A practical guide to edge computing: what it is, where it helps, how to secure it, and why it’s vital for data growth, analytics, and digital strategy.
Over the years, we have published a lot of content on edge computing and related topics. This omnibus aims to bring you all of those valuable, informational content together so that you can access it all from a one-stop solution. Each section is linked to a detailed blog where you can find more information about the said topic. Here's what you need to know about edge computing. Let's start from the basics.
What is Edge Computing?
Edge computing refers to the network paradigm that brings cloud computing services such as network and storage closer to the consumer. Although edge computing is a relatively new technology, it has taken the cloud computing world by storm.
The origin of edge computing can be traced back to the 1990s when Akamai launched its content delivery network. It is now among the largest computing platforms but in order to set it up, nodes were scattered geographically to be closer to end-users. Edge computing expanded on this concept and enabled the nodes to perform computational tasks.
Best Practices in Edge Security
Edge security is decentralized security. Each device or sensor on the edge network has its risks and vulnerabilities. They also have dedicated security measures. These measures do not necessarily have linkage to that of a primary cloud environment or data center.
Edge computing's nature is the leading cause of security concerns, but there might be a hidden security benefit. The idea behind edge computing is that data travels short distances for processing. Thus, fewer interception threats at points of data transmissions. As more and more data resides on the edge of the network, the central data centers are less likely to face security issues. Therefore, keeping core operating systems safe and sound.
The benefit mentioned above comes with a wide array of security challenges. Tackling these hurdles first-hand should be a priority for all. Every member of the network has to implement some best practices to maintain edge security, such as:
- End-to-end encryption and perimeter security
- Threat detection and prevention
- Patching cycles
- Vulnerability management
Future Prospects and Problems of Edge Computing
Edge solutions are considered great for data growth and management. But this doesn't mean that the enterprises are without concern. Here is what the present and future prospects and challenges of edge looks like.
It is no secret that IoT and devices of similar nature create a lot of data. As a result, organizations are more inclined to move data storage and computing capabilities closer to their devices. This move will help reduce the cost of utilizing cloud facilities or a data center and get the analysis done faster through edge computing.
Edge computing also becomes the need of the hour because many companies have a large influx of data. Most of these enterprises are either unequipped to store the data or process them fast enough to take any necessary action out of it. With the aid of Machine learning and AI within edge computing, collection, processing, storage, and action on data is possible at a much faster and efficient rate.
A large number of enterprises are now aware of the many benefits that come with utilizing edge computing for their operations. They are also willing to implement it. But certain factors prevent them from being fully on board.
Operational costs and security are the two major elements that affect decision-making in moving to edge solutions. Furthermore, many organizations, especially small scale ones, have budget constraints when it comes to data collection, processing, and management through edge. Some IT organizations and their decision-makers are also concerned about their operations' secure nature and whether adopting IoT devices for edge solutions would make their data vulnerable.
Role of Edge in Data Analytics
Data analytics in the world of edge computing is better known as edge analytics. It collects and analyzes data from the first touchpoint itself rather than after being transferred to the cloud. So, data is accessed right at the device where information is first generated.
Additionally, it allows the analysis to take place within the device where data generation takes place. In a way, the cloud is not part of the process at all, and embedded IoT devices can do all the work. Edge analytics is not here to replace data analytics in the cloud but rather supplement it.
Multi-Access Edge Computing
Formerly known as mobile edge computing, Multi-Access Edge Computing (MEC) is a network architecture concept that brings technology resources such as cloud computing and IT service capabilities closer to the edge of networks, closer to the end-user. This means that along with running applications, tasks such as data processing and storage in MEC can happen at the edge itself, instead of distant data centers, thereby reducing latency, network congestion, and bandwidth usage significantly.
Some of the main benefits are:
- Super-low latency
- Improved Potential for IoT
- Data localization Edge computing is evolving and VEXXHOST's cloud services are fully equipped to support edge devices. Our clouds are based on OpenStack, making the environments free from licensing fees or vendor lock-ins. For many of our clients, private clouds are the preferred choice because of their highly scalable and secure nature.
Speaking of private clouds, you can now run on a fully agile and customized cloud from VEXXHOST, with no licensing fees and smooth 2-week migration. In fact, we're ready to put our money where our mouth is. We're so confident in being able to save you at least 20% or more on your current cloud infrastructure expenditure that if proven wrong- we'll give you $1,000 credit to our public cloud.
Excited? Find out more.
