Enterprises are interested in the potential of containerized 5G workloads to improve flexibility, scalability, and cost efficiency. 5G workloads, like cloud infrastructure, are designed to be highly reliable and scalable. Therefore, it makes sense to deploy 5G workloads in the cloud and leverage containers to add more flexibility to the equation.
However, deciding which type of cloud to use for 5G functionality, such as a public cloud or a telecom provider’s cloud, can be difficult. Also, containers aren’t always the best fit for his 5G workloads.
What is a 5G workload?
5G workload — sometimes referred to as 5G network function — is an application that helps manage communications on 5G network infrastructure. It performs tasks such as authenticating users and devices connecting to a 5G network and managing the data flowing over the network.
Similar capabilities exist in older generation network infrastructures such as 4G. However, 5G workloads pose special challenges for two main reasons:
new architecture. 5G networks typically rely on a new type of architecture called 3rd Generation Partnership Project architecture. This architecture implements core functions and organizes them in a more complex way than previous communication networks.
Performance requirements. 5G networks must meet significantly higher performance requirements. For example, sending more data at a much lower delay rate.
Telcos, the companies that operate 5G networks, need to find ways to deploy 5G workloads on networks that address both of these challenges.
Comparing Telco Cloud and Public Cloud for 5G Workloads
5G workloads can theoretically run on any infrastructure that can connect to a network infrastructure to help manage it. However, in most cases, it makes sense to deploy workloads in the cloud rather than on-premises, as the cloud offers a better level of scalability and reliability. The cloud also makes it easier to choose a deployment location close to the data sources that 5G capabilities need to manage. This proximity reduces latency.
While it’s clear that most 5G workloads need to reside in the cloud, deciding on which type of cloud to host them isn’t always obvious. Two main options are available: communication cloud and public cloud.
communication cloud
Telecommunications cloud refers to data center infrastructure owned and operated by telecommunications companies. Deployment in a communications cloud typically improves performance because the communications cloud is more tightly integrated with the network infrastructure and data flows that 5G capabilities manage.
Telco clouds give providers more control over 5G workload configurations. For example, providers may choose to host functionality on bare metal servers. This improves performance compared to hosting the functionality on a VM.
public cloud
Public clouds such as AWS, Microsoft Azure, and Google Cloud Platform can host 5G functionality that connects to communications networks. These features are not part of these networks and can lead to decreased performance. However, it is generally faster and easier for a telco to deploy his 5G workloads in the public cloud, since he does not have to acquire, provision, and manage his own infrastructure.
There is no one-size-fits-all answer when it comes to choosing between telecom cloud and public cloud for 5G. Network operators must weigh the pros and cons of each option. In some cases, both cloud infrastructures may be used at the same time, with 5G workloads distributed across the hybrid cloud infrastructure.
No matter what type of cloud infrastructure carriers choose, they will also have to make important choices about how they deploy 5G capabilities.
Advantages and disadvantages of containerized 5G
No matter what type of cloud infrastructure carriers choose, they will also have important choices to make about how they deploy 5G capabilities. For example, you can use containers or run workloads directly on VMs or bare metal servers.
In general, containers have emerged as the most popular option for 5G deployments, as they offer several key benefits:
Portability. Containerized 5G workloads can be more easily moved between environments because containers abstract applications from the hosting environment.
Resource usage is optimized. Containers do not require a hypervisor or guest OS, so they have less resource overhead compared to VMs.
Cost efficiency. Containers have lower resource overhead and cost savings because carriers pay for less infrastructure.
Sophisticated orchestration. Telcos can use technologies like Kubernetes to orchestrate containers. This orchestration makes it easy to manage many 5G feature instances at once. Orchestration technologies for VMs and bare metal apps also exist, but most of them are proprietary and less familiar to engineers than Kubernetes.
However, containers pose some additional challenges for 5G workloads. The biggest one is the added complexity because it introduces tools and layers that aren’t present in other deployment models, such as container runtimes and orchestration platforms. Additionally, containers can complicate security because there are more moving parts in a containerized environment. This creates more potential attack vectors and increases the risk of configuration oversights that could expose your resources to attack.
Essentials of containerized 5G cloud workloads
A few things are clear about 5G workloads. The first is that most workloads benefit from running in some type of cloud. However, whether a telco cloud is better than a public cloud depends on factors such as how much latency your workloads can tolerate and how much infrastructure capacity your telco cloud has. .
Another consideration is that containers generally increase the flexibility and efficiency of 5G workloads. However, these benefits also come at the potential cost of greater complexity and security risks.
Most 5G workloads run in some type of cloud, and most likely containerized. However, exceptions occur, as not all 5G features are ideal candidates for containerized, cloud-based deployments.
Chris Tozzi, Senior Content Editor and DevOps Analyst at Fixate IO, has worked as a journalist and Linux systems administrator, with a special interest in open source agile infrastructure and networking.
