EDGE COMPUTING                        

WHAT IS EDGE COMPUTING?

The future of software will be managed companies like Amazon, Microsoft, and Google have proven to us that we can trust them with our personal data.

Now I introduce you with the ‘edge’ computing.

Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed to improve response times and save bandwidth. It is a topology rather than a technology.

The origin of edge computing lies in content delivery network that were created in the late 1990s to serve web and video content from edge servers that were deployed close to users.

Speed :-

Edge computing brings analytical computational resources close to the end users and therefore can increase the responsiveness and throughput of applications. A well-designed edge platform would significantly outperform a traditional cloud-based system. Some applications rely on short response times, making edge computing a significantly more feasible option than cloud computing. The Edge Computing is likely to be able mimic the same perception speed as human.

FOUR DRIVING FEATURE OF EDGE COMPUTING :

Saving Bandwidth

The proliferation of smart devices means we’re creating an extraordinary amount of data. But not all of that data is critical. Revisiting our security camera example, if you have multiple cameras on a site, and each one is constantly streaming data to the cloud, then that’s using a lot of bandwidth for potentially not very useful data.

Reducing latency

Another advantage of devices being able to sort critical data from the not-so-critical data is a reduction in latency (i.e., the time it takes to send data and receive a reply).

Enhancing security and privacy

Edge computing reduces the amount of data that has to travel over a network, which is an obvious bonus from a security perspective. There’s also the fact that data is distributed (in this case, located on multiple user devices) as opposed to being stored in one place.

Scalability

In cloud computing architecture, the data needs to be forwarded to a centralized datacenter. Most at times modifying or expanding this datacenter can be costly. However the edge can be used to scale your own IoT network without needing to worry about the storage requirements. Moreover in just a single implantation IoT devices can be deployed here.

DISADVANTAGES OF EDGE COMPUTING :

1. Security
Ensuring adequate security can be often challenging in a edge distributed environment. Due to the fact that data processing takes place at the outside edge of the network there are often risks of identity theft and cyber security breaches. Additionally whenever a new IoT device is added here, it will increase the opportunity for the attackers to infiltrate the device.

2. Incomplete Data
Edge computing only process and analyze partial sets of information. The rest of the datas are just discarded. Due to this the companies may end up loosing lots of valuable informations. Therefore, before using edge computing, the organizations must decide what type of informations they are willing to loose. 

3. More Storage Space
Edge computing does take a considerably higher storage space on your device. Since the storage devices are becoming more compact this will not actually be a problem. However it is a point to remember in when developing an IoT device.

4. Investment Cost
Implementing an edge infrastructure can be costly and complex. This is due to their complexity which needs additional equipment and resources. In addition to that the IoT device with the edge computing comes with the need of more local hardware for them to function. This can overall lead to more efficiency but a significant investment is required. 

5. Maintenance
Unlike a centralized cloud architecture, edge computing is a distributed system. Which means that there are more various network combinations with several computing nodes. This requires higher maintenance cost than a centralized infrastructure. 

 

 PRIVACY AND SECURITY :-

The distributed nature of this paradigm introduces a shift in security schemes used in cloud computing. In edge computing, data may travel between different distributed nodes connected through the Internet and thus requires special encryption mechanisms independent of the cloud. Edge nodes may also be resource-constrained devices, limiting the choice in terms of security methods. Moreover, a shift from centralized top-down infrastructure to a decentralized trust model is required. On the other hand, by keeping and processing data at the edge, it is possible to increase privacy by minimizing the transmission of sensitive information to the cloud. Furthermore, the ownership of collected data shifts from service providers to end-users