Srikanth
Edge computing is computation at network level instead of the central nodes. Traditionally, the computation power of any computer is present at the physical location of computer itself. With introduction of new technologies. The focus is on transferring this computation power to the far end of Internet. This process of optimizing cloud computing system is defined as Edge computing.
Advantage of Edge Computing:
1) Quantity of data transferred within a network or from one network to another network is reduced significantly. This is the most common advantage of moving towards Edge computing. Many organizations, who deals with huge chunk of data has seen this as better option over physical computation as this can provide them with huge gains in longer runs.
2) Response time is reduced significantly. The significance of Edge computing relies upon the time in which it performs any computation and shows the result to user, if required. This very concept can fail if the response time is not matched to the one that traditional solutions offer. In fact, the user experience will deteriorate if the time is not taken into consideration.
3) Internet of Things have seen a major boost. With the rapid development of mobile internet and Internet of Things applications, the conventional centralized cloud computing is encountering severe challenges, such as high latency, low Spectral Efficiency (SE), and non-adaptive machine type of communication. Motivated to solve these challenges, a new technology is driving a trend that shifts the function of centralized cloud computing to edge devices of networks. Several edge computing technologies originating from different backgrounds to decrease latency, improve SE, and support the massive machine type of communication have been emerging. This paper comprehensively presents a tutorial on three typical edge computing technologies, namely mobile edge computing, cloudlets, and fog computing. The standardization efforts, principles, architectures, and applications of these three technologies are summarized and compared. The real-time buffering of data is reduced for Edge devices that are directly or indirectly related to a smart IoT device. It helps in preventing parts from failing and improve performance on various parameters.
Let us focus on the last point as this is the one which is driving the current market. There are basically 3 typical edge computing solutions for IoT. Let us focus on them one by one to be able to take a deep dive into the concept of edge computing.
- Cloudlet
This is nothing but a mobility enhanced small scale cloud data-center which is introduced to improve the end to end responsiveness between devices and networks. The main purpose of the cloudlet is supporting resource-intensive and interactive mobile applications by providing powerful computing resources to mobile devices with lower latency. User equipment (UE) can access the computing resources in the nearby cloudlet through a one-hop high-speed wireless local area network. Cloudlets represent the middle tier of the 3-tier hierarchy architecture (mobile device layer, cloudlet layer, and cloud layer) to achieve crisp response time.
- Mobile Edge Computing
Commonly referred to as MEC, this is defined as a technology that provides an IT service environment and cloud-computing capabilities at the edge of the mobile network.
Owing to its advanced features, such as low latency, proximity, high bandwidth, and real-time insight into radio network information and location awareness, MEC enables a large number of new types of applications and services for multiple sectors, such as consumer, enterprise, and health. MEC is deemed to be a promising solution for handling video streaming services in the context of smart cities. Video streams from monitoring devices are locally processed and analyzed at a MEC server to extract meaningful data from video streams. The valuable data can be transmitted to the application server to reduce core network traffic. Augmented reality (AR) mobile applications have inherent collaborative properties in terms of data collection in the uplink, computing at the edge, and data delivery in the downlink. AR data require low latency and a high rate of data processing in order to provide the correct information depending on the location of the user. 
- Fog Computing
Fog computing can be defined as a system-level horizontal architecture that distributes resources and services of computing, storage, control and networking anywhere along the continuum from the cloud to things. Fog computing is different from edge computing and provides tools for distributing, orchestrating, managing, and securing resources and services across networks and between devices that reside at the edge. Edge architecture places servers, applications, and small clouds at the edge. Fog jointly works with the cloud, while edge is defined by the exclusion of cloud.
Challenges ahead for edge computing
With the advent of every new technology, a lot of challenges come forward. But according to me these challenges drive forward the technology to excellence if taken care of with due diligence. The same is the story with edge computing. The first and foremost is to provide a cost-efficient solution by introducing network slicing. Despite the evident attractive advantages in centralized cloud computing, network slicing comes with several severe challenges when applied in edge computing. First, the conventional creation of network slice instance is mainly business driven. The network slicing solution mainly addresses the requirements of different services, which do not highlight the characteristics of edge computing on network slicing creation. Another big challenge is to integrate the solutions with capabilities provided by big data. One such field is data mining. So, networks should be made more proactive to address this problem.
Conclusion
As IoT becomes more pervasive, edge computing will do the same. The ability to analyze data closer to the source will minimize latency, reduce the load on the internet, improve privacy and security, and lower data management costs. The cloud will continue to play a critical role in aggregating important data and performing analyses on this massive set of information to glean insights that can be distributed back to the edge devices. The combination of edge and cloud computing will help you better manage and analyze your data and significantly increase the value of your IoT efforts. It is a mistake to presume that edge computing is a phenomenon which will eventually, entirely, absorb the space of the public cloud. Indeed, it’s the very fact that the edge can be visualized as a place unto itself, separate from lower-order processes, that gives rise to both its real-world use cases and its someday/somehow, imaginary ones. With sufficient security measures implemented, this technology can explore various new roads for a more technologically advanced society and drive various existing technologies to new models of innovations. Big data and IoT are the ones which can directly benefit in market share if edge computing keeps up pace with changing market conditions.
