Why Edge Computing Systems with Kubernetes?

Technology moves extremely fast, with certain solutions within tech experiencing explosive growth as new apps become built upon them. As a result of this technological renaissance, key industries like healthcare, entertainment, and retail have witnessed strong growth with the implementation of advanced solutions. One of the most impactful solutions in this new age of tech is edge computing, specifically edge computing systems with Kubernetes.

Edge computing refers to a framework that brings applications and services closer to users, improving business interactions with data. Before edge computing, data generated from devices like smartphones and tablets would be transferred to the cloud or central data centers. As the data center processes data, businesses are expected to wait for insights. While secure, this process would cause bandwidth and latency issues so severe that edge computing became a necessity in areas where fast data was vital.

Since its inception in the 1990s, edge computing has become a mainstay in industries that require agility and reliability. Edge computing not only increases bandwidth while reducing latency, but also encourages faster data analysis (thus deeper business insights), faster service response times

With the amount of data we generate daily quickly ballooning past previous numbers, edge computing and data agility are of the utmost importance. The emphasis on edge computing isn’t just wishful thinking either: The Linus Foundation estimates that the global edge computing infrastructure market will be worth up to $800 billion by 2028. 

Assisting in the operations of industries like healthcare and retails while streamlining the use of technologies like Internet of Things (IoT) and CDNs, edge computing plays an important role in ushering in a data-driven world. Edge computing can reduce the time needed to make strong decisions, with Kubernetes quickening this time. 

Common Edge Computing Challenges

Because of its proximity to data sources, edge computing systems have to be highly distributed. Despite the high flexibility and agility attributed to this edge computing characteristic, the distributed form of this framework is a contributor to many of edge computing’s challenges, including:

Bandwidth and Connectivity Issues

Edge computing in urban environments is a no-brainer, driving many of the self-driving vehicles, environmental sensors, and more to make a truly “smart city”. But what about remote locations like farms, factories, and places overseas? With a system highly dependent on internet connectivity, how can edge computing systems ensure that they’re available worldwide?

Ensuring a strong bandwidth and connectivity to data endpoints and data centers is paramount to the success of edge computing systems. With the amount of data sources increasing, organizations managing these systems have the unique challenge of creating a highly available network. In addition, sizing of these networks has to be precise, with bigger, more resource-hungry networks requiring a higher bandwidth.

A common occurrence with bigger edge computing networks is a lack of resources, limiting the potential of these systems while clogging data pipelines. It’s important to keep resource requirements within your scope and work towards dismantling limitations that might affect the bandwidth and operations of these valuable networks.

Application and Service Availability

An edge computing system is only as important as the parts that it hosts. While the end data warehouses are typically emphasized because of the role they play in housing data, each component within the edge computing infrastructure plays an equally important role.

From edge components like servers that process data to device components that intake data like sensors, controllers, and other physical devices, and even the apps that run these devices, each part of the edge network is critical to operations. The problem is, availability with so many moving parts is a hassle and requires full teams to manage. This availability is threatened once more remote locations are introduced, more devices are added to the mix, and more unexpected traffic spikes that generate tons of data occur.

Security Issues

Security issues are always there, especially within highly distributed frameworks. The security of edge computing systems must be maintained on two fronts: virtual and physical.

Virtual security is something that most organizations deal with, implementing initiatives like role-based access control (RBAC), firewalls, and more to protect valuable assets. Where edge computing networks can be leveraged is on the physical front. Physical devices like phones and tablets can be tampered with and even stolen, disrupting the flow of information to edge servers. Edge servers themselves require around the clock security and maintenance from time to time. The data warehouse at the end of these systems also require the same, if not stronger security than edge servers.

Scaling to Meet Device Demands

As edge computing systems grow with the addition of devices, organizations must find ways to scale and meet this overwhelming demand. Especially in times with unpredictable traffic, scaling edge systems is paramount to capturing valuable data. In addition, scaled systems can accurately distribute resources to devices and servers that need it more.

Additional devices and any other source of data also pose the issue of management: the bandwidth of edge computing systems might be expanded, but what about the bandwidth of their management teams?

Kubernetes Edge Computing - The Perfect Match

As the title suggests, Kubernetes and edge computing are a match made in heaven, the closest thing we have to a technological Batman and Robin situation. But why? Is Kubernetes just another buzzword in the scope of edge computing? Edge computing Kubernetes seemingly solves many of the framework’s challenges, including:

Bandwidth Flexibility

A huge concern for edge computing is whether strong bandwidth is achievable, especially within remote locations.

Deploying edge computing with Kubernetes allows organizations to mobilize containerized edge applications that are lightweight and flexible. Typically in the form of microservices, these containerized applications contain all of the software components vital to operations, suitable for edge computing situations because of their weight and independence. The result of combining edge computing systems with Kubernetes are extremely flexible deployments that operate in even the most remote locations. 

Network bandwidth itself can also be improved by Kubernetes, allowing you to set resource limitations and bandwidth utilization limits within a Kubernetes cluster and on individual pods. The result is an intuitive edge computing system that works within its own sufficient limitations.

High Service Availability and Reliability 

With the availability of edge applications being anything but guaranteed, Kubernetes helps tackle the issues of uptime that plagues these networks.

A built-in feature of Kubernetes clusters and nodes is self-healing. Everything from health monitoring to cluster restarting and automated healing are a part of this package. Once clusters become inoperable, Kubernetes clusters automatically isolate those clusters and replace them with one that’s operation ready. In the meantime, the isolated cluster gets repaired, ready for deployment once another cluster isn’t operating. The result is a highly resilient and available edge computing system, removing the threat of single point of failure.

Security Through Kubernetes

In a virtual sense, Kubernetes offers a lot of desirable features to edge computing systems. From RBAC and network policies, to auditing and specific secret APIs, Kubernetes itself is a secure tool. Added onto your own securities initiatives, and you’ll find that your edge computing network is in good hands.

In a physical sense, Kubernetes can’t do much.

Scaling and Management Requirements

As your edge system grows in capacity, edge devices and source devices become hard to manage and monitor. Kubernetes is the perfect tool for facilitating the management and scaling of both. 

Kubernetes is able to scale as needed, with cluster autoscaling being one of the container orchestration tool’s biggest perks. As the amount of devices increases, Kubernetes is able to scale the underlying edge applications to keep up with demand and usage. In addition, with the amount of data generated from an edge device alone, Kubernetes is able to deploy highly scalable databases and analytics tools, strengthening real time insights.

For management, Kubernetes clusters can be managed through a central control plane, allowing for centralized management of your dependent edge apps.

Edge Kubernetes Considerations

Kubernetes in edge computing seems too good to be true at times, and it can be!

The silver lining in Kubernetes’ use within edge computing lies within reasons such as:

• Configuration and management complexity
• Resource overconsumption that might affect edge server performance
• The lack of specific edge features, specifically geographical

However, these challenges can be addressed with the use of lightweight versions of Kubernetes, specifically k3s and microK8s.

Deploying Edge Kubernetes with Lyrid

Edge computing systems are a mainstay in much of our lives. From enhancing our user experience with smart devices and smart cars, to changing the way we go about patient care in healthcare systems, edge computing is pushing the boundaries of data speed and accuracy.  

Despite advancements in the space, edge computing suffers from additional devices and bandwidth issues, struggling to ship out services to remote locations and in times of unpredictable traffic. Using edge computing on Kubernetes solves many of the framework’s problems, but even then using Kubernetes is not for the faint of heart.

That’s where Lyrid comes in.

Lyrid Managed Kubernetes is a managed service that offers the utilities and benefits of Kubernetes, without the headache of configuration and complexity. Our managed Kubernetes service is equipped with features such as: 

• Resource autoscaling and distribution
• Kubernetes cluster management API
• Cluster scaling based on workload and traffic
• Simple deployment via Helm charts
• End to end visibility through a single pane of glass

And more!

Whether you’re looking to launch your next Kubernetes edge computing system or want to try out Kubernetes for the first time, Lyrid Managed Kubernetes is a great way to get started!

If you want to learn more about this service, book a meeting with one of our experienced product specialists! We’re eager to help you and excited to spread Kubernetes to more tech!