile Edge Computing, also known as “multi-access edge computing”, refers to a type of edge computing. which takes place in the outermost part of the network. That is, near the place where mobile devices and their applications are generating or consuming data. The main goal in mobile edge computing is to speed up increase responsiveness and reduce network traffic. In fact, if the computing operations related to mobile devices are carried out near the intersection of mobile phone networks with the Internet infrastructure, this huge amount of data is prevented from entering the main Internet network and the speed of providing services in mobile applications MEC technology is designed to be installed at mobile network stations or other base stations within the range of connectivity. These networks should be implemented on the Internet platform.

Technical standards related to this technology are being developed by the European Telecommunications Standards Institute and companies including HP, Huawei, Cisco, Motorola Mobility, IBM, Nokia, Intel and AT&T are involved. Since the MEC architecture and its standards are newly introduced and under development, its exploitation and practical use on a large scale has not been done yet. Some conceivable application areas for this technology are content delivery, mobile big data analysis, computational offloading, edge video caching, collaborative computing, connected cars. to networking, smart places, healthcare and healthcare, indoor positioning, etc. Amazon’s AWS Wavelength service can be considered the first commercial product based on MEC technology that has been used on a large scale. Services in the web services platform Amazon (AWS), this They provide the possibility to run mobile applications on the edge of 4G and 5G networks of specific operators and respond to the relevant users with very little delay.

Using mobile edge computing (MEC) technology, mobile operators can offer new computing services locally to specific subscribers or special groups of subscribers. Also, the use of this technology will reduce the signal load on the operators’ network core and provide the possibility of hosting applications and services in a cheaper way. Application developers and multimedia content providers will also be able to take advantage of proximity to mobile phone users and use live network information.

With the development of technology and the evolution of the Internet of Things (IoT), the business need for more processing capacity and in a location closer to the location of data collection is increasing day by day. Especially in the field of agriculture, oil, gas and wind energy industries, which are usually located in rural and sometimes remote areas. The expansion of the use of various types of sensors to collect huge amounts of data, especially in areas that are limited in terms of high-speed Internet connectivity, will greatly increase the need and demand for mobile edge computing to enable in-situ and image data analysis. Provide quick action based on received data.

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