A new infrastructure model for service providers
As the demands on your networks grow, simplicity, scale, and agility are essential. However, it’s the end user experiences with services, content, and applications that drive the perceived value of your services.
This paper examines the aspects of building a new services infrastructure, which uses computing resources closer to human or machine subscribers. It provides a viewpoint from the network and describes some use cases and considerations towards meeting the latency requirements required for a new services infrastructure.
Today, more applications are moving to the cloud, and multiple clouds are being deployed. With the explosion of end-points, mobility, and nomadic computing, the volumes of data used for analytics, machine learning and automation can result in high costs to transport to central locations for processing. Traditional service provider architectures can no longer meet modern needs.
At the same time, connecting the network has become critical in delivering high-quality experiences, application performance, and security across data, services, and applications. To solve these issues, a new services edge architecture is emerging that is based on distributing computing capacity to the edge of the network. This architecture results in lower latency with respect to subscribers. Throughout this paper, we use the term edge computing to refer to the general architectural shift, which most standards developing organizations (SDOs) such as ETSI refer to as multi-access edge computing or MEC.
Edge computing is the architectural principle of moving services to locations where they can:
For example, lower latency can improve the QoE of certain consumer applications. These applications range from the delivery of HTTP(S) content and video to augmented reality and virtual reality. Offloading may reduce the cost of networking and improve QoE as metro area peering points to the Internet are becoming more ubiquitous. The benefits in terms of customer retention and churn reduction are clear, but there is also an aspect of fundamental service enablement. In addition, some use cases associated with security and the Internet of Things (IoT) may see benefits associated with edge processing and edge analytics. These benefits are apparent when considered against the alternative of transporting vast amounts of data to a centralized data center.
Two types of workloads are being deployed at the edge. Service product workloads are directly associated with service products that generate service revenue. The second type of workload is associated with infrastructure and are workloads that directly enable a better network. Two examples of infrastructure workloads are cloud radio access network (RAN) and user plane offload using a decomposed mobile core, such as in the 5GC or in the LTE CUPS architecture. Other examples of edge infrastructure workloads exist in cable broadband and Gigabit-capable Passive Optical Networks (GPON) access.
The edge computing services architecture is intimately associated with an ecosystem and can’t exist without a value chain. Ensuring greater openness allows a new ecosystem to emerge and more efficient business models to develop. The benefits span the entire ecosystem of applications and service providers, network operators, enterprises, and consumer customers. In the ecosystem, you might have a business-to-business (B2B) model where the operator develops service products that are consumable by other businesses. For example, the operator could develop a service product for public cloud providers based on a low latency edge tenancy supported within the operator network. The public cloud provider, which has an established channel into the enterprise, could offer its own X-as-a-service (XaaS) capabilities to those enterprises.
A similar business model can be established with content delivery network (CDN) providers. In this case, as an alternative to the points of delivery (PODs) supported by CDNs, the operator could offer a tenancy within their network so the CDN provider could build their POD at a low-latency location. This approach would offer significant value to the CDN provider and result in a revenue opportunity for the operator. A final example is the connected vehicle business. Automobile vendors seek to establish more value with connectivity to the vehicles they sell. To establish this connectivity, the vendors could establish a branded presence by consuming a tenancy in a mobile edge cloud.
B2B opportunities don’t exclude the possibility of operators supporting business-to-consumer (B2C) services. In this case, the operator becomes the branded delivery mechanism of consumer services. Consumer video is the most salient example of this type of product opportunity since it’s well known that video streaming benefits from low latency to the subscriber.
These examples show the advantages of an open edge computing model:
In a B2B model, consumers of network operator services can include the following:
|Content publishers||Connected car vendors||Industrial and automation|
|Public cloud providers||Gaming, Augmented and virtual reality vendors||Utilities|
|Mobile virtual network operators (MVNOs) and MVNO enablement platforms||Municipalities||CDN providers|
|Government organizations including public safety||IoT-connected device platforms||Enterprises|
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As a service provider, supporting the value chain and your selected partners requires developing an operational model that makes it easy for partners to develop and deploy their own solutions. Such an operational model also needs to support partners’ access to data and analytics that are appropriate for ensuring the health, QoE, financial reconciliation, and trust of the services.
To achieve the scale required and manage the growing complexity, the operational model needs to include automation across the ecosystem and be able to meet the end customers’ changing needs. Open application programming interfaces (APIs) must be a necessary part of the overall operational model. Standardizing an operational environment can help you achieve your objectives. An entire edge computing platform can be designed based on a Network-as-a-Service (NaaS) business model. In this model, APIs are essential along with the necessary software that controls and manages the platform and supports your ecosystem.
Figure 1. Emergence of the infrastructure edge.
Three major architectural shifts underpin the emergence of the edge computing network infrastructure:
The 5G system promotes the emergence of an edge infrastructure that combines decomposed subscriber management from a converged core with the data plane of a wireline access node, for example: DSLAM/OLT, as well as upper layers of the 3GPP radio stack.
Edge computing use cases are driven by the need to optimize infrastructure through offloading, better radio, and more bandwidth to fixed and mobile subscribers.
Figure 2. The edge.
Some organizations are testing edge computing at the cell-site itself. At first glance, this approach might appear reasonable because it puts the computing as close as possible to the mobile subscribers. However, several issues result:
Instead of focusing on proximity, instead you can focus on addressing latency requirements. A good IP design can cure latency issues from a centralized metro location to the cell site. The economics are more important for the location of the edge in edge computing. You need to consider capital expenditures (CapEx) and operating expenses (OpEx) to ensure a good IP network design.
Figure 3. Edge closer to cell-site
An edge server closer to the cell site means less IP network growth but more cost (OpEx and CapEx) for the edge servers because of the larger number of locations that need to be supported.
Figure 4. Edge server further from the cell site
An edge server located farther from the cell site means that the operator must deploy more IP network capacity. But it can lower edge server costs due to the economies of scale of the centralized metro location. The higher network capacity is easily manageable through priority queues on latency-sensitive traffic.
To determine the location of the edge computing node, consider these questions:
The use cases for edge computing fall into one of four categories:
To determine the uses cases to support, consider these questions:
At this point in the emergence of edge computing, wireline, and wireless infrastructure use cases dominate, and the challenges are mostly in the operator monetization model for service workloads. These challenges will be overcome in time as more robust ecosystems form, more use cases are discovered (or discarded) through lessons learned, and the economics are better understood.
The current way networks are built is no longer sustainable. A new approach is necessary, one that's more open and easily places the computing capacity needed for a set of services, to where best located. The end user experience drives the perceived value of your services and is directly related to how the network performs and how the required latency is achieved. Building the foundation for edge computing requires you to consider many factors which influence your services architecture.
Low latency benefits many types of services towards a good user experience. Low latency isn’t equivalent to close proximity. A properly designed IP network supports low latency while supporting optimal economics. Interesting locations for edge computing include metro data centers and repurposed central offices or public exchanges, not cell sites. In the enterprise environment, an enterprise’s locations might be the optimal locations.
At this stage in the evolution of edge computing, infrastructure use cases dominate the requirements for edge computing nodes and associated activities. Meanwhile, you also need to develop your business model and partner ecosystems. Standardizing on an operational environment will help you achieve your objectives. An entire edge computing platform can be designed based on a NaaS business model in which APIs are essential, plus the necessary software that controls and manages the platform and supports your ecosystem.