Edge Computing is revolutionizing the internet and is a hot topic in IT circles and businesses. It entails bringing computing resources and data storage closer to data sources to improve response times, save bandwidth, and unlock new business opportunities across various sectors, such as manufacturing, retail, healthcare, and telecommunications.
Examples include network-dependent applications such as live interactions, augmented reality, connected cars, autonomous driving, and manufacturing. In an era where consumers and businesses demand the shortest possible time between question and answer, Edge Computing is the only way to shorten the time it takes to deliver this information. Edge Computing accelerates this timeframe by reducing latency, processing data even with limited bandwidth, cutting costs, and ensuring data sovereignty and compliance. It also poses some hurdles, but they can be overcome through tailored use of Kubernetes.
Edge Computing is On the Rise – Companies are Turning to Kubernetes
The radically new way companies can create and process data at the edge will create new markets – and is already doing so. The global Edge Computing market is expected to grow massively in the coming years. One forecast predicts that the market volume will increase from three billion US dollars in 2020 to twelve billion US dollars in 2028. The key question is: which operational models and technologies will be able to tap into this potential effectively?
Edge Computing is still new, and established practices have yet to emerge. Even without standards in this area, many companies are turning to Kubernetes to meet these requirements. According to the latest survey by the Cloud Native Computing Foundation (CNCF), 86 percent of companies already use the open-source system to manage container applications. While Kubernetes was born in the cloud, its benefits extend to the rapidly emerging Edge Computing market. Given that hardware and software are distributed across hundreds or thousands of locations, the only practical way to manage these systems is through standardization and automation using cloud-native technologies. However, companies must consider the specific challenges that the Edge presents if they want to use Kubernetes to manage their Edge Computing deployments.
Why is Edge Computing a challenge?
Managing applications across multiple edge locations demands security, centralized resource management, and automated operations for resilience. Edge systems must swiftly deploy workloads, offer stability, and support workload portability. With containers, notably Kubernetes, gaining traction, managing deployments across growing clusters poses a challenge.
Companies encounter challenges when integrating Edge Computing into their existing infrastructure, as it requires efficient management of diverse infrastructures across different locations. This shift from traditional data centers and cloud computing also involves deploying containers and virtual machines at the edge.
Common Challenges in Edge Computing
Companies face three major challenges:
- Building a consistent infrastructure to reduce snowflake-servers
- Overcoming the issues of unstable products
- Recruiting skilled personnel to build and maintain Edge Computing architectures.
IT executives know that automation in deploying and managing applications significantly improves stability and innovation rates while reducing costs. They have recognized that the path to achieving their Edge Computing goals lies in the cloud, but most are stuck in the proof-of-concept phase or have only implemented a handful of applications. These companies have not yet overcome the hurdle of IT automation in the Edge area. This is often because they underestimate the complexity of Edge Computing or fail to implement the necessary new operational models. Some have also failed to build expertise in cloud-native automation.
Resource constraints are often the biggest concern. Kubernetes was developed in the cloud with nearly unlimited scaling capabilities. In contrast, Edge Computing typically has a very limited number of resources. The constraints can vary significantly, from a few servers to a few hundred MB of memory when moving from regional Edge to Device Edge. However, they all share the restriction that any additional overhead impairs the execution of the actual applications. Against this backdrop, the question now arises: How can the footprint of Kubernetes be reduced to make more room for business applications?
Operationalizing Edge Computing
Standardizing and automating cloud-native technologies like Kubernetes could be crucial for Edge Computing success. However, managing containers and virtual machines in a unified stack remains challenging. Solutions like KubeVirt enable seamless integration of virtual machines into Kubernetes, offering various benefits for developers and operators.
Multi-cluster management is vital for Edge Computing, requiring solutions like Kubermatic to automate management across diverse infrastructures. This approach streamlines the deployment, control, and operation of Edge Computing environments, making them more efficient and productive.
Edge Computing spans various locations, each with different device and bandwidth constraints levels. Each level of Edge Computing connects to and is influenced by higher-level functions, with the Edge area serving as the workload’s execution space.
Kubernetes can be viewed as two parts: a central control component and a distributed processing area. Worker Nodes execute applications, while the control plane coordinates workload installation, application lifecycle, and config management. Workloads can function without the control plane, only requiring it for updates and restarts of the worker node. This paradigm shift allows for rethinking Kubernetes cluster construction for resource-constrained environments, with Worker Nodes operating on limited resources and the Control Plane centrally located with additional resources.
Edge Computing Requires a Cloud-Native Mindset Today
You may ask: “Why Opt for Cloud-Native Solutions for the Edge?” The rationale behind adopting a cloud-native framework for edge computing primarily revolves around the need to manage expansive edge environments akin to cloud workloads effectively. By leveraging a scalable and standardized API for infrastructure management, organizations can seamlessly extend their operational capabilities to the edge, ensuring consistency, scalability, and efficiency across diverse deployment scenarios.
Managing distributed computer systems isn’t new to IT; it predates the internet and is fundamental to its development. However, Edge Computing introduces new challenges in scope and complexity. Beyond the multitude of locations, Edge Computing must navigate rugged environments, remote or inaccessible areas, sporadic connectivity, dynamic deployment, global data access, and security risks. These technical challenges are compounded by business considerations. Viewing the edge environment as a business entity reveals the necessity for near-zero-touch operations.
Cloud-native technologies, born in the cloud, hold the key to realizing Edge Computing’s potential. Cloud-native principles, such as standardized infrastructure and automated processes, minimize operational effort, making Edge Computing operationally and financially feasible.
Conclusion
Edge Computing presents a paradigm shift in internet architecture, addressing the demand for rapid data processing and delivery. By leveraging Kubernetes, companies are exploring new operational models to tap into the potential of Edge Computing. Despite challenges like infrastructure standardization and resource constraints, embracing cloud-native principles can streamline deployments, making them operationally and financially viable. With a cloud-native mindset, organizations can overcome hurdles and unlock opportunities in Edge Computing, transforming internet infrastructure.
