Distributed computing trends
Marginal computing is creating a new internet network. In an age where consumers and businesses require the smallest possible delay between asking questions and getting answers, edge computing is the only way to reduce insights time. Edge computing closes this gap by reducing latency, processing data even without enough bandwidth, reducing costs, and processing sovereignty and data compliance. While centralized cloud computing will still exist, a completely different way we can create and act on data at the edge of the network will and is creating new markets and unlocking new value. By 2024, the marginal computing market is expected to be worth over $9.0 billion with a compound annual growth rate of 30%.
However, to make these markets viable and reach their full potential, it is necessary to take into account the operational and business models they require. While cloud computing can rely on focus and economies of scale to build a business model, edge computing needs a new model. With hardware and software spread across hundreds or thousands of locations, the only viable way to manage these distributed systems is through standardization and automation.
Distributed computer system management is not new to IT, its predecessor and some argue that bringing the Internet, but the scale and complexity of edge computing is novel. In addition to the sheer number of locations, marginal computing must take into account harsh environments outside traditional sterilization data centers, remote or in-access locations, unstable connections, dynamic provisioning, global data experiences, and security risks. In addition to these technical challenges there are also business challenges. When considering the advantages of a business, it quickly becomes clear that they need to be as close to a contactless environment as possible because each truck can create a large dent on the sidelines.
What is Cloud Native Edge computing?
What is cloud-native edge computing? This is a purpose-built computer environment that allows machines to operate in real time not by humans, who can withstand one or two-second latency, but by machines, with a time of i.e. nano seconds or less. It’s a coordinated environment from the cloud, but it doesn’t depend on it, and doesn’t need remote IT professionals delivered globally and is available 24/7 to maintain.
For example, a self-driving car can only avoid a person’s road section if it has a marginal calculation without lag. It can quickly learn and update its “drivers” instantly with the latest data from all other cars in a cloud platform. There is no time to think when there is an emergency and if there is a dependence on the cloud, the device can not ‘think’ fast enough because of the latency.
How is Cloud-native Edge Computing different?
Cloud-native edge computing is a different design than just using old embedded algorithms and adding 4G or 5G connections.
Cloud-native applications follow a framework designed to maximize resilience through predictable behaviors.
The cloud-native microservices architecture enables automatic scalability that eliminates down time due to errors and recovers quickly in the event of a faulty application or infrastructure.
Automatic updates regularly provide a clean, new software image every day, an image that will clean up anything that happened before security countermeasures.
Cloud-native edge provides a rich understanding of operations through environmental remote measurement data that can provide insight into current operations as well as the ability to improve over time with automation to extend edge operations to millions of end points.
On the other hand, traditional applications cannot realize all the benefits of running on the cloud due to the unique, highly integrated architecture of each device.
Traditional embedded applications depend on the operating system, which makes moving and expanding applications across new infrastructures complex and risky.
Thromboethmic architectures cause unnecessary dependence between services, resulting in a loss of flexibility in development and deployment.
Over time, traditional embedded computer systems become full of vulnerabilities, making them very vulnerable to hacking.
Cloud Native, Kubernetes and Edge Computing
Although cloud-native technologies are born from the cloud, the operational and business models they allow will make edge computing feasible. Looking at the original definition of cloud, we found that standardization, like in variable infrastructure and declaration APIs, combined with robust automation creates manageable systems that require minimal work. This standardization and automation is key to making margin calculations feasible both operationally and financially.
At the core of the cloud-native ecosystem is Kubernetes. It was originally designed as a loosely combined system with a declaration API and built-in tuning rings. These two features make Kubernetes perfectly suited for marginal computing.
First, it provides a standardized API to perform life cycle management of hardware and software across different infrastructures and locations. Instead of having to redesign computers and apps for each use case or location, they can be designed once and deployed multiple times. This will allow businesses to easily scale around the world to meet customers on their doorstep.
Second, the adjustment loops autom out manually tasks to build a touchless environment with self-recovery infrastructure and applications. Leveraging Kubernetes to provide high-level standardization and automation of infrastructure and applications will allow companies to scale through software rather than people. This opens up new business models that were previously too expensive to be feasible.
5G and Edge Computing
One of the most hyped uses for running modern software is the 5G. 5G network that promises faster speeds (both download and upload), reduced latency, higher device capacity, and efficient network cutting. All of these innovations have tremendous potential to impact multiple business verticals and create entirely new innovations with IoT, AR/VR, self-driving cars, smart cities, and Industry 4.0 being one of the most commonly cited use cases.
However, since 5G will use a higher frequency than previous generation networks, the connection range will be much shorter. Therefore, 5G requires more than 5-10 times the base station. Using traditional management models and transferring this increase in costs directly to end users or businesses is not feasible. To make 5G and the business models it creates possible will require new standardization and automation.
Key trends of Cloud Native and Edge Computing after 2020
IDC predicts that, by 2020, more than 50 billion terminals and devices will be connected to the Internet and more than 40% of data will be analyzed, processed and stored at the edge of the network. This means that edge computing will face many new scenarios and evolve in infinite ways.
As we enter the new decade, we anticipate that the marginal computing sector will undergo the following changes by 2020:
With the rapid development of Kubernetes-based cloud-native technology in recent years, great strides have been made in the scope of application, application scenarios and technical maturity. One of its core advantages is to provide consistent functions and experiences in the cloud and any other infrastructure through unified standards. By combining cloud-native technology with edge computing, you can quickly deliver applications that achieve cloud terminal integration. This provides a solution for consistent large-scale application distribution, maintenance and control across a large number of terminals and edge devices.
In terms of security, cloud-native technology can provide a more secure workload operating environment, such as containers, as well as network and adjustment policies, which can effectively improve the security of edge and edge data services.
In marginal network environments, edge containers capabilities based on cloud-native technology can ensure the autonomy of weak and disconnected networks and provide effective self-resilience. This solution is also compatible with complex network access environments.
With the support of powerful communities and providerss in the cloud-native sector, cloud-native technology is becoming more applicable to heerthed resources. In the IoT field, cloud-native technology has supported a variety of CPU architectures and communication protocols, with low resource consumption.